Question: The impact of fearful and angry facial expressions on actions related to moving closer or further away.

The Impact of Regulatory Focus on Approach and Avoidance Behaviors in Goal Pursuit

Across three studies, the authors explored the hypothesis that regulatory focus, both as a stable individual trait and as a situational factor, has distinct effects on the intensity of approach and avoidance strategies as individuals near their goal. In two studies, the researchers employed flexion and extension arm pressure as real-time indicators of approach and avoidance strength, respectively. Consistent with predictions, individuals with a promotion focus, characterized by aspirations and gains, exhibited a steeper increase in approach intensity as they approached their goal, compared to those with a prevention focus, oriented towards responsibilities and avoiding losses. Conversely, the avoidance intensity pattern was reversed, with prevention-focused individuals showing a more pronounced increase. In a third study, this differential pattern was replicated using a persistence measure of motivational strength. Specifically, promotion-focused individuals demonstrated longer persistence on tasks when they served as approach means for goal attainment, as opposed to avoidance means, while the opposite effect was observed in prevention-focused individuals.

This research underscores the nuanced role of regulatory focus in modulating approach and avoidance behaviors during goal pursuit, suggesting that the motivational dynamics underlying goal attainment are complex and context-dependent.

Threat-related stimuli are strong competitors for attention, particularly in anxious individuals. We used functional magnetic resonance imaging (fMRI) with healthy human volunteers to study how the processing of threat-related distractors is controlled and whether this alters as anxiety levels increase. Our work builds upon prior analyses of the cognitive control functions of lateral prefrontal cortex (lateral PFC) and anterior cingulate cortex (ACC). We found that rostral ACC was strongly activated by infrequent threat-related distractors, consistent with a role for this area in responding to unexpected processing conflict caused by salient emotional stimuli. Participants with higher anxiety levels showed both less rostral ACC activity overall and reduced recruitment of lateral PFC as expectancy of threat-related distractors was established. This supports the proposal that anxiety is associated with reduced top-down control over threat-related distractors. Our results suggest distinct roles for rostral ACC and lateral PFC in governing the processing of task-irrelevant, threat-related stimuli, and indicate reduced recruitment of this circuitry in anxiety.

Metaphors linking spatial location and affect (e.g., feeling up or down) may have subtle, but pervasive, effects on evaluation. In three studies, participants evaluated words presented on a computer. In Study 1, evaluations of positive words were faster when words were in the up rather than the down position, whereas evaluations of negative words were faster when words were in the down rather than the up position. In Study 2, positive evaluations activated higher areas of visual space, whereas negative evaluations activated lower areas of visual space. Study 3 revealed that, although evaluations activate areas of visual space, spatial positions do not activate evaluations. The studies suggest that affect has a surprisingly physical basis.

Multimodal recognition of affective states is a difficult problem, unless the recording conditions are carefully controlled. For recognition “in the wild”, large variances in face pose and illumination, cluttered backgrounds, occlusions, audio and video noise, as well as issues with subtle cues of expression are some of the issues to target. In this paper, we describe a multimodal approach for video-based emotion recognition in the wild. We propose using summarizing functionals of complementary visual descriptors for video modeling. These features include deep convolutional neural network (CNN) based features obtained via transfer learning, for which we illustrate the importance of flexible registration and fine-tuning. Our approach combines audio and visual features with least squares regression based classifiers and weighted score level fusion. We report state-of-the-art results on the EmotiW Challenge for “in the wild” facial expression recognition. Our approach scales to other problems, and ranked top in the ChaLearn-LAP First Impressions Challenge 2016 from video clips collected in the wild.

It has been argued that the communication of emotions is more difficult in computer-mediated communication (CMC) than in face-to-face (F2F) communication. The aim of this paper is to review the empirical evidence in order to gain insight in whether emotions are communicated differently in these different modes of communication. We review two types of studies: (1) studies that explicitly examine discrete emotions and emotion expressions, and (2) studies that examine emotions more implicitly, namely as self-disclosure or emotional styles. Our conclusion is that there is no indication that CMC is a less emotional or less personally involving medium than F2F. On the contrary, emotional communication online and offline is surprisingly similar, and if differences are found they show more frequent and explicit emotion communication in CMC than in F2F. 2007 Elsevier Ltd. All rights reserved.

Personality is strongly influenced by motivation systems that organise responses to rewards and punishments and that drive approach and avoidance behavior. Neuropsychological research has identified: (a) two avoidance systems, one related to pure active avoidance and escape, and one to passive avoidance and behavioral inhibition produced by goal-conflict; and (b) two approach systems, one related to the actions of reward seeking and one to experience and behavior related to pleasure on receiving reward. These systems mediate fluid moment-by-moment reactions to changing stimuli, with relatively stable person-specific sensitivities to these stimuli manifested in personality traits. We review what is known about these motivational traits, integrating the theory-driven approach based on animal learning paradigms with the empirical tradition of the Big Five personality model. People differ from one another, and this fact is obvious to everyone. It is common to talk about people’s personalities using lexical terms to describe their characteristic ways of thinking, feeling and behaving (e.g., ‘bold’, ‘lazy’, ‘intelligent’), and we use these descriptors to infer people’s intentions and likely future behavior. Personality psychologists have long analyzed the ratings of large numbers of trait descriptive adjectives to produce the most widely used taxonomy of personality: the Big Five, which includes the dimensions of Extraversion, Neuroticism, Conscientiousness, Agreeableness, and Openness to Experience ⁄ Intellect (John, Naumann, & Soto, 2008). These Big Five traits also emerge from existing personality questionnaires that were not designed specifically to measure them (e.g., Markon, Krueger, & Watson, 2005), suggesting it is a good candidate for a consensual model in personality psychology. What the Big Five model does not immediately offer, however, is an explanation for the causal sources of personality traits. Why do people think, feel, and act in the ways that they do? People react to situations, of course; but different people react differently to the same situation, suggesting that they have different behavioral propensities. In order to answer this why question, we must discover what drives people’s actions and reactions. Inferring motivation from observed personality has been something of a dark art in psychology. However, one promising approach to this question is based on the biology of motivational control systems, studied by psychologists for over a century in non-human animals, and for somewhat less time in humans. This approach operates on the premise that stable individual differences in behavior (personality traits) must be due to relatively stable individual differences in the operation of brain systems that produce (state) behavior from moment-tomoment. From this perspective, each of our many traits reflects the operations of a set of brain systems that has evolved to respond to a different class of functional requirements (Denissen & Penke, 2008; McNaughton, 1989; Nettle, 2006; Pickering & Gray, 1999). Social and Personality Psychology Compass 7/3 (2013): 158–175, 10.1111/spc3.12016 a 2013 Blackwell Publishing Ltd In what follows, we focus on those motivational processes and personality traits most closely aligned with biological research on reactions to reward and punishment and associated approach and avoidance behavior. This focus is warranted both by the importance of these phenomena for motivation and by the existence of extensive research on them. Our aim is to offer an introduction for researchers wishing to explore the role of motivation in personality from the perspective of these underlying psychobiological systems. Only after a description of what is known about the operation of these systems do we branch out to consider the personality traits associated with them. Our major assumption is that most fundamental personality traits have a motivational core; and we aim to show that the descriptive personality research tradition, which produced the Big Five, can be integrated with the experimental research tradition that has focused on the sensitivities of basic motivation systems. In this review, we focus on systems related to approach and avoidance primarily at the level of explanation that Gray (1975) labeled ‘the conceptual nervous system’, which is based on analysis of behavior as well as neurobiology and attempts to describe important psychological processes without specifying their exact instantiation in the nervous system – this approach has afforded a detailed analysis of reactions to classes of motivationally significant stimuli and can be used to derive predictions concerning the functions of the real nervous system (e.g., in fMRI studies). Rather than going into extensive detail regarding the biological basis of the systems, we focus primarily on their functions, discussing biological evidence only when it is necessary for addressing some functional question. Approach-Avoidance Theories of Motivation and Their Relation to Personality The most important classes of motivational stimuli can be grouped into ‘rewards’ and ‘punishments’. Animals can be seen as cybernetic systems with attractors and repulsors (positive and negative goals) that have evolved to promote survival and reproduction (Carver & Scheier, 1998; DeYoung, 2010d). Without a tendency to approach beneficial stimuli (e.g., food, drink, and sexual mates) and to avoid aversive stimuli (e.g., predators and poisons) a species would not survive. ‘Reward’ and ‘punishment’ may seem straightforward concepts, but they hide some nonobvious complexities. For the classical behaviorist, rewards increase the frequency of the behavior leading to them, whereas punishments decrease the frequency of behavior leading to them. That is, a ‘reward’ is something a person will work to obtain; and a ‘punishment’ is something a person will work to avoid. But the behaviorist definition of ‘reward’ also includes a different class of stimuli, namely the termination or omission of expected punishment. The effect on behavior and emotion of the ‘hope’ of achieving a reward is similar to that of anticipated ‘relief’ through avoiding a punishment. Similarly, although a ‘punishment’ can be described as something people will work to avoid or escape from (or which they will attack defensively), the omission of an expected reward is experienced as punishing; an effect known as frustrative nonreward. Thus, ‘fear’ has important similarities with ‘frustration’. (For further discussion of this literature, see Corr & McNaughton, 2012.) These complexities can be understood straightforwardly from the cybernetic perspective, in which rewards are any stimuli that indicate progress toward or attainment of a goal, whereas punishments are any stimuli that disrupt progress toward a goal. However, in any experimental situation, it is necessary to confirm that the subject perceives stimuli as actually rewarding and punishing, as there are likely to be significant individual differences in how people react to the same stimuli (for further discussion of this point, see Corr, forthcoming). Motivation and Personality 159 a 2013 Blackwell Publishing Ltd Social and Personality Psychology Compass 7/3 (2013): 158–175, 10.1111/spc3.12016 Current approach-avoidance theories trace their origins to early researchers who posited that two motivation ⁄emotion processes underlie behavior (e.g., Konorski, 1967; Mowrer, 1960; Schneirla, 1959), one related to reward (approach behavior and positive emotions), and the other to punishment (avoidance behavior and negative emotions). Neuroscience measures, including pharmacological manipulation, assessment of neural activity, and neuroanatomical studies, have been used to investigate the neuropsychological systems that underlie reactions to these classes of stimuli, providing confirmation of the hypothesis that distinct systems underlie rewardand punishment-related motivation (Gray & McNaughton, 2000). This animal-based work migrated into personality psychology in the 1970s via Jeffrey A. Gray (e.g., 1970, 1972a,b, 1975, 1977), whose Reinforcement Sensitivity theory (RST) argued that the major traits of personality reflect long-term stabilities in systems that mediate reactions to different classes of reinforcing stimuli, generating emotion and shaping (‘motivating’) approach and avoidance behavior. The leap from understanding motivational systems to understanding personality traits requires the postulate that relatively stable individual differences exist in the operations of these brain-behavioral systems. A personality trait can be defined as a probabilistic constant in equations that predict the frequency and intensity with which individuals exhibit various motivational states, as well as the behavioral, emotional, and cognitive states that accompany these motivational states (DeYoung, 2010c; Fleeson, 2001; Fleeson & Gallagher, 2009). Note that this assumes exposure of the population to a normal range of situations. If situations are limited to prevent exposure to some trait-relevant class of situations, then individual differences in that trait may not be manifest. A neuropsychological approach to personality aims to understand both the biological systems that are responsible for the states associated with any given trait and the parameters of those systems that cause them to differ across individuals. The systems themselves will be present in every intact human brain, but the values of their parameters will vary from person to person. Thus, for example, all people have brain systems that respond to punishing stimuli, but in different individuals these systems respond differently to a given stimulus. It is the typical level of response of such a system in any given individual, averaged across different situations, that is associated with that individual’s score on the personality trait in question. This is not to imply that an individual wil

Altered visual exploration of faces likely contributes to social cognition deficits seen in autism. To investigate the relationship between face gaze and social cognition in autism, we measured both face gaze and how facial regions were actually used during emotion judgments from faces. Compared to IQ-matched healthy controls, nine high-functioning adults with autism failed to make use of information from the eye region of faces, instead relying primarily on information from the mouth. Face gaze accounted for the increased reliance on the mouth, and partially accounted for the deficit in using information from the eyes. These findings provide a novel quantitative assessment of how people with autism utilize information in faces when making social judgments.

Visually guided eating, biting and kissing, and avoiding objects moving toward the face and toward which the face moves require prompt, coordinated processing of spatial visual and somatosensory information in order to protect the face and the brain. Single-cell recordings in parietal cortex have identified multisensory neurons with spatially restricted, aligned visual and somatosensory receptive fields, but so far, there has been no evidence for a topographic map in this area. Here we mapped the organization of a multisensory parietal face area in humans by acquiring functional magnetic resonance images while varying the polar angle of facial air puffs and close-up visual stimuli. We found aligned maps of tactile and near-face visual stimuli at the highest level of human association cortex—namely, in the superior part of the postcentral sulcus. We show that this area may code the location of visual stimuli with respect to the face, not with respect to the retina.*NOTE: In the version of this article initially published online, there was an error in the affiliation in the html version. The first affiliation should read Department of Cognitive Science, University of California San Diego, La Jolla, California 92093, USA. The error has been corrected online.

Some writers assume--and others deny--that all human beings distinguish emotions from nonemotions and divide the emotions into happiness, anger, fear, and so on. A review of ethnographic and cross-cultural studies on (a) emotion lexicons, (b) the emotions inferred from facial expressions, and (c) dimensions implicit in comparative judgments of emotions indicated both similarities and differences in how the emotions are categorized in different languages and cultures. Five hypotheses are reviewed: (a) Basic categories of emotion are pancultural, subordinate categories culture specific; (b) emotional focal points are pancultural, boundaries culture specific; (c) emotion categories evolved from a single primitive category of physiological arousal; (d) most emotion categories are culture specific but can be defined by pancultural semantic primitives; and (e) an emotion category is a script with both culture-specific and pancultural components.

In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison.

Emotion Detection is one of the most emerging issues in human computer interaction. A sufficient amount of work has been done by researchers to detect emotions from facial and audio information whereas recognizing emotions from textual data is still a fresh and hot research area. This paper presented a knowledge based survey on emotion detection based on textual data and the methods used for this purpose. At the next step paper also proposed a new architecture for recognizing emotions from text document. Proposed architecture is composed of two main parts, emotion ontology and emotion detector algorithm. Proposed emotion detector system takes a text document and the emotion ontology as inputs and produces one of the six emotion classes (i.e. love, joy, anger, sadness, fear and surprise) as the output.


Question: The impact of fearful and angry facial expressions on actions related to approaching and avoiding.

This study investigates the emotional processing in psychopaths by examining their startle reflex modulation in response to affective stimuli. Fifty-four incarcerated sexual offenders were categorized into three groups—nonpsychopathic, psychopathic, and mixed—based on their file and interview data. The experiment involved measuring startle-elicited blinks while subjects were exposed to slides with varying emotional content. The results paralleled previous findings with normal college students, demonstrating a significant linear relationship between slide valence and startle magnitude in nonpsychopaths and mixed subjects. Specifically, startle responses were largest during unpleasant slides and smallest during pleasant ones. However, this effect was notably absent in the psychopathic group. The observed group differences in startle modulation were associated with the affective features of psychopathy, rather than antisocial behavior alone. Interestingly, psychopathy did not influence autonomic or self-report responses to the slides. These findings suggest that psychopaths exhibit a distinct abnormality in the processing of emotional stimuli, which is evident independently of their affective report. This highlights a potential neurological basis for the emotional insensitivity characteristic of psychopathy.

Recent advances in computer animation and robotics have lead to greater and greater realism of human appearance to be obtained both on screen and in physical devices. A particular issue that has arisen in this pursuit is whether increases in realism necessarily lead to increases in acceptance. The concept of the uncanny valley suggests that high, though not perfect, levels of realism will result in poor acceptance. We review this concept and its psychological basis.

The deployment of human gaze has been almost exclusively studied independent of any specific ongoing task and limited to two-dimensional picture viewing. This contrasts with its use in everyday life, which mostly consists of purposeful tasks where gaze is crucially involved. To better understand deployment of gaze under such circumstances, we devised a series of experiments, in which subjects navigated along a walkway in a virtual environment and executed combinations of approach and avoidance tasks. The position of the body and the gaze were monitored during the execution of the task combinations and dependence of gaze on the ongoing tasks as well as the visual features of the scene was analyzed. Gaze distributions were compared to a random gaze allocation strategy as well as a specific "saliency model." Gaze distributions showed high similarity across subjects. Moreover, the precise fixation locations on the objects depended on the ongoing task to the point that the specific tasks could be predicted from the subject's fixation data. By contrast, gaze allocation according to a random or a saliency model did not predict the executed fixations or the observed dependence of fixation locations on the specific task.

Citizens are frequently misinformed about political issues and candidates but the circumstances under which inaccurate beliefs emerge are not fully understood. This experimental study demonstrates that the independent experience of two emotions, anger and anxiety, in part determines whether citizens consider misinformation in a partisan or open-minded fashion. Anger encourages partisan, motivated evaluation of uncorrected misinformation that results in beliefs consistent with the supported political party, while anxiety at times promotes initial beliefs based less on partisanship and more on the information environment. However, exposure to corrections improves belief accuracy, regardless of emotion or partisanship. The results indicate that the unique experience of anger and anxiety can affect the accuracy of political beliefs by strengthening or attenuating the influence of partisanship.

The interaction between human beings and computers will be more natural if computers are able to perceive and respond to human non-verbal communication such as emotions. Although several approaches have been proposed to recognize human emotions based on facial expressions or speech, relatively limited work has been done to fuse these two, and other, modalities to improve the accuracy and robustness of the emotion recognition system. This paper analyzes the strengths and the limitations of systems based only on facial expressions or acoustic information. It also discusses two approaches used to fuse these two modalities: decision level and feature level integration. Using a database recorded from an actress, four emotions were classified: sadness, anger, happiness, and neutral state. By the use of markers on her face, detailed facial motions were captured with motion capture, in conjunction with simultaneous speech recordings. The results reveal that the system based on facial expression gave better performance than the system based on just acoustic information for the emotions considered. Results also show the complementarily of the two modalities and that when these two modalities are fused, the performance and the robustness of the emotion recognition system improve measurably.

The present study examines the relationship between questionnaire measures of social phobia and gelotophobia. A sample of 211 Colombian adults filled in Spanish versions of the Social Anxiety and Distress scale (SAD; Watson & Friend, 1969), the Fear of Negative Evaluation scale (FNE; Watson & Friend, 1969) and the GELOPH<15> (Ruch & Proyer, 2008). Results confirmed that both Social Anxiety and Distress and Fear of Negative Evaluation scale overlapped with the fear of being laughed at without being identical with it. The SAD and FNE correlated highly with the GELOPH<15> but not all high scorers in these scales expressed a fear of being laughed at. Furthermore, an item factor analysis yielded three factors that were mostly loaded by items of the respective scales. This three-factor structure was verified using confirmatory factor analysis. A measure model where one general factor of social anxiety was specified, or another one where two different factors were defined (gelotophobia vs. social anxiety assessed by SAD and FNE) showed a very poor fit to the data. It is concluded that the fear of being laughed cannot fully be accounted for by these measures of social phobia.

Attentional biases for negative stimuli have been observed in school-age and adolescent children of depressed mothers and may reflect a vulnerability to depression. The direction of these biases and whether they can be identified in early childhood remains unclear. The current study examined attentional biases in 5-7-year-old children of depressed and non-depressed mothers. Following a mood induction, children participated in a dot-probe task assessing biases for sad and happy faces. There was a significant interaction of group and sex: daughters of depressed mothers attended selectively to sad faces, while children of controls and sons of depressed mothers did not exhibit biases. No effects were found for happy stimuli. These findings suggest that attentional biases are discernible in early childhood and may be vulnerability markers for depression. The results also raise the possibility that sex differences in cognitive biases are evident before the emergence of sex differences in the prevalence of depression.

Music is an intriguing stimulus widely used in movies to increase the emotional experience. However, no brain imaging study has to date examined this enhancement effect using emotional pictures (the modality mostly used in emotion research) and musical excerpts. Therefore, we designed this functional magnetic resonance imaging study to explore how musical stimuli enhance the feeling of affective pictures. In a classical block design carefully controlling for habituation and order effects, we presented fearful and sad pictures (mostly taken from the IAPS) either alone or combined with congruent emotional musical excerpts (classical pieces). Subjective ratings clearly indicated that the emotional experience was markedly increased in the combined relative to the picture condition. Furthermore, using a second-level analysis and regions of interest approach, we observed a clear functional and structural dissociation between the combined and the picture condition. Besides increased activation in brain areas known to be involved in auditory as well as in neutral and emotional visual-auditory integration processes, the combined condition showed increased activation in many structures known to be involved in emotion processing (including for example amygdala, hippocampus, parahippocampus, insula, striatum, medial ventral frontal cortex, cerebellum, fusiform gyrus). In contrast, the picture condition only showed an activation increase in the cognitive part of the prefrontal cortex, mainly in the right dorsolateral prefrontal cortex. Based on these findings, we suggest that emotional pictures evoke a more cognitive mode of emotion perception, whereas congruent presentations of emotional visual and musical stimuli rather automatically evoke strong emotional feelings and experiences.

Numerous studies have shown that memory is enhanced for emotionally negative and positive information relative to neutral information. We examined whether emotion-induced memory enhancement is influenced by low-level perceptual attributes such as color. Because in everyday life red is often used as a warning signal, whereas green signals security, we hypothesized that red might enhance memory for negative information and green memory for positive information. To capture the signaling function of colors, we measured memory for words standing out from the context by color, and manipulated the color and emotional significance of the outstanding words. Making words outstanding by color strongly enhanced memory, replicating the well-known von Restorff effect. Furthermore, memory for colored words was further increased by emotional significance, replicating the memory-enhancing effect of emotion. Most intriguingly, the effects of emotion on memory additionally depended on color type. Red strongly increased memory for negative words, whereas green strongly increased memory for positive words. These findings provide the first evidence that emotion-induced memory enhancement is influenced by color and demonstrate that different colors can have different functions in human memory.

We present a robust method to map detected facial Action Units (AUs) to six basic emotions. Automatic AU recognition is prone to errors due to illumination, tracking failures and occlusions. Hence, traditional rule based methods to map AUs to emotions are very sensitive to false positives and misses among the AUs. In our method, a set of chosen AUs are mapped to the six basic emotions using a learned statistical relationship and a suitable matching technique. Relationships between the AUs and emotions are captured as template strings comprising the most discriminative AUs for each emotion. The template strings are computed using a concept called discriminative power. The Longest Common Subsequence (LCS) distance, an approach for approximate string matching, is applied to calculate the closeness of a test string of AUs with the template strings, and hence infer the underlying emotions. LCS is found to be efficient in handling practical issues like erroneous AU detection and helps to reduce false predictions. The proposed method is tested with various databases like CK+, ISL, FACS, JAFFE, MindReading and many real-world video frames. We compare our performance with rule based techniques, and show clear improvement on both benchmark databases and real-world datasets.

In literary and drama criticism, emotions, and moral emotions in particular, have been pointed out as one of characterizing features of stories. In this paper, we propose to model story characters as value-based emotional agents, who appraise their own and others’ actions based on their desires and values, and feel the appropriate moral emotions in response to narrative situations that challenge their goals and values. In order to validate the appropriateness of the agent model for narrative characters, we ran an experiment with human participants aimed at comparing their expectations about characters’ emotions with the predictions of the value-based model of emotional agent. The results of the experiment show that the participants’ expectations meet the predictions of the model. 1998 ACM Subject Classification I.2.11 Intelligent Agents, I.2.0 Cognitive Simulation


Question: IntelliArm: An exoskeletal device for the assessment and therapy of individuals with neurological deficits.

IntelliArm: A Neural-Controlled Exoskeleton for Rehabilitation of Neurologically Impaired Patients

Exoskeleton robots have emerged as innovative assistive and rehabilitative devices, particularly beneficial for individuals experiencing force deficits or undergoing recovery from conditions such as stroke. The successful integration and control of these devices are heavily reliant on the human-machine interface (HMI), a critical component that enables users to seamlessly operate the exoskeleton as an extension of their own body. This paper focuses on the development of a real-time myoprocessor for an upper limb exoskeleton, designed to enhance the HMI at the neuromuscular level. The myoprocessor, a real-time model of human muscle, is integrated into the exoskeleton system to predict joint torques based on joint kinematics and neural activation levels, mirroring the natural function of the human body.

The myoprocessor is based on the Hill phenomenological muscle model, a widely accepted representation of muscle behavior. Genetic algorithms are employed to fine-tune the internal parameters of the myoprocessor, optimizing its performance using an experimental database. This database provides both input data for the model and a benchmark for evaluating its accuracy. The findings demonstrate a high level of correlation between the joint moment predictions generated by the myoprocessor model and the actual data collected from human subjects. This validation confirms the model's reliability and robustness, making it a suitable candidate for incorporation into the exoskeleton's control system.

The IntelliArm exoskeleton, equipped with this advanced myoprocessor, represents a significant step forward in the rehabilitation of patients with neurological impairments. By providing intuitive control and seamless integration, IntelliArm offers a promising solution for enhancing the quality of life and facilitating the recovery process for individuals affected by neuromuscular disorders.

This paper proposes a Myo device that has electromyography (EMG) sensors for detecting electrical activities from different parts of the forearm muscles; it also has a gyroscope and an accelerometer. EMG sensors detect and provide very clear and important data from muscles compared with other types of sensors. The Myo armband sends data from EMG, gyroscope, and accelerometer sensors to a computer via Bluetooth and uses these data to control a virtual robotic arm which was built in Unity 3D. Virtual robotic arms based on EMG, gyroscope, and accelerometer sensors have different features. A robotic arm based on EMG is controlled by using the tension and relaxation of muscles. Consequently, a virtual robotic arm based on EMG is preferred for a hand amputee to a virtual robotic arm based on a gyroscope and an accelerometer

Effective physical Human-Robot Interaction (pHRI) needs to account for variable human dynamics and also predict human intent. Recently, there has been a lot of progress in adaptive impedance and admittance control for human-robot interaction. Not as many contributions have been reported on online adaptation schemes that can accommodate users with varying physical strength and skill level during interaction with a robot. The goal of this paper is to present and evaluate a novel adaptive admittance controller that can incorporate human intent, nominal task models, as well as variations in the robot dynamics. An outer-loop controller is developed using an ARMA model which is tuned using an adaptive inverse control technique. An inner-loop neuroadaptive controller linearizes the robot dynamics. Working in conjunction and online, this two-loop technique offers an elegant way to decouple the pHRI problem. Experimental results are presented comparing the performance of different types of admittance controllers. The results show that efficient online adaptation of the robot admittance model for different human subjects can be achieved. Specifically, the adaptive admittance controller reduces jerk which results in a smooth human-robot interaction.

A quasi-passive leg exoskeleton is presented for load-carrying augmentation during walking. The exoskeleton has no actuators, only ankle and hip springs and a knee variabledamper. Without a payload, the exoskeleton weighs 11.7kg and requires only 2 Watts of electrical power during loaded walking. For a 36 kg payload, we demonstrate that the quasi-passive exoskeleton transfers on average 80% of the load to the ground during the single support phase of walking. By measuring the rate of oxygen consumption on a study participant walking at a self-selected speed, we find that the exoskeleton slightly increases the walking metabolic cost of transport (COT) as compared to a standard loaded backpack (10% increase). However, a similar exoskeleton without joint springs or damping control (zero-impedance exoskeleton) is found to increase COT by 23% compared to the loaded backpack, highlighting the benefits of passive and quasi-passive joint mechanisms in the design of efficient, low-mass leg exoskeletons.

In the nearly six decades since researchers began to explore methods of creating them, exoskeletons have progressed from the stuff of science fiction to nearly commercialized products. While there are still many challenges associated with exoskeleton development that have yet to be perfected, the advances in the field have been enormous. In this paper, we review the history and discuss the state-of-the-art of lower limb exoskeletons and active orthoses. We provide a design overview of hardware, actuation, sensory, and control systems for most of the devices that have been described in the literature, and end with a discussion of the major advances that have been made and hurdles yet to be overcome.

In order to control a reaching movement of the arm and body, several different computational problems must be solved. Some parallel methods that could be implemented in networks of neuron-like processors are described. Each method solves a different part of the overall task. First, a method is described for finding the torques necessary to follow a desired trajectory. The methods is more economical and more versatile than table look-up and requires very few sequential steps. Then a way of generating an internal representation of a desired trajectory is described. This method shows the trajectory one piece at a time by applying a large set of heuristic rules to a "motion blackboard" that represents the static and dynamic parameters of the state of the body at the current point in the trajectory. The computations are simplified by expressing the positions, orientations, and motions of parts of the body in terms of a single, non-accelerating, world-based frame of reference, rather than in terms of the joint-angles or an egocentric frame based on the body itself.

A wheelchair-mounted robotic arm (WMRA) system was designed and built to meet the needs of mobilityimpaired persons with limitations of upper extremities, and to exceed the capabilities of current devices of this type. The control of this 9-degree-of-freedom system expands upon conventional control methods and combines the 7-DoF robotic arm control with the 2-degree-of-freedom power wheelchair control. The 3- degrees of redundancy are optimized to effectively perform activities of daily living and overcome singularities, joint limits and some workspace limitations. The control system is designed for teleoperated or autonomous coordinated Cartesian control, which offers expandability for future research. A P300 Brain Computer Interface (BCI), the BCI2000, was implemented to control the WMRA system. The control is done by recording and analysing the brain activity through an electrode cap while providing visual stimulation to the user via a visual matrix. The visual matrix contains a symbolic or an alphabetic array corresponding to the motion of the WMRA. By recognizing online and in real-time, which element in the matrix elicited a P300, the BCI system can identify which element the user chose to communicate. The chosen element is then communicated to the controller of the WMRA system. The speed and accuracy of the BCI system was tested. This paper gives details of the WMRA's integration with the BCI2000 and documents the experimental results of the BCI and the WMRA in simulation.

Postural balance control of machines is one of the most important human motor skills. We present a balance equilibrium manifold (BEM) concept to study how a human rider balances a bicycle-like robot (i.e., bikebot) while maintaining tracking a desired trajectory. The BEM is built on the external-internal convertible (EIC) structure of the rider-bikebot dynamics to capture the balance properties of the rider-bikebot systems. We analyze the contribution of the upper-body movements and the steering inputs to the task of balance control of the systems. The results reveal that steering actuation is more effective than upper-body movement for balance task. A performance metric is also introduced to quantify the balance motor skills using the BEM. Extensive experiments are conducted to validate and demonstrate the analyses and the proposed balance skill metrics.

We propose an extensible pneumatic continuum arm that elongates to perform reaching movements and object grasping, and is suspended on the ceiling to prevent interference with human workers in a desktop workspace. The selected actuators with bellows aim to enhance the arm motion capabilities. A single actuator can provide a maximum tension force of 150 N, and the proposed arm has a three-segment structure with a bundle of three actuators per segment. We measured the three-dimensional motion at the arm tip by using an optical motion-capture system. The corresponding results show that the arm can grasp objects with approximate radius of 80 mm and reach any point on the desktop. Furthermore, the maximum elongation ratio is 180%, with length varying between 0.75 m and 2.1 m. Experiments verified that the arm can grasp objects of various sizes and shapes. Moreover, we demonstrate the vertical transportation of objects taking advantage of the arm extensibility. We expect to apply the proposed arm for tasks such as grasping objects, illuminating desktops, and physically interacting with users.

While multi-fingered robotic hands have been developed for decades, none has been used for surgical operations. μAngelo is an anthropomorphic master-slave system for teleoperated robot-assisted surgery. As part of this system, this paper focuses on its slave instrument, a miniature three-digit hand. The design of the mechanism of such a manipulator poses a challenge due to the required miniaturization and the many active degrees of freedom. As the instrument has a human-centered design, its relation to the human hand is discussed. Two ways of routing its cable-driven mechanism are investigated and the method of deriving the input-output functions that drive the mechanism is presented.

The first energetically autonomous lower extremity exoskeleton capable of carrying a payload has been demonstrated at U.C. Berkeley. This paper summarizes the mechanical design of the Berkeley Lower Extremity Exoskeleton (BLEEX). The anthropomorphically-based BLEEX has seven degrees of freedom per leg, four of which are powered by linear hydraulic actuators. The selection of the degrees of freedom and their ranges of motion are described. Additionally, the significant design aspects of the major BLEEX components are covered.


Question: IntelliArm: An exoskeletal device for the assessment and therapy of individuals with neurological disabilities.

This document details the evolution and clinical application of mechatronic systems designed for post-stroke therapy, developed at the VA Palo Alto in conjunction with Stanford University. Over a span of more than two decades, researchers have explored the potential of robotics in delivering rehabilitation services, particularly for individuals with severe disabilities. The paper highlights the development and testing of three robotic systems that are specifically tailored for therapy after a stroke, outlining the guiding principles and insights gained throughout their development. A distinctive feature of the Palo Alto approach is the provision of bimanual, mirror-image, patient-directed therapeutic exercises, which are crucial for enhancing motor skills and coordination. The initial validation of this concept was achieved with a 2-degree-of-freedom (DOF) elbow/forearm manipulator. Further advancements were made with the second-generation therapy robot, which facilitated planar forearm movements. Clinical assessments on 19 hemiplegic and control subjects confirmed the efficacy and consistency of interaction forces during robot-assisted upper-limb exercises. Subsequent clinical trials involving 21 chronic stroke patients compared the outcomes of 3-D robot-assisted therapy against conventional therapy methods. The results showed a significant improvement in motor recovery, as measured by the Fugl-Meyer (FM) scale, in the group that received robot-assisted therapy, demonstrating its superiority over traditional therapy in promoting recovery.

With the growth of elderly population in our society, technology will play an important role in providing functional mobility to humans. In this paper, we propose a robot walking helper with both passive and active control modes of guidance. From the perspective of human safety, the passive mode adopts the braking control law on the wheels to differentially steer the vehicle. The active mode can guide the user efficiently when the passive control with user-applied force is not adequate for guidance. The theory of differential flatness is used to plan the trajectory of control gains within the proposed scheme of the controller. Since the user input force is not known a-priori, the theory of model predictive control is used to periodically compute the trajectory of these control gains. The simulation results show that the walking assist robot, along with the structure of this proposed control scheme, can guide the user to a goal effectively.

The Kinect sensor is an attachment for the Xbox gaming console which allows players to interact with games through body movement. This paper explores the possibility of using the Kinect in a clinical setting for detection of Parkinson's, postural, flapping, titubation and voice tremors. All of the physical tremors were simulated and the ability of the Kinect to capture them was evaluated. The Kinect was also used to record voice data from real voice tremors patients. Physical and voice data were gathered from healthy persons for comparison. The results showed that the Kinect could reliably detect voice, postural and Parkinson's tremors. A very consistent graph could be obtained repeatedly for both Parkinson's and postural tremors. For voice tremors there was also a consistent pattern that differentiated a normal voice from one with a tremor. We have therefore proven that the Kinect can consistently record Parkinson's, postural and voice tremors.

Approximately one-third of patients with stroke exhibit persistent disability after the initial cerebrovascular episode, with motor impairments accounting for most poststroke disability. Exercise and training have long been used to restore motor function after stroke. Better training strategies and therapies to enhance the effects of these rehabilitative protocols are currently being developed for poststroke disability. The advancement of our understanding of the neuroplastic changes associated with poststroke motor impairment and the innate mechanisms of repair is crucial to this endeavor. Pharmaceutical, biological and electrophysiological treatments that augment neuroplasticity are being explored to further extend the boundaries of poststroke rehabilitation. Potential motor rehabilitation therapies, such as stem cell therapy, exogenous tissue engineering and brain–computer interface technologies, could be integral in helping patients with stroke regain motor control. As the methods for providing motor rehabilitation change, the primary goals of poststroke rehabilitation will be driven by the activity and quality of life needs of individual patients. This Review aims to provide a focused overview of neuroplasticity associated with poststroke motor impairment, and the latest experimental interventions being developed to manipulate neuroplasticity to enhance motor rehabilitation.

Brain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains.

We present a socially assistive robot (SAR) system designed to engage elderly users in physical exercise. We discuss the system approach, design methodology, and implementation details, which incorporate insights from psychology research on intrinsic motivation, and we present five clear design principles for SAR-based therapeutic interventions. We then describe the system evaluation, consisting of a multi-session user study with older adults (n = 33), to evaluate the effectiveness of our SAR exercise system and to investigate the role of embodiment by comparing user evaluations of similar physically and virtually embodied coaches. The results validate the system approach and effectiveness at motivating physical exercise in older adults according to a variety of user performance and outcomes measures. The results also show a clear preference by older adults for the physically embodied robot coach over the virtual coach in terms of enjoyableness, helpfulness, and social attraction, among other factors.

Backdrivability is a keyword with rising importance, not only in humanoid robots, but also to wearable robots. Power assist robots, namely exoskeletons are expected to provide mobility and independence to elderly and people with disability. In such robots, without backdrivability, error between human and robot motion can be painful; sometimes dangerous. In this research, we apply a type of hydraulic actuator, specially designed to realize backdrivability, to exoskeletal robot to improve backdrivability. We present the basic methodology to apply such hydraulic actuator to wearable robots. We developed prototype of knee joint power assist exoskeleton and performed fundamental tests to verify the design method validity.

Electroencephalogram (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by, e.g., patients with amyotrophic lateral sclerosis (ALS) to develop a simple binary response in order to reply to specific questions. Four subjects participated in a series of on-line sessions with an EEG-based cursor control. The EEG was recorded from electrodes overlying sensory-motor areas during left and right motor imagery. The EEG signals were analyzed in subject-specific frequency bands and classified on-line by a neural network. The network output was used as a feedback signal. The on-line error (100%-perfect classification) was between 10.0 and 38.1%. In addition, the single-trial data were also analyzed off-line by using an adaptive autoregressive (AAR) model of order 6. With a linear discriminant analysis the estimated parameters for left and right motor imagery were separated. The error rate obtained varied between 5.8 and 32.8% and was, on average, better than the on-line results. By using the AAR-model for on-line classification an improvement in the error rate can be expected, however, with a classification delay around 1 s.

This paper presents the concept for a test setup to prototype control algorithms for a multi-finger gripper. The human-robot interface has to provide enough degrees of freedom (DOF) to intuitively control advanced gripper and to accomplish this task a simple sensor glove equipped with flex and force sensors has been prepared for this project. The software architecture has to support both real hardware and simulation, as well as flexible communication standards, therefore, ROS architecture was employed in this matter. Paper presents some preliminary results for using sensor glove and simulated model of the three finger gripper.

The purpose of this paper is to present and describe a motor assessment scale (MAS) for stroke patients and to report on the investigation of two aspects of its reliability. The MAS is a brief and easily administered assessment of eight areas of motor function and one item related to muscle tone. Each item is scored on a scale from 0 to 6. To check interrater reliability, we videotaped five stroke patients while they were being assessed with the MAS. These scores were used as the criterion ratings. Twenty raters then assessed these patients, and their results were correlated with the criterion ratings. We determined test-retest reliability by assessing on two occasions, separated by a four-week interval, 14 stroke patients whose recovery was considered to be stable and by correlating these scores. The MAS was found to be highly reliable with an average interrater correlation of .95 and an average test-retest correlation of .98.

OBJECTIVE
Brain-computer interfaces (BCIs) could potentially be used to interact with pathological brain signals to intervene and ameliorate their effects in disease states. Here, we provide proof-of-principle of this approach by using a BCI to interpret pathological brain activity in patients with advanced Parkinson disease (PD) and to use this feedback to control when therapeutic deep brain stimulation (DBS) is delivered. Our goal was to demonstrate that by personalizing and optimizing stimulation in real time, we could improve on both the efficacy and efficiency of conventional continuous DBS.

METHODS
We tested BCI-controlled adaptive DBS (aDBS) of the subthalamic nucleus in 8 PD patients. Feedback was provided by processing of the local field potentials recorded directly from the stimulation electrodes. The results were compared to no stimulation, conventional continuous stimulation (cDBS), and random intermittent stimulation. Both unblinded and blinded clinical assessments of motor effect were performed using the Unified Parkinson's Disease Rating Scale.

RESULTS
Motor scores improved by 66% (unblinded) and 50% (blinded) during aDBS, which were 29% (p = 0.03) and 27% (p = 0.005) better than cDBS, respectively. These improvements were achieved with a 56% reduction in stimulation time compared to cDBS, and a corresponding reduction in energy requirements (p < 0.001). aDBS was also more effective than no stimulation and random intermittent stimulation.

INTERPRETATION
BCI-controlled DBS is tractable and can be more efficient and efficacious than conventional continuous neuromodulation for PD.


Question: IntelliArm: An exoskeletal device for the assessment and rehabilitation of individuals with neurological deficits.

Innovative Robotics in Neurorehabilitation: A Comprehensive Review
The utilization of robotics and automation in neurorehabilitation has shown significant potential in enhancing the recovery of upper extremity function in patients with neurological injuries. This paper offers a detailed review of the current state-of-the-art robotic devices designed for upper limb therapy in individuals suffering from neurological impairments. These devices, including exoskeletons and end-effector robots, aim to facilitate task-oriented, repetitive movements, which are crucial for improving muscle strength and coordination. The review critically evaluates these devices based on their technical capabilities, suitability for clinical use, and, where available, the impact on patient outcomes. This analysis highlights the role of robotics in supporting, augmenting, and monitoring the rehabilitation process, providing therapists with valuable data to tailor treatment plans more effectively.

OBJECTIVE
Brain-computer interfaces (BCIs) could potentially be used to interact with pathological brain signals to intervene and ameliorate their effects in disease states. Here, we provide proof-of-principle of this approach by using a BCI to interpret pathological brain activity in patients with advanced Parkinson disease (PD) and to use this feedback to control when therapeutic deep brain stimulation (DBS) is delivered. Our goal was to demonstrate that by personalizing and optimizing stimulation in real time, we could improve on both the efficacy and efficiency of conventional continuous DBS.

METHODS
We tested BCI-controlled adaptive DBS (aDBS) of the subthalamic nucleus in 8 PD patients. Feedback was provided by processing of the local field potentials recorded directly from the stimulation electrodes. The results were compared to no stimulation, conventional continuous stimulation (cDBS), and random intermittent stimulation. Both unblinded and blinded clinical assessments of motor effect were performed using the Unified Parkinson's Disease Rating Scale.

RESULTS
Motor scores improved by 66% (unblinded) and 50% (blinded) during aDBS, which were 29% (p = 0.03) and 27% (p = 0.005) better than cDBS, respectively. These improvements were achieved with a 56% reduction in stimulation time compared to cDBS, and a corresponding reduction in energy requirements (p < 0.001). aDBS was also more effective than no stimulation and random intermittent stimulation.

INTERPRETATION
BCI-controlled DBS is tractable and can be more efficient and efficacious than conventional continuous neuromodulation for PD.

BACKGROUND
About 50% of the patients with advanced Parkinson's disease (PD) suffer from freezing of gait (FOG), which is a sudden and transient inability to walk. It often causes falls, interferes with daily activities and significantly impairs quality of life. Because gait deficits in PD patients are often resistant to pharmacologic treatment, effective non-pharmacologic treatments are of special interest.

OBJECTIVES
The goal of our study is to evaluate the concept of a wearable device that can obtain real-time gait data, processes them and provides assistance based on pre-determined specifications.

METHODS
We developed a real-time wearable FOG detection system that automatically provides a cueing sound when FOG is detected and which stays until the subject resumes walking. We evaluated our wearable assistive technology in a study with 10 PD patients. Over eight hours of data was recorded and a questionnaire was filled out by each patient.

RESULTS
Two hundred and thirty-seven FOG events have been identified by professional physiotherapists in a post-hoc video analysis. The device detected the FOG events online with a sensitivity of 73.1% and a specificity of 81.6% on a 0.5 sec frame-based evaluation.

CONCLUSIONS
With this study we show that online assistive feedback for PD patients is possible. We present and discuss the patients' and physiotherapists' perspectives on wearability and performance of the wearable assistant as well as their gait performance when using the assistant and point out the next research steps. Our results demonstrate the benefit of such a context-aware system and motivate further studies.

Information theory has recently been employed to specify more precisely than has hitherto been possible man's capacity in certain sensory, perceptual, and perceptual-motor functions (5, 10, 13, 15, 17, 18). The experiments reported in the present paper extend the theory to the human motor system. The applicability of only the basic concepts, amount of information, noise, channel capacity, and rate of information transmission, will be examined at this time. General familiarity with these concepts as formulated by recent writers (4,11, 20, 22) is assumed. Strictly speaking, we cannot study man's motor system at the behavioral level in isolation from its associated sensory mechanisms. We can only analyze the behavior of the entire receptor-neural-effector system. How-

This paper presents the EACare project, an ambitious multidisciplinary collaboration with the aim to develop an embodied system, capable of carrying out neuropsychological tests to detect early signs of dementia, e.g., due to Alzheimer’s disease. The system will use methods from Machine Learning and Social Robotics, and be trained with examples of recorded clinician-patient interactions. The interaction will be developed using a participatory design approach. We describe the scope and method of the project, and report on a first Wizard of Oz prototype.

Cephalosomatic anastomosis has been carried out in both monkeys and mice with preservation of brain function. Nonetheless the spinal cord was not reconstructed, leaving the animals unable to move voluntarily. Here we review the details of the GEMINI spinal cord fusion protocol, which aims at restoring electrophysiologic conduction across an acutely transected spinal cord. The existence of the cortico-truncoreticulo-propriospinal pathway, a little-known anatomic entity, is described, and its importance concerning spinal cord fusion emphasized. The use of fusogens and electrical stimulation as adjuvants for nerve fusion is addressed. The possibility of achieving cephalosomatic anastomosis in humans has become reality in principle.

Electromyography (EMG) is a technique that studies the electrical activity of the muscle. EMG is the basic component of Myo armband. The MYO ARMBAND is a device which can be used by humans to interact with computers. The myo armband involves the interaction of devices in VE (virtual environment). Working and characteristics of Myo are studied and analyzed. This paper surveys the work on visual analysis of gestures by using Myo armband and the input from keyboard and mouse. We have also performed two tests where participants are allowed to play a game, first by using keyboard and other by using MYO. Discussion and conclusion are also written at the end of this paper.

We have been developing a wearable robot that directly and physically supports human movement. A ldquomuscle suitrdquo for the arm that will provide muscular support for the paralyzed or those otherwise unable to move unaided is one example. Whereas low back pain is one of the most serious issue for the manual worker and in this paper, we focus on developing the support system for forward tilting of the upper body. In this paper, the concept and mechanical structure of the support system is shown. From the experimental result to keep forward tilting posture with load indicates our system is very effective for keeping forward tilting posture and it becomes clear that more than 60% reduction of muscular power is possible.

We present GRAVITY-ASSIST, a series elastic active body weight support and inertia compensation system for use in robot assisted gait rehabilitation. The device consists of a single degree of freedom series elastic actuator that connects to the trunk of a patient. The series elastic system is novel in that, it can provide the desired level of dynamic unloading such that the patient experiences only a percentage of his/her weight and inertia. Inertia compensation is important, since the inertial forces can cause significant deviations from the desired unloading force, specially at low support forces and fast walking speeds. Furthermore, this feature enables the inertia of the harness and force sensing unit attached to the patient to be compensated for, making sure that the device does not interfere with the natural gait cycle. We present a functional prototype of the device, its characterization and experimental verification of the approach.

BACKGROUND
The elastic therapeutic taping has been considered a promising resource for disabled children.

OBJECTIVE
To systematically review the evidence of the effects of elastic therapeutic taping on motor function in children with motor impairments.

METHOD
Three independent evaluators conducted searches in electronic databases (MEDLINE/PubMed, Scopus, LILACS, BIREME/BVS, Science Direct, SciELO, and PEDro). Clinical studies design, published until 2016, involving elastic therapeutic taping and children aged 0-12 years with motor impairments were included. The variables considered were the methodological aspects (study design, participants, outcome measurements, and experimental conditions); results presented in the studies, and also the methodological quality of studies.

RESULTS
Final selection was composed by 12 manuscripts (five randomized controlled trials), published in the last 10 years. Among them, cerebral palsy (CP) was the most recurrent disorder (n = 7), followed by congenital muscular torticollis (n = 2) and brachial plexus palsy (n = 2). Positive results were associated with taping application: improvement in the upper limb function, gross motor skills, postural control, muscular balance, and performance in the dynamics functional and daily activities.

LIMITATIONS
Lower quality of the studies, clinical and population heterogeneity existed across studies.

CONCLUSIONS
The elastic therapeutic taping has been shown to be a promising adjunct resource to the conventional rehabilitation in children with motor impairments. However, high methodological studies about its efficacy in this population are already scarce. Implications for Rehabilitation Elastic therapeutic taping has been shown to be a promising adjunct resource to the conventional rehabilitation in disabled children. Clinical trials have indicated improvement in the postural control and functional activities with both, upper and lower limbs, and increase in the functional independency resulting from the taping use. Randomized control trials and well-established protocols are needed to increase the confidence in applying elastic therapeutic taping to specific clinical conditions.

We envision that diverse social exercising games, or exergames, will emerge, featuring much richer interactivity with immersive game play experiences. Further, the recent advances of mobile devices and wireless networking will make such social engagement more pervasive - people carry portable exergame devices (e.g., jump ropes) and interact with remote users anytime, anywhere. Towards this goal, we explore the potential of using heterogeneous exercise devices as game controllers for a multi-player social exergame; e.g., playing a boat paddling game with two remote exercisers (one with a jump rope, and the other with a treadmill). In this paper, we propose a novel platform called ExerLink that converts exercise intensity to game inputs and intelligently balances intensity/delay variations for fair game play experiences. We report the design considerations and guidelines obtained from the design and development processes of game controllers. We validate the efficacy of game controllers and demonstrate the feasibility of social exergames with heterogeneous exercise devices via extensive human subject studies.


Question: IntelliArm: An exoskeleton utilized for diagnosing and treating patients with neurological impairments.

This paper introduces a novel powered exoskeleton system that integrates human control with robotic assistance, specifically focusing on the elbow joint for natural operation. The Human-Robot interface is established at the neuromuscular level, utilizing electromyography (EMG) signals as the primary input for the exoskeleton's control system. The EMG signals, combined with joint kinematic data, are processed through a myoprocessor, which employs a Hill-based muscle model to estimate muscle moments at the elbow. A moment-based control strategy is implemented, integrating the myoprocessor's moment predictions with feedback from the human arm/exoskeleton interface and the external load. The exoskeleton, designed as a two-link, two-joint mechanism, is worn by the operator, with the shoulder joint fixed at predetermined positions and the actuator located on the exoskeleton's elbow joint. The operator is able to manipulate an external weight, located at the exoskeleton's end, while experiencing a scaled version of the load. The exoskeleton's actuator compensates for the residual external load on the joint. Four performance metrics are utilized to assess the quality of the human-machine integration and to delineate the system's operational capabilities. Experimental findings demonstrate that the use of processed EMG signals as control commands, in conjunction with external-load/human-arm moment feedback, markedly enhances the system's mechanical gain, while preserving the operator's intuitive control over the system. These results underscore the potential of EMG-based power exoskeleton systems as integrated human-machine systems, leveraging high-level neurological signals for control.

We present GRAVITY-ASSIST, a series elastic active body weight support and inertia compensation system for use in robot assisted gait rehabilitation. The device consists of a single degree of freedom series elastic actuator that connects to the trunk of a patient. The series elastic system is novel in that, it can provide the desired level of dynamic unloading such that the patient experiences only a percentage of his/her weight and inertia. Inertia compensation is important, since the inertial forces can cause significant deviations from the desired unloading force, specially at low support forces and fast walking speeds. Furthermore, this feature enables the inertia of the harness and force sensing unit attached to the patient to be compensated for, making sure that the device does not interfere with the natural gait cycle. We present a functional prototype of the device, its characterization and experimental verification of the approach.

OBJECTIVE
To evaluate the effect of hippotherapy (physical therapy utilizing the movement of a horse) on muscle activity in children with spastic cerebral palsy.

DESIGN
Pretest/post-test control group.

SETTING/LOCATION
Therapeutic Riding of Tucson (TROT), Tucson, AZ.

SUBJECTS
Fifteen (15) children ranging from 4 to 12 years of age diagnosed with spastic cerebral palsy.

INTERVENTIONS
Children meeting inclusion criteria were randomized to either 8 minutes of hippotherapy or 8 minutes astride a stationary barrel.

OUTCOME MEASURES
Remote surface electromyography (EMG) was used to measure muscle activity of the trunk and upper legs during sitting, standing, and walking tasks before and after each intervention.

RESULTS
After hippotherapy, significant improvement in symmetry of muscle activity was noted in those muscle groups displaying the highest asymmetry prior to hippotherapy. No significant change was noted after sitting astride a barrel.

CONCLUSIONS
Eight minutes of hippotherapy, but not stationary sitting astride a barrel, resulted in improved symmetry in muscle activity in children with spastic cerebral palsy. These results suggest that the movement of the horse rather than passive stretching accounts for the measured improvements.

The term 'synergy' - from the Greek synergia - means 'working together'. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project "The Hand Embodied" (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.

In this paper, we have developed a method to tele-control a robot arm to imitate human writing skills using electromyography (EMG) signals. The control method is implemented on the Baxter® robot with a brush attached on the endpoint of its arm to imitate human writing skills, and a MYO sensor is used to measure the surface electromyographic (sEMG) signals generated by contractions of human muscles involved in writing tasks. A haptic device Sensable® Omni is used to control the motion of the Baxter® robot arm, and V-Rep® is employed to simulate the movement of the arm and to calculate the inverse kinematic of Baxter® robot arm. All the communications for Baxter® robot, V-Rep simulator, Omni device and MYO are integrated in MATLAB®/Simulink®. The main test is to make the Baxter® arm following the movement of a human subject when writing with Omni stylus, and the EMG signals are processed using low pass filter and moving average technique to extract the smoothed envelope which is utilised to control the variation of position instead of variation of force along the vertical Z axis, so when the operator writes with force variations, the Baxter® can draw lines with variable thickness. This imitation system is successfully tested to make the Baxter® arm to follow the writing movement of the operator's hand and would have potential applications in more complicated human robot teleoperation tasks such as tele-rehabilitation and tele-surgery.

A twisted string actuation module with an integrated force sensor based on optoelectronic components is presented in this paper. This solution is especially suited for very compact, light-weight and wearable robotic devices, such as exoskeletons, but is appropriate for various robotic applications. An in-depth presentation of the proposed actuation module, the description and the basic sensor working principle of the integrated force sensor are portrayed and discussed. Extensive experimental measures have been carried out to verify the actuation module compliant frame design and the related finite element analysis results. Therefore, the proposed actuation module has been used for the implementation of a simple assistive application where surface-electromyographic signals are used to detect the user's muscle activity in order to control the support action provided by the actuator, thus reducing his/her effort.

This paper presents a portable, assistive, soft robotic glove designed to augment hand rehabilitation for individuals with functional grasp pathologies. The robotic glove utilizes soft actuators consisting of molded elastomeric chambers with fiber reinforcements that induce specific bending, twisting and extending trajectories under fluid pressurization. These soft actuators were mechanically programmed tomatch and support the range of motion of individual fingers. They demonstrated the ability to generate significant force when pressurized and exhibited low impedance when un-actuated. To operate the soft robotic glove, a control hardware system was designed and included fluidic pressure sensors in line with the hydraulic actuators and a closed-loop controller to regulate the pressure. Demonstrations with the complete system were performed to evaluate the ability of the soft robotic glove to carry out gross and precise functional grasping. Compared to existing devices, the soft robotic glove has the potential to increase user freedom and independence through its portable waist belt pack and open palm design. © 2014 Elsevier B.V. All rights reserved.

In this work an electromechanical hand is implemented based on the study and implementation of the phalanges of the five fingers of the hand, a control system based on a microcontroller was implemented that takes as feedback signals coming from flexosensors and strain gauges. The movement of the fingers was made based on myoelectric signals in real time, these signals were acquired through an innovative method of acquisition based on patterns. The tests performed were measurements with extended hand, closed hand and pressing an object that validates the present proposal.

Information theory has recently been employed to specify more precisely than has hitherto been possible man's capacity in certain sensory, perceptual, and perceptual-motor functions (5, 10, 13, 15, 17, 18). The experiments reported in the present paper extend the theory to the human motor system. The applicability of only the basic concepts, amount of information, noise, channel capacity, and rate of information transmission, will be examined at this time. General familiarity with these concepts as formulated by recent writers (4,11, 20, 22) is assumed. Strictly speaking, we cannot study man's motor system at the behavioral level in isolation from its associated sensory mechanisms. We can only analyze the behavior of the entire receptor-neural-effector system. How-

This paper introduces a novel human activity monitoring system combining Inertial Measurement Units (IMU) and Mechanomyographic (MMG) muscle sensing technology. While other work has recognised and implemented systems for combined muscle activity and motion recording, they have focused on muscle activity through EMG sensors, which have limited use outside controlled environments. MMG is a low frequency vibration emitted by skeletal muscle whose measurement does not require gel or adhesive electrodes, potentially offering much more efficient implementation for pervasive use. We have developed a combined IMU and MMG sensor the fusion of which provides a new dimension in human activity monitoring and bridges the gap between human dynamics and muscle activity. Results show that synergy between motion and muscle detection is not only viable, but straightforward, inexpensive, and can be packaged in a lightweight wearable package.

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals. That men are machines (whatever else they may be) has long been suspected; but not till our generation have men fairly felt in concrete just what wonderful psycho-neuro-physical mechanisms they are. William James (1893; 1842 – 1910) A. Fred, J. Filipe, and H. Gamboa (Eds.): BIOSTEC 2009, CCIS 52, pp. 21–47, 2010. c © Springer-Verlag Berlin Heidelberg 2010 22 E.L. van den Broek et al.


Question: IntelliArm: An exoskeletal device for the assessment and therapy of individuals with neurological deficits.

This study explores the feasibility of utilizing an electromyography (EMG)-driven musculoskeletal model to accurately estimate knee joint moments and muscle forces in dynamic, real-world conditions. The objective was to ascertain whether such a model could reliably predict knee moments, typically calculated through inverse dynamics, across a diverse array of contractile scenarios. To achieve this, the lengths of muscle-tendon units and moment arms of 13 muscles that span the knee joint were determined from three-dimensional joint kinematics, using a detailed anatomical model of the lower limb. Muscle activation levels were estimated using a second-order discrete non-linear model, with rectified and filtered EMG data serving as input. A refined Hill-type muscle model was employed to calculate individual muscle forces, factoring in both activation levels and muscle-tendon lengths. The model was fine-tuned for six subjects by adjusting a set of parameters grounded in physiological principles, through a mathematical optimization process aimed at aligning the net flexion-extension (FE) muscle moment with measurements obtained via inverse dynamics. Calibration was performed using data from five distinct tasks, encompassing passive and active FE in an isokinetic dynamometer, running, and cutting maneuvers, all captured with three-dimensional motion analysis. Post-calibration, the model's predictive capability was tested against over 200 tasks involving isokinetic dynamometer use, running, and sidestepping. The predictions of inverse dynamics joint moments were found to have an average R(2) value of 0.91 and a mean residual error of roughly 12 Nm. Re-calibration, focusing solely on the EMG-to-activation parameters, demonstrated that the model could maintain similar accuracy over weeks. Enhancing the muscle model to better reflect physiological reality resulted in improved predictions. The proposed modeling technique provides an effective method for estimating in vivo muscle forces during various movement tasks.

Despite significant advancements in the field of wearable robots (WRs), commercial WRs still use traditional direct-drive actuation units to power their joints. On the other hand, in research prototypes compliant actuators are increasingly being used to more adequately address the issues of safety, robustness, control and overall system efficiency. The advantages of mechanical compliance are exploited in a novel modular actuator prototype designed for the knee joint. Due to its modularity, the actuator can be implemented in a knee joint of a standalone or a multi-joint lower-limbs orthosis, for use in gait rehabilitation and/or walking assistance. Differently from any other actuator used in orthotic research prototypes, it combines a Variable Stiffness Actuator (VSA) and a Parallel Elasticity Actuation (PEA) unit in a single modular system. Although independent, the units are designed to work together in order to fully mimic dynamic behavior of the human knee joint. In this paper, design aspects and functional evaluation of the new actuator are presented and a rationale for such a design in biomechanics of the human knee joint is given. The VSA subsystem is characterized in a quasi-static benchmarking environment and the results showing main performance indicators are presented.

A smart wheelchair (SW) is a power wheelchair (PW) to which computers, sensors, and assistive technology are attached. In the past decade, there has been little effort to provide a systematic review of SW research. This paper aims to provide a complete state-of-the-art overview of SW research trends. We expect that the information gathered in this study will enhance awareness of the status of contemporary PW as well as SW technology and increase the functional mobility of people who use PWs. We systematically present the international SW research effort, starting with an introduction to PWs and the communities they serve. Then, we discuss in detail the SW and associated technological innovations with an emphasis on the most researched areas, generating the most interest for future research and development. We conclude with our vision for the future of SW research and how to best serve people with all types of disabilities.

This paper presents the design of the lightweight BioRob manipulator with spring-loaded tendon-driven actuation developed for safe physical human-robot interaction. The safety of the manipulator is analyzed by an analytical worst-case estimation of impact and clamping forces in the absence of collision detection. As intrinsic joint compliance can pose a threat by storing energy, a safety evaluation method is proposed taking the potential energy stored in the elastic actuation into account. The evaluation shows that the robot arm design constrains the worst case clamping forces to only 25 N, while being able to handle loads up to 2 kg, and inherits extremely low impact properties, such as an effective mass of less than 0.4 kg in non near-singular configurations, enabling safe operation even in case of high velocities. The results are validated in simulation and experiments.

The octopus is a marine animal whose body has no rigid structures. It has eight arms composed of a peculiar muscular structure, named a muscular hydrostat. The octopus arms provide it with both locomotion and grasping capabilities, thanks to the fact that their stiffness can change over a wide range and can be controlled through combined contractions of the muscles. The muscular hydrostat can better be seen as a modifiable skeleton. Furthermore, the morphology the arms and the mechanical characteristics of their tissues are such that the interaction with the environment (i.e., water) is exploited to simplify control. Thanks to this effective mechanism of embodied intelligence, the octopus can control a very high number of degrees of freedom, with relatively limited computing resources. From these considerations, the octopus emerges as a good model for embodied intelligence and for soft robotics. The prototype of a robot arm has been built based on an artificial muscular hydrostat inspired to the muscular hydrostat of the Octopus vulgaris. The prototype presents the morphology of the biological model and the broad arrangement of longitudinal and transverse muscles. Actuation is obtained with cables (longitudinally) and with shape memory alloy springs (transversally). The robot arm combines contractions and it can show the basic movements of the octopus arm, like elongation, shortening and bending, in water. © Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2012

The XWand is a novel wireless sensor package that enables styles of natural interaction with intelligent environments. For example, a user may point the wand at a device and control it using simple gestures. The XWand system leverages the intelligence of the environment to best determine the user's intention. We detail the hardware device, signal processing algorithms to recover position and orientation, gesture recognition techniques, a multimodal (wand and speech) computational architecture and a preliminary user study examining pointing performance under conditions of tracking availability and audio feedback.

We present the novel kinematics, workspace characterization, functional prototype and impedance control of a six degrees of freedom haptic interface designed to train surgeons for laparoscopic procedures, through virtual reality simulations. The parallel kinematics of the device is constructed by connecting a 3RRP planar parallel mechanism to a linearly actuated modified delta mechanism with a connecting link. The configuration level forward and inverse kinematics of the device assume analytic solutions, while its workspace can be shaped to enable large end-effector translations and rotations, making it well-suited for laparoscopy operations. Furthermore, the haptic interface features a low apparent inertia with high structural stiffness, thanks to its parallel kinematics with grounded actuators. A model-based open-loop impedance controller with feed-forward gravity compensation has been implemented for the device and various virtual tissue/organ stiffness levels have been rendered.

This study aims at robotic clothing assistance as it is yet an open field for robotics despite it is one of the basic and important assistance activities in daily life of elderly as well as disabled people. The clothing assistance is a challenging problem since robots must interact with non-rigid clothes generally represented in a high-dimensional space, and with the assisted person whose posture can vary during the assistance. Thus, the robot is required to manage two difficulties to perform the task of the clothing assistance: 1) handling of non-rigid materials and 2) adaptation of the assisting movements to the assisted person's posture. To overcome these difficulties, we propose to use reinforcement learning with the cloth's state which is low-dimensionally represented in topology coordinates, and with the reward defined in the low-dimensional coordinates. With our developed experimental system, for T-shirt clothing assistance, including an anthropomorphic dual-arm robot and a soft mannequin, we demonstrate the robot quickly learns a suitable arm motion for putting the mannequin's head into a T-shirt.

Clumsy intermediary devices constrain our interaction with computers and their applications. Glove-based input devices let us apply our manual dexterity to the task. We provide a basis for understanding the field by describing key hand-tracking technologies and applications using glove-based input. The bulk of development in glove-based input has taken place very recently, and not all of it is easily accessible in the literature. We present a cross-section of the field to date. Hand-tracking devices may use the following technologies: position tracking, optical tracking, marker systems, silhouette analysis, magnetic tracking or acoustic tracking. Actual glove technologies on the market include: Sayre glove, MIT LED glove, Digital Data Entry Glove, DataGlove, Dexterous HandMaster, Power Glove, CyberGlove and Space Glove. Various applications of glove technologies include projects into the pursuit of natural interfaces, systems for understanding signed languages, teleoperation and robotic control, computer-based puppetry, and musical performance.<<ETX>>

This study presents a novel myoelectric pattern recognition strategy towards restoration of hand function after incomplete cervical spinal cord Injury (SCI). High density surface electromyogram (EMG) signals comprised of 57 channels were recorded from the forearm of nine subjects with incomplete cervical SCI while they tried to perform six different hand grasp patterns. A series of pattern recognition algorithms with different EMG feature sets and classifiers were implemented to identify the intended tasks of each SCI subject. High average overall accuracies (>; 97%) were achieved in classification of seven different classes (six intended hand grasp patterns plus a hand rest pattern), indicating that substantial motor control information can be extracted from partially paralyzed muscles of SCI subjects. Such information can potentially enable volitional control of assistive devices, thereby facilitating restoration of hand function. Furthermore, it was possible to maintain high levels of classification accuracy with a very limited number of electrodes selected from the high density surface EMG recordings. This demonstrates clinical feasibility and robustness in the concept of using myoelectric pattern recognition techniques toward improved function restoration for individuals with spinal injury.

The term 'synergy' - from the Greek synergia - means 'working together'. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project "The Hand Embodied" (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.