Question: Applying artificial intelligence for forecasting trends in the stock market.

This paper presents an innovative method, Long Short-Term Memory (LSTM), aimed at enhancing the learning capability of recurrent neural networks (RNNs) to store information over long periods. The traditional method of recurrent backpropagation often encounters the issue of insufficient and decaying error backflow, leading to prolonged training times. We first revisit Hochreiter's (1991) analysis of this problem, and then propose LSTM as a solution. LSTM employs a novel, efficient, and gradient-based approach to overcome the challenges of long-term memory retention in RNNs. By selectively truncating the gradient where it does not impact learning, LSTM can successfully bridge time lags exceeding 1000 discrete-time steps. This is achieved by maintaining a constant error flow through special units called constant error carousels, while multiplicative gate units learn to control access to this error flow. LSTM is both spatially and temporally local, with computational complexity per time step and weight being O.1.

Our experimental evaluations using artificial data encompass various pattern representations, including local, distributed, real-valued, and noisy patterns. In comparative analyses with other algorithms such as real-time recurrent learning, backpropagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM outperforms significantly. It not only leads to a higher number of successful learning runs but also learns considerably faster. Moreover, LSTM demonstrates its efficacy by solving complex, artificial long-time-lag tasks that have remained unsolved by previous recurrent network algorithms. This makes LSTM a promising tool for applications requiring long-term memory and fast learning, such as stock market prediction.

Extracting up-to-date information from financial documents can be important in making investment decisions. However, the unstructured nature and enormity of the volume of such data makes manual analysis tedious and time consuming. Information extraction technology can be applied to automatically extract the most relevant and precise financial information. This paper introduces a rule-based information extraction methodology for the extraction of highly accurate financial information to aid investment decisions. The methodology includes a rule-based symbolic learning model trained by the Greedy Search algorithm and a similar model trained by the Tabu Search algorithm. The methodology has been found very effective in extracting financial information from NASDAQ-listed companies. Also, the Tabu Search based model performed better than some well-known systems. The simple rule structure makes the system portable and it should make parallel processing implementations easier.

According to UNESCO, education is a fundamental human right and every nation's citizens should be granted universal access with equal quality to it. Because this goal is yet to be achieved in most countries, in particular in the developing and underdeveloped countries, it is extremely important to find more effective ways to improve education. This paper presents a model based on the application of computational intelligence (data mining and data science) that leads to the development of the student's knowledge profile and that can help educators in their decision making for best orienting their students. This model also tries to establish key performance indicators to monitor objectives' achievement within individual strategic planning assembled for each student. The model uses random forest for classification and prediction, graph description for data structure visualization and recommendation systems to present relevant information to stakeholders. The results presented were built based on the real dataset obtained from a Brazilian private k-9 (elementary school). The obtained results include correlations among key data, a model to predict student performance and recommendations that were generated for the stakeholders.

Time series forecasting plays an important role in many fields such as economics, finance, business intelligence, natural sciences, and the social sciences. This forecasting task can be achieved by using different techniques such as statistical methods or Artificial Neural Networks (ANN). In this paper, we present two different approaches to time series forecasting: evolving Takagi-Sugeno (eTS) fuzzy model and ANN. These two different methods will be compared taking into account the different characteristic of each approach.

This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats. After analyzing the ways in which AI may influence the threat landscape in the digital, physical, and political domains, we make four high-level recommendations for AI researchers and other stakeholders. We also suggest several promising areas for further research that could expand the portfolio of defenses, or make attacks less effective or harder to execute. Finally, we discuss, but do not conclusively resolve, the long-term equilibrium of attackers and defenders.

Culprits’ identification by the means of suspicious pattern detection techniques from mobile device data is one of the most important aims of the mobile forensic data analysis. When criminal activities are related to entirely automated procedures such as malware propagation, predicting the corresponding behavior is a rather achievable task. However, when human behavior is involved, such as in cases of traditional crimes, prediction and detection become more compelling. This paper introduces a combined criminal profiling and suspicious pattern detection methodology for two criminal activities with moderate to the heavy involvement of mobile devices, cyberbullying and low-level drug dealing. Neural and Neurofuzzy techniques are applied on a hybrid original and simulated dataset. The respective performance results are measured and presented, the optimal technique is selected, and the scenarios are re-run on an actual dataset for additional testing and verification.

Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses. Doctor AI is a temporal model using recurrent neural networks (RNN) and was developed and applied to longitudinal time stamped EHR data from 260K patients over 8 years. Encounter records (e.g. diagnosis codes, medication codes or procedure codes) were input to RNN to predict (all) the diagnosis and medication categories for a subsequent visit. Doctor AI assesses the history of patients to make multilabel predictions (one label for each diagnosis or medication category). Based on separate blind test set evaluation, Doctor AI can perform differential diagnosis with up to 79% recall@30, significantly higher than several baselines. Moreover, we demonstrate great generalizability of Doctor AI by adapting the resulting models from one institution to another without losing substantial accuracy.

It is undeniable that artificial intelligence (AI) and blockchain concepts are spreading at a phenomenal rate. Both technologies have distinct degree of technological complexity and multi-dimensional business implications. However, a common misunderstanding about blockchain concept, in particular, is that “a blockchain is decentralized and therefore no one controls it”. But the underlying development of a blockchain system is still attributed to a cluster of core developers. Take smart contract as an example, it is essentially a collection of codes (or functions) and data (or states) that are programmed and deployed on a blockchain (say, Ethereum) by different human programmers. It is thus, unfortunately, less likely to be free of loopholes and flaws. In this article, through a brief overview about how artificial intelligence could be used to deliver bug-free smart contract so as to achieve the goal of blockchain 2.0, we to emphasize that the blockchain implementation can be assisted or enhanced via various AI techniques. The alliance of AI and blockchain is expected to create numerous possibilities.

Intelligent monitoring and control of critical infrastructure such as electric power grids, public water utilities and transportation systems produces massive volumes of time series data from heterogeneous sensor networks. Time Series Forecasting (TSF) is essential for system safety and security, and also for improving the efficiency and quality of service delivery. Being highly dependent on various external factors, the observed system behavior is usually stochastic, which makes the next value prediction a tricky and challenging task that usually needs customized methods. In this paper we propose a novel deep learning based framework for time series analysis and prediction by ensembling parametric and nonparametric methods. Our approach takes advantage of extracting features at different time scales, which improves accuracy without compromising reliability in comparison with the state-of-the-art methods. Our experimental evaluation using real-world SCADA data from a municipal water management system shows that our proposed method outperforms the baseline methods evaluated here.

Forecasting exchange rates is an important financial problem which has received much attention. Nowadays, neural network has become one of the effective tools in this research field. In this paper, we propose the use of a deep belief network (DBN) to tackle the exchange rate forecasting problem. A DBN is applied to predict both British Pound/US dollar and Indian rupee/US dollar exchange rates in our experiments. We use six evaluation criteria to evaluate its performance. We also compare our method to a feedforward neural network (FFNN), which is the state-of-the-art method for forecasting exchange rate with neural networks. Experiments indicate that deep belief networks (DBNs) are applicable to the prediction of foreign exchange rate, since they achieve better performance than feedforward neural networks (FFNNs).

Health informatics has emerged as a growing domain of interest among researchers world-wide owing to its major implications on society. Applications of machine learning in healthcare range from disease prediction to patient-level personalized services. The prevalence of big data in healthcare has paved the way for applications based on deep learning techniques in the past few years. This paper reviews recent trends and applications of deep learning applied to the healthcare domain. We highlight recent research work, identify challenges and suggest possible future directions that could be pursued further in this domain.


Question: Applying artificial intelligence for forecasting stock market trends.

This paper outlines a novel methodology for optimizing portfolios, asset allocations, and trading strategies through the application of direct reinforcement (DR) techniques. Unlike conventional methods, DR views investment decision-making as a stochastic control problem, enabling the direct discovery of effective strategies. We introduce a dynamic algorithm, Recurrent Reinforcement Learning (RRL), designed to uncover optimal investment policies without the need for explicit forecasting models, leading to enhanced trading performance. This approach contrasts with traditional reinforcement learning methods like TD-learning and Q-learning, which focus on estimating a value function for the control problem. RRL's direct reinforcement framework offers a more streamlined representation of the problem, circumvents the dimensionality issues associated with Bellman's curse, and demonstrates superior efficiency. We illustrate how direct reinforcement can be utilized to optimize risk-adjusted returns, including the differential Sharpe ratio, while accounting for transaction costs. Through extensive simulations using real financial data, we show that RRL-based strategies outperform those generated by Q-learning, a value function method. Practical applications of our approach include high-frequency currency trading and monthly asset allocation for the S&P 500 Stock Index and T-Bills, showcasing its effectiveness in real-world financial markets.

Organizations have been collecting data for decades, building massive data warehouses in which to store the data. Even though this data is available, very few of these organizations have been able to realize the actual value stored in it. The question these organizations are asking is how to extract meaningful data and uncover patterns and relationship from their databases. This paper presents a study of regression analysis for use in stock price prediction. Data were obtained from the daily official list of the prices of all shares traded on the stock exchange published by the Nigerian Stock Exchange using banking sector of Nigerian economy with three banks namely:First Bank of Nigeria Plc, Zenith Bank Plc, and Skye Bank Plc to build a database. A data mining software tool was used to uncover patterns and relationships and also to extract values of variables from the database to predict the future values of other variables through the use of time series data that employed moving average method. The tools were found capable technique to describe the trends of stock market prices and predict the future stock market prices of three banks sampled.

Nowadays, sentiment analysis is a popular research area in computer science. which aims to determine a person’s or a group’s mood, behaviour and opinion about any topic with using textual documents. With the proliferation of social micro-blogging sites, digital opinion text data is increased. Thus, many sentiment analysis researches are performed on these public data in different sociological fields, such as finance, economy and politics. In this paper, a novel sentiment analysis method is proposed on micro-blogging sites which uses new user metrics. Proposed method is used to measure financial community’s sentiment polarity on micro-blogging sites. In addition to that we analyze the correlation between the mood of financial community and the behavior of the Borsa Istanbul 100 index weekly by Pearson correlation coefficient method. Our test results show that this novel sentiment analysis method improves to the accuracy of linear relationship between the behavior of the stock market and the sentiment polarity of the financial community.

Supervised classification is one of the tasks most frequently carried out by so-called Intelligent Systems. Thus, a large number of techniques have been developed based on Artificial Intelligence (Logic-based techniques, Perceptron-based techniques) and Statistics (Bayesian Networks, Instance-based techniques). The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various classification algorithms and the recent attempt for improving classification accuracy—ensembles of classifiers.

Multivariate time series forecasting is extensively studied throughout the years with ubiquitous applications in areas such as finance, traffic, environment, etc. Still, concerns have been raised on traditional methods for incapable of modeling complex patterns or dependencies lying in real word data. To address such concerns, various deep learning models, mainly Recurrent Neural Network (RNN) based methods, are proposed. Nevertheless, capturing extremely long-term patterns while effectively incorporating information from other variables remains a challenge for time-series forecasting. Furthermore, lack-of-explainability remains one serious drawback for deep neural network models. Inspired by Memory Network proposed for solving the question-answering task, we propose a deep learning based model named Memory Timeseries network (MTNet) for time series forecasting. MTNet consists of a large memory component, three separate encoders, and an autoregressive component to train jointly. Addtionally, the attention mechanism designed enable MTNet to be highly interpretable. We can easily tell which part of the historic data is referenced the most.

The social media has Immense and popularity among all the services today. Data from SNS (Social Network Service) can be used for a lot of objectives such as prediction or sentiment analysis. Twitter is a SNS that has a huge data with user posting, with this significant amount of data, it has the potential of research related to text mining and could be subjected to sentiment analysis. But handling such a huge amount of unstructured data is a difficult task, machine learning is needed for handling such huge of data. Deep learning is of the machine learning method that use the deep feed forward neural network with many hidden layers in the term of neural network with the result of the experiment about 75%.

OBJECTIVE
To review the history and current applications of artificial intelligence in the intensive care unit.

DATA SOURCES
The MEDLINE database, bibliographies of selected articles, and current texts on the subject.

STUDY SELECTION
The studies that were selected for review used artificial intelligence tools for a variety of intensive care applications, including direct patient care and retrospective database analysis.

DATA EXTRACTION
All literature relevant to the topic was reviewed.

DATA SYNTHESIS
Although some of the earliest artificial intelligence (AI) applications were medically oriented, AI has not been widely accepted in medicine. Despite this, patient demographic, clinical, and billing data are increasingly available in an electronic format and therefore susceptible to analysis by intelligent software. Individual AI tools are specifically suited to different tasks, such as waveform analysis or device control.

CONCLUSIONS
The intensive care environment is particularly suited to the implementation of AI tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new AI tools have become available in recent years that can function as intelligent assistants to clinicians, constantly monitoring electronic data streams for important trends, or adjusting the settings of bedside devices. The integration of these tools into the intensive care unit can be expected to reduce costs and improve patient outcomes.

Feature extraction from financial data is one of the most important problems in market prediction domain for which many approaches have been suggested. Among other modern tools, convolutional neural networks (CNN) have recently been applied for automatic feature selection and market prediction. However, in experiments reported so far, less attention has been paid to the correlation among different markets as a possible source of information for extracting features. In this paper, we suggest a CNN-based framework with specially designed CNNs, that can be applied on a collection of data from a variety of sources, including different markets, in order to extract features for predicting the future of those markets. The suggested framework has been applied for predicting the next days direction of movement for the indices of S&P 500, NASDAQ, DJI, NYSE, and RUSSELL markets based on various sets of initial features. The evaluations show a significant improvement in predictions performance compared to the state of the art baseline algorithms.

The main objective of this study is to create a fast, easy and an efficient algorithm for disease prediction , with less error rate and can apply with even large data sets and show reasonable patterns with dependent variables. For disease identification and prediction in data mining a new hybrid algorithm was constructed. The Disease Identification and Prediction(DIP) algorithm, which is the combination of decision tree and association rule is used to predict the chances of some of the disease hits in some particular areas. It also shows the relationship between different parameters for the prediction. To implement this algorithm using vb.net software was also developed.

Big Data Analytics and Deep Learning are two high-center of information science. Big Data has become important because of companies i.e. both public and private have been gathering huge measures of domain-specific data, which can contain helpful data about issues, for example, national intelligence, cyber security, fraud detection, marketing, and medical informatics. Deep Learning algorithms extract high-level, complex reflections as information representations through a progressive learning process. A advantage of Deep Learning is the study and learning of huge size of unsupervised information, making it a significant instrument for Big Data Analytics where crude information is to a great extent unlabeled and un-sorted. The present survey gives an idea of the previous work done by several researchers on the Big Data Analytics and Deep Learning applications.

Accurate forecasting of directional changes in stock prices is important for algorithmic trading and investment management. Technical analysis has been successfully used in financial forecasting and recently researchers have explored the optimization of parameters for technical indicators. This study investigates the relationship between the window size used for calculating technical indicators and the accuracy of one-step-ahead (variable steps) forecasting. The directions of the future price movements are predicted using technical analysis and machine learning algorithms. Results show a correlation between window size and forecasting step size for the Support Vector Machines approach but not for the other approaches.


Question: Applying artificial intelligence for forecasting trends in the stock market.

This paper introduces a refined approach to discretizing continuous-valued attributes for classification learning, focusing on the entropy minimization heuristic. The primary challenge in classification learning, particularly in real-world applications, involves dealing with continuous-valued attributes. This paper delves into the effectiveness of using the entropy minimization heuristic for segmenting the range of such attributes into multiple intervals. We provide theoretical justification for the applicability of this heuristic in the binary discretization algorithm utilized by ID3, C4, CART, and other learning algorithms. The empirical findings support the extension of the algorithm to generate multiple intervals. A criterion based on the minimum description length principle is formally derived to determine the partitioning of intervals. The paper then presents empirical evidence, gathered from testing on several real-world datasets, that demonstrates the improved performance of decision trees when using the new multi-interval algorithm. This advancement is crucial for classification learning algorithms that employ heuristics to navigate the vast space of potential relations between combinations of attribute values and classes. The heuristic of selecting attributes that minimize the information entropy of classes in a dataset (as seen in ID3 and its extensions) is extended to handle continuous-valued attributes through effective discretization. The paper discusses the importance of handling both nominal and continuous attributes, with the latter requiring discretization before attribute selection. The focus on continuous attributes involves partitioning their range into subranges. The paper's contributions include a deeper understanding of the entropy minimization heuristic for binary discretization, formal evidence supporting its use, and enhanced computational efficiency that accelerates the evaluation process for continuous-valued attribute discretization. The extension to multi-interval discretization is motivated by the need for greater flexibility and is empirically validated to produce superior decision trees. This advancement is significant for improving the accuracy and efficiency of classification learning algorithms in handling complex datasets with continuous attributes.

OBJECTIVE
To review the history and current applications of artificial intelligence in the intensive care unit.

DATA SOURCES
The MEDLINE database, bibliographies of selected articles, and current texts on the subject.

STUDY SELECTION
The studies that were selected for review used artificial intelligence tools for a variety of intensive care applications, including direct patient care and retrospective database analysis.

DATA EXTRACTION
All literature relevant to the topic was reviewed.

DATA SYNTHESIS
Although some of the earliest artificial intelligence (AI) applications were medically oriented, AI has not been widely accepted in medicine. Despite this, patient demographic, clinical, and billing data are increasingly available in an electronic format and therefore susceptible to analysis by intelligent software. Individual AI tools are specifically suited to different tasks, such as waveform analysis or device control.

CONCLUSIONS
The intensive care environment is particularly suited to the implementation of AI tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new AI tools have become available in recent years that can function as intelligent assistants to clinicians, constantly monitoring electronic data streams for important trends, or adjusting the settings of bedside devices. The integration of these tools into the intensive care unit can be expected to reduce costs and improve patient outcomes.

Demand forecasting is crucial to electricity providers because their ability to produce energy exceeds their ability to store it. Excess demand can cause “brown outs,” while excess supply ends in waste. In an industry worth over $1 trillion in the U.S. alone [1], almost 9% of GDP [2], even marginal improvements can have a huge impact. Any plan toward energy efficiency should include enhanced utilization of existing production. Energy loads provide an interesting topic for Machine Learning techniques due to the availability of large datasets that exhibit fundamental nonlinear patterns. Using data from the Kaggle competition “Global Energy Forecasting Competition 2012 Load Forecasting” [3] we sought to use deep learning architectures to predict energy loads across different network grid areas, using only time and temperature data. Data included hourly demand for four and a half years from 20 different geographic regions, and similar hourly temperature readings from 11 zones. For most of our analysis we focused on short term load forecasting because this will aide online operational scheduling.

This paper surveys knowledge management (KM) development using a literature review and classification of articles from 1995 to 2002 with keyword index in order to explore how KM technologies and applications have developed in this period. Based on the scope of 234 articles of knowledge management applications, this paper surveys and classifies KM technologies using the seven categories as: KM framework, knowledge-based systems, data mining, information and communication technology, artificial intelligence/expert systems, database technology, and modeling, together with their applications for different research and problem domains. Some discussion is presented, indicating future development for knowledge management technologies and applications as the followings: (1) KM technologies tend to develop towards expert orientation, and KM applications development is a problem-oriented domain. (2) Different social studies methodologies, such as statistical method, are suggested to implement in KM as another kind of technology. (3) Integration of qualitative and quantitative methods, and integration of KM technologies studies may broaden our horizon on this subject. (4) The ability to continually change and obtain new understanding is the power of KM technologies and will be the application of future works. q 2003 Elsevier Science Ltd. All rights reserved.

Trading in financial markets is undergoing a radical transformation, one in which algorithmic methods are becoming increasingly more important. The development of intelligent agents that can act as autonomous traders seems like a logical step forward in this “algorithms arms race”. In this paper we describe an infrastructure for implementing hybrid intelligent agents with the ability to trade financial instruments without requiring human supervision. We used this infrastructure, which relies heavily on artificial intelligence models and problem solving methodologies, to implement two currency trading agents. So far these agents have been able to profit from inefficiencies in the currency market, which lead us to believe our infrastructure can be of practical interest to the investment industry.

The aim of this paper is to identify the most suitable model for churn prediction based on three different techniques. The paper identifies the variables that affect churn in reverence of customer complaints data and provides a comparative analysis of neural networks, regression trees and regression in their capabilities of predicting customer churn. Keywords—Churn, Decision Trees, Neural Networks, Regression.

The use of artificial neural network (ANN) modeling for prediction and forecasting variables in water r esources engineering are being increasing rapidly. Infrastru ctural applications of ANN in terms of selection of inputs, architectur e of networks, training algorithms, and selection of training para meters in different types of neural networks used in water resources en gin ering have been reported. ANN modeling conducted for water res ources engineering variables (river sediment and discharge ) published in high impact journals since 2002 to 2011 have been e xamined and presented in this review. ANN is a vigorous techniq ue to develop immense relationship between the input and output v ariables, and able to extract complex behavior between the water resources variables such as river sediment and discharge. It can produce robust prediction results for many of the water resources engineering problems by appropriate learning from a set of exam ples. It is important to have a good understanding of the input and output variables from a statistical analysis of the data b efore network modeling, which can facilitate to design an efficie nt network. An appropriate training based ANN model is able to ado pt the physical understanding between the variables and may generat more effective results than conventional prediction techniques. Keywords—ANN, discharge, modeling, prediction, sediment,

With the rapid growth over the past few decades, people are consuming more and more electrical energies. In order to solve the contradiction between supply and demand to minimize electricity cost, it is necessary and useful to predict the electricity demand. In this paper, we apply an improved neural network algorithm to forecast the electricity, and we test it on a collected electricity demand data set in Queensland to verify its performance. There are two contributions in this paper. Firstly, comparing with backpropagation (BP) neural network, the results show a better performance on this improved neural network. Secondly, the performance on various hidden layers shows that different dimension of hidden layer in this improved neural network has little impact on the Queensland's electricity demand forecasting.

Keywords in the ACM Digital Library and IEEE Xplore digital library and in NSF grants anticipate future CS research.

In this paper, we examine the use of GARCH models, Neural Network Regression (NNR), Recurrent Neural Network (RNN) regression and model combinations for forecasting and trading currency volatility, with an application to the GBP/USD and USD/JPY exchange rates. Both the results of the NNR/RNN models and the model combination results are benchmarked against the simpler GARCH alternative. The idea of developing a nonlinear nonparametric approach to forecast FX volatility, identify mispriced options and subsequently develop a trading strategy based upon this process is intuitively appealing. Using daily data from December 1993 through April 1999, we develop alternative FX volatility forecasting models. These models are then tested out-of-sample over the period April 1999-May 2000, not only in terms of forecasting accuracy, but also in terms of trading efficiency: In order to do so, we apply a realistic volatility trading strategy using FX option straddles once mispriced options have been identified. Allowing for transaction costs, most trading strategies retained produce positive returns. RNN models appear as the best single modelling approach yet, somewhat surprisingly, model combination which has the best overall performance in terms of forecasting accuracy, fails to improve the RNN-based volatility trading results. Another conclusion from our results is that, for the period and currencies considered, the currency option market was inefficient and/or the pricing formulae applied by market participants were inadequate. * Christian Dunis is Girobank Professor of Banking and Finance at Liverpool Business School and Director of CIBEF (E-mail: cdunis@globalnet.co.uk). The opinions expressed herein are not those of Girobank. ** Xuehuan Huang is an Associate Researcher with CIBEF. *** CIBEF – Centre for International Banking, Economics and Finance, JMU, John Foster Building, 98 Mount Pleasant, Liverpool L3 5UZ. We are grateful to Professor Ken Holden and Professor John Thompson of Liverpool Business School for helpful comments on an earlier version of this paper.

The development of high frequency data bases allows for empirical investigations of a wide range of issues in the financial markets. In this paper, we set out some of the many important issues connected with the use, analysis, and application of high-frequency data sets. These include the effects of market structure on the availability and interpretation of the data, methodological issues such as the treatment of time, the effects of intra-day seasonals, and the effects of time-varying volatility, and the information content of various market data. We also address using high frequency data to determine the linkages between markets and to determine the applicability of temporal trading rules. The paper concludes with a discussion of the issues for future research. © 1997 Elsevier Science B.V. JEL classification: Cl0; C50; F30; G14; GI5


Question: Designing an advanced digital heartbeat monitor utilizing fundamental electronic components.

This paper introduces the conception and realization of an innovative heart rate measuring apparatus utilizing the fingertip, aimed at enhancing the accuracy and affordability of heart rate monitoring. With the rising prevalence of heart diseases, the demand for reliable and cost-effective heart rate monitoring solutions has grown significantly. Many existing heart rate measurement tools and environments are expensive and lack ergonomic design. Our proposed Heart Rate Monitor (HRM) device stands out for its economic viability and user-centric design, employing optical technology to track blood flow in the index finger. The device's operation is divided into three stages: pulse detection, signal extraction, and pulse amplification. Rigorous qualitative and quantitative assessments on real-life signals reveal the device's high accuracy in heart rate estimation, even during strenuous physical activities. To validate its performance, the HRM device was compared against Electrocardiogram (ECG) readings and manual heartbeat counts from 90 individuals across various age groups. The findings indicate that the device's margin of error is remarkably low, underscoring its potential as a dependable heart rate monitoring tool.

A low cost pulse transceiver has been developed for measuring the electrical properties of materials. The transceiver generates an ultra wide band (UWB) pulse as well as samples the received pulse. The pulse generator has been designed using a silicon-germanium (SiGe) analog comparator which is a possible alternative to step recovery diodes. The pulse is received by an extended time sampling circuit. The sampling circuit presented is an alternative design that does not require the use of a broadband balun.

Patient tracking is an important component toward creating smart cities. In this demo we use Bluetooth Low Energy beacons and single board computers to track patients in the emerging field of smart care facilities. Our model utilizes a fixed scanner moving transmitter method for wireless tracking of patients through the facility. The data collected by all scanners is stored within a central database that is designed to be efficiently queried. We show how inexpensive components in conjunction with free open source software can be used to implement a patient tracking system. We focus on the pipeline between acquisition and display of the location data. Additionally, we also discuss the manipulation of the data required for usability, and optional filtering operations that improve accuracy.

With recent advances in technology, various wearable sensors have been developed for the monitoring of human physiological parameters. A Body Sensor Network (BSN) consisting of such physiological and biomedical sensor nodes placed on, near or within a human body can be used for real-time health monitoring. In this paper, we describe an on-going effort to develop a system consisting of interconnected BSNs for real-time health monitoring of soldiers. We discuss the background and an application scenario for this project. We describe the preliminary prototype of the system and present a blast source localization application.

Cardiac telemedicine systems facilitate treatment of patients by sending blood pressure and cardiac performance information to hospitals and medical emergency care units. Location-based services can be used in mobile cardiac telemedicine systems to improve efficiency, robustness and accuracy. This paper proposes a combination of mobile telemedicine systems and location-based services that will enable hospitals and emergency departments to do continuous monitoring of a patient’s heart activity; such a system would decrease the probability of critical condition patients in emergency cases and increase the chance of saving lives. In order to evaluate whether this kind of health care system might work, we explore the success factors for such a system by adapting the DeLone & McLean IS success model to the context of location-based mobile system in cardiac healthcare. After reviewing previous works, we identify fourteen factors which can affect the success of a location-based mobile cardiac telemedicine system: Realization of user expectations, Portability, Accessibility, Extent of data processing, Time response, Urgency, Currency, Sufficiency, Understandability, Reliability, Extent of Mobility (spatial, temporal, contextual), System support, Responsiveness, and Assurance. We apply these factors to propose a success model for our location-based mobile cardiac telemedicine system.

An ultra-low power, ultra portable, wearable integrated bio-medical device, the Non-Invasive Cuff-less Blood Pressure Monitor (NICBPM) is proposed to measure the Pulse Transit Time (PTT). The relationship between PTT and Blood Pressure (BP) is investigated using linear and non linear regression models. Simultaneous reading obtained from an FDA approved, commercially available Blood Pressure monitor and the (Electrocardiogram) ECG and (photoplethsymogram) PPG signal from the NICBPM are used to calibrate the device for 30 subjects during cardiopulmonary exercise test. Bland-Altman plots for the linear regression model revealed limits of agreement of −10.6 to 10.6 mmHg for systolic BP (sBP) and −10.5 to 10.5 mmHg for distolic BP (dBP). Bland-Altman plots for the non linear regression model revealed marginally better limits of agreement between −8.5 to 8.5 mmHg for sBP and −6.6 to 6.6 mmHg for dBP. sBP can be estimated from the NICBPM with 99% accuracy and 99% sensitivity.

We present a prototype design of dual element photoplethysmograph (PPG) probe along with associated measurement system for carotid local pulse wave velocity (PWV) evaluation in a non-invasive and continuous manner. The PPG probe consists of two identical sensing modules placed 23 mm apart. Simultaneously measured blood pulse waveforms from these arterial sites were processed and the pulse transit time delay was resolved using the developed application-specific software. The ability of developed PPG probe and associated measurement system to detect acute changes in carotid local PWV due to blood pressure (BP) variations was experimentally validated by an in-vivo study. Intra-subject carotid BP elevation was achieved by an upper arm cuff based occlusion, which offered a controlled way of local PWV escalation. The elevated carotid BP values were also recorded by a calibrated pressure tonometer prior to the study, and was used as a reference. A significant increment (1.0 – 2.6 m/s) in local PWV was observed and was proportional to the BP increment induced by the occlusive reactive hyperemia. Study results demonstrated the feasibility of real-time signal acquisition and reliable local PWV evaluation under normal and elevated BP conditions using the developed measurement system.

Advances in wireless technology and supporting infrastructure provide unprecedented opportunity for ubiquitous real-time healthcare and fitness monitoring without constraining the activities of the user. Wirelessly connected miniaturized sensors and actuators placed in, on, and around the body form a body area network for continuous, automated, and unobtrusive monitoring of physiological signs to support medical, lifestyle and entertainment applications. BAN technology is in the early stage of development, and several research challenges have to be overcome for it to be widely accepted. In this article we study the core set of application, functional, and technical requirements of the BAN. We also discuss fundamental research challenges such as scalability (in terms of data rate, power consumption, and duty cycle), antenna design, interference mitigation, coexistence, QoS, reliability, security, privacy, and energy efficiency. Several candidate technologies poised to address the emerging BAN market are evaluated, and their merits and demerits are highlighted. A brief overview of standardization activities relevant to BANs is also presented.

This paper describes an investigation into how real-time but low-cost biometric information can be interpreted by computer games to enhance gameplay without fundamentally changing it. We adapted a cheap sensor, (the Lightstone mediation sensor device by Wild Divine), to record and transfer biometric information about the player (via sensors that clip over their fingers) into a commercial game engine, Half-Life 2. During game play, the computer game was dynamically modified by the player’s biometric information to increase the cinematically augmented “horror” affordances. These included dynamic changes in the game shaders, screen shake, and the creation of new spawning points for the game’s non-playing characters (zombies), all these features were driven by the player’s biometric data. To evaluate the usefulness of this biofeedback device, we compared it against a control group of players who also had sensors clipped on their fingers, but for the second group the gameplay was not modified by the biometric information of the players. While the evaluation results indicate biometric data can improve the situated feeling of horror, there are many design issues that will need to be investigated by future research, and the judicious selection of theme and appropriate interaction is vital. Author

Advanced 0.16 μm BCD technology platform offering dense logic transistors (1.8 V-5 V CMOS) and high performance analog features has been developed. Thanks to dedicated field plate optimization, body and drain engineering, state of the art power devices (8 V to 42 V rated) have been obtained ensuring large Safe Operating Areas with best RONXAREA-BVDSS tradeoff.

The system design of an integrated microwave imaging radar for the diagnostic screening of breasts cancer is presented. A custom integrated circuit implemented in a 65-nm CMOS technology and a pair of patch antennas realized on a planar laminate are proposed as the basic module of the imaging antenna array. The radar operates on the broad frequency range from 2 to 16 GHz with a dynamic range of 107 dB. Imaging experiments carried out on a realistic breast phantom show that the system is capable of detecting tumor targets with a resolution of 3 mm.


Question: Designing an advanced digital heartbeat monitor utilizing fundamental electronic components.

A Digital Heartbeat Monitor Design Using Basic Electronic Components: An Innovative Approach

Innovations in health technology have significantly advanced the monitoring and maintenance of patient well-being, particularly in the critical area of heart rate monitoring. Heart rate, a fundamental health indicator, directly correlates with the condition of the cardiovascular system and can reflect various physiological states such as physical exertion, stress, focus, and the activity level of the autonomic nervous system. This paper outlines a novel method for measuring heart rate through a fingertip using Arduino and Processing software, employing the principle of photoplethysmography (PPG). PPG is a non-invasive technique that detects changes in blood volume in tissues by illuminating the skin and measuring the reflected light.

During heartbeats, the volume of blood within the finger's arteries fluctuates, a phenomenon detectable through an optical sensor placed around the fingertip. The sensor comprises an infrared light-emitting diode (IR LED) and a photodiode. The IR LED emits infrared light into the fingertip, part of which is absorbed and part reflected by the blood in the arteries. The photodiode captures the reflected light, the intensity of which varies with the blood volume. This variation, amplified by a high-gain amplifier, is converted into a detectable pulse, which is then processed by Arduino through serial port communication. The Processing software facilitates the monitoring and counting of heartbeats.

This approach offers a simple yet effective method for heart rate monitoring using basic electronic components, demonstrating the potential for advanced digital health monitoring systems that can be easily implemented and maintained.

Noninvasive continuous blood pressure (BP) monitoring is not yet practically available for daily use. Challenges include making the system easily wearable, reducing noise level and improving accuracy. Variations in each person's physical characteristics, as well as the possibility of different postures, increase the complexity of continuous BP monitoring, especially outside the hospital. This study attempts to provide an easily wearable solution and proposes training to specific posture and individual for further improving accuracy. The wrist watch-based system we developed can measure electrocardiogram and photoplethysmogram. From these two signals, we measure pulse transit time through which we can obtain systolic and diastolic blood pressure through regression techniques. In this study, we investigate various functions to perform the training to obtain blood pressure. We validate measurements on different postures and subjects, and show the value of training the device to each posture and each subject. We observed that the average RMSE between the measured actual systolic BP and calculated systolic BP is between 7.83 to 9.37 mmHg across 11 subjects. The corresponding range of error for diastolic BP is 5.77 to 6.90 mmHg. The system can also automatically detect the arm position of the user using an accelerometer with an average accuracy of 98%, to make sure that the sensor is kept at the proper height. This system, called BioWatch, can potentially be a unified solution for heart rate, SPO2 and continuous BP monitoring.

One of the most important challenges in chronic disease self-management is medication non-adherence, which has irrevocable outcomes. Although many technologies have been developed for medication adherence monitoring, the reliability and cost-effectiveness of these approaches are not well understood to date. This paper presents a medication adherence monitoring system by user-activity tracking based on wrist-band wearable sensors. We develop machine learning algorithms that track wrist motions in real-time and identify medication intake activities. We propose a novel data analysis pipeline to reliably detect medication adherence by examining single-wrist motions. Our system achieves an accuracy of 78.3% in adherence detection without need for medication pillboxes and with only one sensor worn on either of the wrists. The accuracy of our algorithm is only 7.9% lower than a system with two sensors that track motions of both wrists.

BACKGROUND
One of the main problems especially in operating room and monitoring devices is measurement of Blood Pressure (BP) by sphygmomanometer cuff. Objective :In this study we designed a new method to measure BP changes continuously for detecting information between cuff inflation times by using vital signals in monitoring devices. This will be achieved by extraction of the time difference between each cardiac cycle and a relative pulse wave.

METHODS
Finger pulse and ECG signals in lead I were recorded by a monitoring device. The output of monitoring device wasinserted in a computer by serial network communication. A software interface (Microsoft Visual C#.NET ) was used to display and process the signals in the computer. Time difference between each cardiac cycle and pulse signal was calculated throughout R wave detection in ECG and peak of pulse signal by the software. The relation between time difference in two waves and BP was determined then the coefficients of equation were obtained in different physical situations. The results of estimating BP were compared with the results of sphygmomanometer method and the error rate was calculated.

RESULTS
In this study, 25 subjects participated among them 15 were male and 10 were female. The results showed that BP was linearly related to time difference. Average of coefficient correlation was 0.9±0.03 for systolic and 0.82±0.04 for diastolic blood pressure. The highest error percentage was calculated 8% for male and 11% for female group. Significant difference was observed between the different physical situation and arm movement changes. The relationship between time difference and age was estimated in a linear relationship with a correlation coefficient of 0.76.

CONCLUSION
By determining linear relation values with high accuracy, BP can be measured with insignificant error. Therefore it can be suggested as a new method to measure the blood pressure continuously.

The paper presents a novel Driving Right Leg (DgRL) circuit designed to mitigate the effect of common mode signals deriving, say, from power line interferences. The DgRL drives the isolated ground of the instrumentation towards a voltage which is fixed with respect to the common mode potential on the subject, therefore minimizing common mode voltage at the input of the front-end. The paper provides an analytical derivation of the common mode rejection performances of DgRL as compared to the usual grounding circuit or Driven Right Leg (DRL) loop. DgRL is integrated in a bio-potential acquisition system to show how it can reduce the common mode signal of more than 70 dB with respect to standard patient grounding. This value is at least 30 dB higher than the reduction achievable with DRL, making DgRL suitable for single-ended front-ends, like those based on active electrodes. EEG signal acquisition is performed to show how the system can successfully cancel power line interference without any need for differential acquisition, signal post-processing or filtering.

Physiological sensors are gaining the attention of manufacturers and users. As denoted by devices such as smartwatches or the newly released Kinect 2 – which can covertly measure heartbeats – or by the popularity of smartphone apps that track heart rate during fitness activities. Soon, physiological monitoring could become widely accessible and transparent to users. We demonstrate how one could take advantage of this situation to increase users’ engagement and enhance user experience in human-agent interaction. We created an experimental protocol involving embodied agents – “virtual avatars”. Those agents were displayed alongside a beating heart. We compared a condition in which this feedback was simply duplicating the heart rates of users to another condition in which it was set to an average heart rate. Results suggest a superior social presence of agents when they display feedback similar to users’ internal state. This physiological “similarity-attraction” effect may lead, with little effort, to a better acceptance of agents and robots by the general public.

Wireless, remote patient monitoring system and control using feedback and GSM technology is used to monitor the different parameters of an ICU patient remotely and also control over medicine dosage is provided. Measurement of vital parameters can be done remotely and under risk developing situation can be conveyed to the physician with alarm triggering systems in order to initiate the proper control actions. In the implemented system a reliable and efficient real time remote patient monitoring system that can play a vital role in providing better patient care is developed. This system enables expert doctors to monitor vital parameters viz body temperature, blood pressure and heart rate of patients in remote areas of hospital as well as he can monitor the patient when he is out of the premises. The system in addition also provides a feedback to control the dosage of medicine to the patient as guided by the doctor remotely, in response to the health condition message received by the doctor. Mobile phones transfer measured parameters via SMS to clinicians for further analysis or diagnosis. The timely manner of conveying the real time monitored parameter to the doctor and control action taken by him is given high priority which is very much needed and which is the uniqueness of the developed system. The system even facilitates the doctor to monitor the patient's previous history from the data in memory inbuilt in the monitoring device. Also data can be sent to several doctors incase a doctor fails to respond urgently.

Electrically non-contact ECG measurement system on a chair can be applied to a number of various fields for continuous health monitoring in daily life. However, the body is floated electrically for this system due to the capacitive electrodes and the floated body is very sensitive to the external noises or motion artifacts which affect the measurement system as the common mode noise. In this paper, the Driven-Seat-Ground circuit similar to the Driven-Right-Leg circuit is proposed to reduce the common mode noise. The analysis of this equivalent circuit is performed and the output signal waveforms are compared between with Driven-Seat-Ground and with capacitive ground. As the results, the Driven-Seat-Ground circuit improves significantly the properties of the fully capacitive ECG measurement system as the negative feedback.

Patient biopotentials are usually measured with conventional disposable Ag/AgCl electrodes. These electrodes provide excellent signal quality but are irritating for long-term use. Skin preparation is usually required prior to the application of electrodes such as shaving and cleansing with alcohol. To overcome these difficulties, researchers and caregivers seek alternative electrodes that would be acceptable in clinical and research environments. Dry electrodes that operate without gel, adhesive or even skin preparation have been studied for many decades. They are used in research applications, but they have yet to achieve acceptance for medical use. So far, a complete comparison and evaluation of dry electrodes is not well described in the literature. This work compares dry electrodes for biomedical use and physiological research, and reviews some novel systems developed for cardiac monitoring. Lastly, the paper provides suggestions to develop a dry-electrode-based system for mobile and long-term cardiac monitoring applications.

Arterial stiffness index is one of the biomechanical indices of vascular healthiness. These indexes are based on detailed pulse waveform analysis which is presented here. After photoplethysmographyic (PPG) pulse wave measurement, we decompose the pulse waveform for the estimation and determination of arterial elasticity. Firstly, it is electro-optically measured PPG signal and by electromechanical film (EMFi) measured signal that are analyzed and investigated by dividing each wave into five logarithmic normal function components. For both the PPG and EMFi waveform we can find very easily a good fit between the original and overlapped and summed wave components. Each wave component is assumed to resemble certain phenomenon in the arteries and certain indexes can be calculated for example based on the mutual timing of the components. Several studies have demonstrated that these kinds of indexes calculated based on actual biomechanical processed can predict future cardiovascular events. Many dynamic factors, e.g., arterial stiffness, depend on fixed structural features of the arterial wall. For more accurate description, arterial stiffness is estimated based on pulse wave decomposition analysis in the radial measured by EMFi and PPG method and tibial arterial walls measured by PPG method parallelly. Elucidation of the precise relationship between endothelial function and arterial stiffness can be done through biomechanics. However, arterial wall elasticity awaits still further biomechanical studies with clinical relations and the influence of arterial flexibility, resistance and ageing inside of the radial pulse waveform.

Pulse oximetry has been extensively used to estimate oxygen saturation in blood, a vital physiological parameter commonly used when monitoring a subject's health status. However, accurate estimation of this parameter is difficult to achieve when the fundamental signal from which it is derived, the photoplethysmograph (PPG), is contaminated with noise artifact induced by movement of the subject or the measurement apparatus. This study presents a novel method for automatic rejection of artifact contaminated pulse oximetry waveforms, based on waveform morphology analysis. The performance of the proposed algorithm is compared to a manually annotated gold standard. The creation of the gold standard involved two experts identifying sections of the PPG signal containing good quality PPG pulses and/or noise, in 104 fingertip PPG signals, using a simultaneous electrocardiograph (ECG) signal as a reference signal. The fingertip PPG signals were each 1 min in duration and were acquired from 13 healthy subjects (10 males and 3 females). Each signal contained approximately 20 s of purposely induced artifact noise from a variety of activities involving subject movement. Some unique waveform morphology features were extracted from the PPG signals, which were believed to be correlated with signal quality. A simple decision-tree classifier was employed to arrive at a classification decision, at a pulse-by-pulse resolution, of whether a pulse was of acceptable quality for use or not. The performance of the algorithm was assessed using Cohen's kappa coefficient (κ), sensitivity, specificity and accuracy measures. A mean κ of 0.64 ± 0.22 was obtained, while the mean sensitivity, specificity and accuracy were 89 ± 10%, 77 ± 19% and 83 ± 11%, respectively. Furthermore, a heart rate estimate, extracted from uncontaminated sections of PPG, as identified by the algorithm, was compared with the heart rate derived from an uncontaminated simultaneous ECG signal. The mean error between both heart rate readings was 0.49 ± 0.66 beats per minute (BPM), in comparison to an error value observed without using the artifact detection algorithm of 7.23 ± 5.78 BPM. These results demonstrate that automated identification of signal artifact in the PPG signal through waveform morphology analysis is achievable. In addition, a clear improvement in the accuracy of the derived heart rate is also evident when such methods are employed.


Question: Designing an advanced digital heartbeat monitor utilizing fundamental electronic components.

This paper presents an innovative design for a Microcontroller-Based Wireless Heartbeat and Temperature Monitoring System. The system is centered around a versatile sensor platform, known as the Wireless Sensor Node (WSN), which boasts remote accessibility. The WSN is designed to standardize sensor nodes, akin to a system-on-module, and unify software, aiming for a modular and adaptable solution for monitoring various vital signs. The architecture of the sensor node is flexible, allowing for easy customization to collect and transmit different types of vital parameters. A prototype has been developed, utilizing a wireless communication channel based on the IEEE 802.15.4 standard for wireless local area networks. This enables the remote viewing of sensor data, such as heartbeat and temperature, and provides the capability to adjust vital parameters according to specific needs. The system's design and implementation demonstrate its potential for effective and efficient patient monitoring in various healthcare settings.

An overview of the key challenges facing the practice of medicine today is presented along with the need for technological solutions that can "prevent" problems. Then, the development of the Wearable Motherboard/spl trade/ (Smart Shirt) as a platform for sensors and monitoring devices that can unobtrusively monitor the health and well being of individuals (directly and/or remotely) is described. This is followed by a discussion of the applications and impact of this technology in the continuum of life-from preventing SIDS to facilitating independent living for senior citizens. Finally, the future advancements in the area of wearable, yet comfortable, systems that can continue the transformation of healthcare - all aimed at enhancing the quality of life for humans - are presented.

Current comparator is a fundamental component of current-mode analog integrated circuits. A novel high-performance continuous-time CMOS current comparator is proposed in this paper, which comprises one CMOS complementary amplifier, two resistive-load amplifiers and two CMOS inverters. A MOS resistor is used as the CMOS complementary amplifier’s negative feedback. Because the voltage swings of the CMOS complementary amplifier are reduced by low input and output resistances, the delay time of the current comparator is shortened. Its power consumption can be reduced rapidly with the increase of input current. Simulation results based on 1.2 μm CMOS process model show the speed of the novel current comparator is comparable with those of the existing fastest CMOS current comparators, and its power consumption is the lowest, so it has the smallest power-delay product. Furthermore, the new current comparator occupies small area and is process-robust, so it is very suitable to high-speed and low-power applications.

Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted.

This book provides, for the first time, a broad and deep treatment of the fields of both ultra low power electronics and bioelectronics. It discusses fundamental principles and circuits for ultra low power electronic design and their applications in biomedical systems. It also discusses how ultra energy-efficient cellular and neural systems in biology can inspire revolutionary low power architectures in mixed-signal and RF electronics. The book presents a unique, unifying view of ultra low power analog and digital electronics and emphasizes the use of the ultra energy-efficient subthreshold regime of transistor operation in both. Chapters on batteries, energy harvesting, and the future of energy provide an understanding of fundamental relationships between energy use and energy generation at small scales and at large scales. A wealth of insights and examples from brain implants, cochlear implants, biomolecular sensing, cardiac devices, and bio-inspired systems make the book useful and engaging for students and practicing engineers.

An integrated CMOS amperometric instrument with on-chip electrodes and packaging for biosensor arrays is presented. The mixed-signal integrated circuit supports a variety of electrochemical measurement techniques including linear sweep, constant potential, cyclic and pulse voltammetry. Implemented in 0.5 μm CMOS, the 3 × mm2 chip dissipates 22.5 mW for a 200 kHz clock. The highly programmable chip provides a wide range of user-controlled stimulus rate and amplitude settings with a maximum scan range of 2 V and scan rates between 1 mV/sec and 400 V/sec. The amperometric readout circuit provides ±500 fA linear resolution and supports inputs up to ±47 μA. A 2 × 2 gold electrode array was fabricated on the surface of the CMOS instrumentation chip. An all-parylene packaging scheme was developed for compatibility with liquid test environments as well as a harsh piranha electrode cleaning process. The chip was tested using cyclic voltammetry of different concentrations of potassium ferricyanide at 100 mV/s and 200 mV/s, and results were identical to measurements using commercial instruments.

Interacting with smartwatches poses new challenges. Although capable of displaying complex content, their extremely small screens poorly match many of the touchscreen interaction techniques dominant on larger mobile devices. Addressing this problem, this paper presents beating gestures, a novel form of input based on pairs of simultaneous or rapidly sequential and overlapping screen taps made by the index and middle finger of one hand. Distinguished simply by their temporal sequence and relative left/right position these gestures are designed explicitly for the very small screens (approx. 40mm square) of smartwatches and to operate without interfering with regular single touch input. This paper presents the design of beating gestures and a rigorous empirical study that characterizes how users perform them -- in a mean of 355ms and with an error rate of 5.5%. We also derive thresholds for reliably distinguishing between simultaneous (under 30ms) and sequential (under 400ms) pairs of screen touches or releases. We then present five interface designs and evaluate them in a qualitative study in which users report valuing the speed and ready availability of beating gestures.

To reduce elderly falling risk in the area where video surveillance system is unavailable due to privacy reason and so on, we need a monitor system which does no recognize what they are doing. This paper presents an elderly-falling detection system using ultrasonic sensors. The ultrasonic technology-based multi sensors are couple of receiver and transmitter together, which are connected to Arduino microcontroller in order to send the elderly person's fall related signal using WiFi to the processing unit. The sensors are positioned as an array on the roof and wall in the room. The signal is analyzed to recognize human by sensing distance, and detect the action such as standing, sitting, and falling by pattern matching with the standard templates of top and side signal. In experiments, the proposed system can recognize accuracy of falling detection is 93% approximately.

A microcontroller based advanced technique of generating sine wave with minimized harmonics is implemented in this paper. The proposed technique “impulse-sine product” aims to design and implement a voltage regulated inverter with ripple free and glitch free output sine wave that can operate electronic devices efficiently. The output voltage of the inverter is regulated by a feedback loop using analog to digital protocol of PIC16f877 microcontroller. The design is essentially focused upon low power electronic appliances such as personal computers, chargers, television sets. The inverter output is regulated from 16-volt rms to 220-volt rms for a variation of load between 1-Ω to 180-Ω loads. The designed inverter provides a range of operation from 25 Watts to 250 Watts. The design is mathematically modeled which is simulated in Matlab, Proteus and finally the results are practically verified.

In this work, we present a novel ultra-low power timer designed using the gate leakage of MOS capacitors. The test chip was fabricated in a 0.13 mum CMOS technology and the total circuit area is 480 mum2. Measurement results show that the circuit functions correctly at a wide range of supply voltages from 300 mV to 1.2 V, making it particularly suitable for subthreshold systems. The temperature sensitivity is 0.16%/degC at 600 mV and 0.6%/degC at 300 mV. The power dissipation is less than 1pW running at 20degC and 300 mV.

An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.


Question: Designing an advanced digital heartbeat monitor utilizing fundamental electronic components.

Innovative Design of a Portable Bluetooth EKG Sensor for Real-Time Heart Monitoring
As cellular network technology advances with 2G and 3G capabilities, mobile devices can transmit data in real-time to monitoring centers, making them ideal for medical applications. This paper presents the development and assessment of a portable Bluetooth electrocardiogram (EKG) sensor designed to send critical medical data to a smartphone. The data is visualized and recorded on the phone, with plans to extend the system's functionality by transmitting this information via a cellular General Packet Radio Service (GPRS) network. The current system offers a cost-effective and lightweight alternative to traditional EKG event monitors. Upon completion, the GPRS-enabled system will facilitate continuous heart monitoring for patients within cellular coverage areas, revolutionizing remote patient monitoring capabilities.

Relaxation oscillators have a profound scope as on-chip reference clock sources or sensor front-ends in comparison to ring oscillators due to their superior frequency stability, control linearity, and wide tuning range. However, despite a better fundamental limit predicted in theory, the phase noise performance of relaxation oscillators trails behind that of ring oscillators. Furthermore, there is a huge gap between the maximum achievable 1/f2 phase noise FOM (169dBc/Hz) [1] and those achieved by recently proposed low-power relaxation oscillator implementations [1-4].

A novel ultra low power temperature sensor for UHF RFID tag chip is presented. The sensor consists of a constant pulse generator, a temperature related oscillator, a counter and a bias. Conversion of temperature to digital output is fulfilled by counting the number of the clocks of the temperature related oscillator in a constant pulse period. The sensor uses time domain comparing, where high power consumption bandgap voltage references and traditional ADCs arc not needed. The sensor is realized in a standard 0.18 mum CMOS process, and the area is only 0.2 mm2. The accuracy of the temperature sensor is plusmn1degC after calibration. The power consumption of the sensor is only 0.9 muW.

We present a prototype design of dual element photoplethysmograph (PPG) probe along with associated measurement system for carotid local pulse wave velocity (PWV) evaluation in a non-invasive and continuous manner. The PPG probe consists of two identical sensing modules placed 23 mm apart. Simultaneously measured blood pulse waveforms from these arterial sites were processed and the pulse transit time delay was resolved using the developed application-specific software. The ability of developed PPG probe and associated measurement system to detect acute changes in carotid local PWV due to blood pressure (BP) variations was experimentally validated by an in-vivo study. Intra-subject carotid BP elevation was achieved by an upper arm cuff based occlusion, which offered a controlled way of local PWV escalation. The elevated carotid BP values were also recorded by a calibrated pressure tonometer prior to the study, and was used as a reference. A significant increment (1.0 – 2.6 m/s) in local PWV was observed and was proportional to the BP increment induced by the occlusive reactive hyperemia. Study results demonstrated the feasibility of real-time signal acquisition and reliable local PWV evaluation under normal and elevated BP conditions using the developed measurement system.

Photoplethysmography relies on characteristic changes in the optical absorption of tissue due to pulsatile (arterial) blood flow in peripheral vasculature. Sensors for observing the photoplethysmographic effect have traditionally required contact with the skin surface. Recent advances in non-contact imaging photoplethysmography have demonstrated that measures of cardiopulmonary system state, such as pulse rate, pulse rate variability, and respiration rate, can be obtained from a participant by imaging their face under relatively motionless conditions. A critical limitation in this method that must be resolved is the inability to recover these measures under conditions of head motion artifact. To investigate the adequacy of channel space dimensionality for the use of blind source separation in this context, nine synchronized, visible spectrum imagers positioned in a semicircular array centered on the imaged participant were used for data acquisition in a controlled lighting environment. Three-lead electrocardiogram and finger-tip reflectance photoplethysmogram were also recorded as ground truth signals. Controlled head motion artifact trial conditions were compared to trials in which the participant remained stationary, with and without the aid of a chinrest. Bootstrapped means of one-minute, non-overlapping trial segments show that, for situations involving little to no head motion, a single imager is sufficient for recovering pulse rate with an average absolute error of less than two beats per minute. However, error in the recovered pulse rate measurement for the single imager can be as high as twenty-two beats per minute when head motion artifact is severe. This increase in measurement error during motion artifact was mitigated by increasing the dimensionality of the imager channel space with multiple imagers in the array prior to applying blind source separation. In contrast to single-imager results, the multi-imager channel space resulted in an absolute error in the recovered pulse rate measurement that is comparable with pulse rate measured via fingertip reflectance photoplethysmography. These results demonstrate that non-contact, imaging photoplethysmography can be accurate in the presence of head motion artifact when a multi-imager array is implemented to increase the dimensionality of the decomposed channel space.

A micro-power circuit is encapsulated with a 2.1 MHz ZT-cut quartz in a vacuum package. The oscillator core has 2 complementary active MOSFETS and amplitude stabilization. New coupling and biasing circuits, and dynamic frequency dividers allow to achieve ±2 ppm frequency stability down to 1.8 V with a current under 0.5 ¿A.

Technological advances on the development of mobile devices, medical sensors, and wireless communication systems support a new generation of unobtrusive, portable, and ubiquitous health monitoring systems for continuous patient assessment and more personalized health care. There exist a growing number of mobile apps in the health domain; however, little contribution has been specifically provided, so far, to operate this kind of apps with wearable physiological sensors. The PhysioDroid, presented in this paper, provides a personalized means to remotely monitor and evaluate users' conditions. The PhysioDroid system provides ubiquitous and continuous vital signs analysis, such as electrocardiogram, heart rate, respiration rate, skin temperature, and body motion, intended to help empower patients and improve clinical understanding. The PhysioDroid is composed of a wearable monitoring device and an Android app providing gathering, storage, and processing features for the physiological sensor data. The versatility of the developed app allows its use for both average users and specialists, and the reduced cost of the PhysioDroid puts it at the reach of most people. Two exemplary use cases for health assessment and sports training are presented to illustrate the capabilities of the PhysioDroid. Next technical steps include generalization to other mobile platforms and health monitoring devices.

The convergence of science and technology in our dynamic digital era has resulted in the development of innovative digital health devices that allow easy and accurate characterization in health and disease. Technological advancements and the miniaturization of diagnostic instruments to modern smartphone-connected and mobile health (mHealth) devices such as the iECG, handheld ultrasound, and lab-on-a-chip technologies have led to increasing enthusiasm for patient care with promises to decrease healthcare costs and to improve outcomes. This 'hype' for mHealth has recently intersected with the 'real world' and is providing important insights into how patients and practitioners are utilizing digital health technologies. It is also raising important questions regarding the evidence supporting widespread device use. In this state-of-the-art review, we assess the current literature of mHealth and aim to provide a framework for the advances in mHealth by understanding the various device, patient, and clinical factors as they relate to digital health from device designs and patient engagement, to clinical workflow and device regulation. We also outline new strategies for generation and analysis of mHealth data at the individual and population-based levels.

This brief evaluates the effect of conducted electromagnetic interference (EMI) that is injected in the power supply of a classic Kuijk bandgap reference voltage circuit. Two modified Kuijk bandgap topologies with high immunity to EMI are introduced and compared to the original structure. Measurements of a test IC confirm the theoretical analyses.

This article provides an overview of body area sensor networks (BASNs), an application of wireless technology that changed health care to suit the comfort of the population. There are various applications of BASNs and attempts have been made to make the human body a channel for wireless communication. BASNs employ a three-tiered architectural system that requires various technical requirements for its optimal and efficient operation. Energy consumption is one of the major issues that is currently being addressed through self-harvesting and many other techniques. Even with the benefits at hand, there are various issues such as interference and eavesdropping that BASNs have to tackle. Biometrics is a widely used solution. Researchers are also working on various ambitious projects that deal with improving deep brain simulation, heart regulation, drug delivery, and prosthetic actuation to use BASN effectively.

A new method for pulse oximetry is presented that possesses an inherent insensitivity to corruption by motion artifact, a primary limitation in the practical accuracy and clinical applicability of current technology. Artifact corruption of the underlying photoplethysmographic signals is reduced in real time, using an electronic processing methodology that is based upon inversion of a physical artifact model. This fundamental approach has the potential to provide uninterrupted output and superior accuracy under conditions of sustained subject motion, therefore, widening the clinical scope of this useful measurement. A new calibration technique for oxygen saturation is developed for use with these processed signals, which is shown to be a generalization of the classical Interpretation. The detailed theoretical and practical issues of Implementation are then explored, highlighting important engineering simplifications implicit in this new approach. A quantitative investigation of the degree of insensitivity to artifact is also undertaken, with the aid of a custom electronic system and commercial pulse oximeter probes, which is compared and contrasted with the performance of a conventional implementation. It is demonstrated that this new methodology results in a reduced sensitivity to common classes of motion artifact, while retaining the generality to be combined with conventional signal processing techniques.


Question: Designing an advanced digital heartbeat monitor utilizing fundamental electronic components.

This document explores the innovative use of augmented reality (AR) in enhancing medical education, particularly focusing on the human heart's anatomy. An online application has been developed to leverage AR technology, aiming to enrich users' understanding of the heart's complex structure. The evaluation process consists of two phases. Initially, a single investigator, guided by an expert, assesses the viability of a three-dimensional heart model within the application. Subsequently, the cognitive walkthrough method is employed to identify any usability issues. Three medical students, with no prior experience, are tasked to perform specific activities within the application. Their performance is analyzed through a set of standard cognitive walkthrough questions to gauge task completion. The initial evaluation uncovers areas where the AR content could be improved, specifically to enhance the educational value of the three-dimensional heart model. The cognitive walkthrough reveals further refinements that could be made to augment the application's usability in future updates. This work represents a preliminary evaluation phase, where standardized methods are applied to refine the application before its broader implementation among student populations. Such evaluations are crucial in optimizing experiential learning techniques that support online education in anatomy.

**Question**: Comparative study of traditional and machine learning-based approaches in stock market prediction

**Documents**: A Comparative Study of Machine Learning Methods for Stock Market Prediction
In this paper, we conduct a comparative study of machine learning (ML) methods for stock market prediction. We consider four ML methods: Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). We apply these methods to historical stock market data to predict future stock prices. We compare the performance of these methods using various performance metrics such as accuracy, precision, recall, and F1 score. Our results show that the RF method performs the best among the four methods in terms of accuracy and F1 score. SVM performs the second-best, followed by ANN and DT. We also find that the performance of these methods is sensitive to the choice of hyperparameters. We conclude that machine learning methods have the potential to improve stock market prediction accuracy, but the choice of method and hyperparameters need to be carefully considered.

**Similar Documents**: This paper presents a comprehensive analysis comparing various machine learning techniques in the context of stock market prediction. Four prominent machine learning algorithms—Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN)—were selected for this study. By applying these algorithms to historical stock data, we aimed to forecast future stock prices. To evaluate the efficacy of each

Positron emission tomography (PET) is a nuclear functional imaging technique that produces a three-dimensional image of functional organs in the body. PET requires high resolution, fast and low power multichannel analog to digital converter (ADC). A typical multichannel ADC for PET scanner architecture consists of several blocks. Most of the blocks can be designed by using fast, low power D flip-flops. A preset-able true single phase clocked (TSPC) D flip-flop shows numerous glitches (noise) at the output due to unnecessary toggling at the intermediate nodes. Preset-able modified TSPC (MTSPC) D flip-flop have been proposed as an alternative solution to alleviate this problem. However, the MTSPC D flip-flop requires one extra PMOS to suspend toggling of the intermediate nodes. In this work, we designed a 7-bit preset-able gray code counter by using the proposed D flip-flop. This work involves UMC 180 nm CMOS technology for preset-able 7-bit gray code counter where we achieved 1 GHz maximum operation frequency with most significant bit (MSB) delay 0.96 ns, power consumption 244.2 μW (micro watt) and power delay product (PDP) 0.23 pJ (Pico joule) from 1.8 V power supply.

This paper presents the design of a four electrode impedance measurement circuit dedicated to bioimpedance embedded applications. It extends the use of the AD5933 measurement chip to allow it to operate in a four electrode configuration in order to limit the effects of the parasitic impedances between the medium under test and the electrodes. The circuit has shown a good measurement accuracy on various test circuits. In association with a four microband electrode system it has been successfully used to characterize small physiological samples (50 μl) with conductivities ranging from 0.14 to 1.2 S m(-1). It can be used as an alternative bioimpedance measurement approach for embedded applications operating in the four electrode configuration.

This paper presents an Internet of Things (IoT) approach to Human Activity Recognition (HAR) using remote monitoring of vital signs in the context of a healthcare system for self-managed chronic heart patients. Our goal is to create a HAR-IoT system using learning algorithms to infer the activity done within 4 categories (lie, sit, walk and run) as well as the time consumed performing these activities and, finally giving feedback during and after the activity. Alike in this work, we provide a comprehensive insight on the cloud-based system implemented and the conclusions after implementing two different learning algorithms and the results of the overall system for larger implementations.

Omer Gottesman, Fredrik Johansson, Joshua Meier, Jack Dent, Donghun Lee, Srivatsan Srinivasan, Linying Zhang, Yi Ding, David Wihl, Xuefeng Peng, Jiayu Yao, Isaac Lage, Christopher Mosch, Li-wei H. Lehman, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David Sontag, and Finale Doshi-Velez Paulson School of Engineering and Applied Sciences, Harvard University Institute for Medical Engineering and Science, MIT T.H. Chan School of Public Health, Harvard University Department of Statistics, Harvard University Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT Department of Surgery and Cancer, Faculty of Medicine, Imperial College London Department of Bioengineering, Imperial College London Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center MIT Critical Data

Digital Technology is constantly improving as information becomes wireless. These advances demand more wearable and mobile form factors for products that access information. A product that is wearable should have wearability. This paper explores the concept of dynamic wearability, through design research. Wearability is defined as the interaction between the human body and the wearable object. Dynamic wearability extends that definition to include the human body in motion. Our research has been to locate, understand, and define the spaces on the human body where solid and flexible forms can rest-without interfering with fluid human movement. The result is a set of design guidelines embodied in a set of wearable forms. These wearable forms describe the three dimensional spaces on the body best suited for comfortable and unobtrusive wearability by design.

The system design of an integrated microwave imaging radar for the diagnostic screening of breasts cancer is presented. A custom integrated circuit implemented in a 65-nm CMOS technology and a pair of patch antennas realized on a planar laminate are proposed as the basic module of the imaging antenna array. The radar operates on the broad frequency range from 2 to 16 GHz with a dynamic range of 107 dB. Imaging experiments carried out on a realistic breast phantom show that the system is capable of detecting tumor targets with a resolution of 3 mm.

We present a contactless solution for detecting sleep apnea events on smartphones. To achieve this, we introduce a novel system that monitors the minute chest and abdomen movements caused by breathing on smartphones. Our system works with the phone away from the subject and can simultaneously identify and track the fine-grained breathing movements from multiple subjects. We do this by transforming the phone into an active sonar system that emits frequency-modulated sound signals and listens to their reflections; our design monitors the minute changes to these reflections to extract the chest movements. Results from a home bedroom environment shows that our design operates efficiently at distances of up to a meter and works even with the subject under a blanket.
 Building on the above system, we develop algorithms that identify various sleep apnea events including obstructive apnea, central apnea, and hypopnea from the sonar reflections. We deploy our system at the UW Medicine Sleep Center at Harborview and perform a clinical study with 37 patients for a total of 296 hours. Our study demonstrates that the number of respiratory events identified by our system is highly correlated with the ground truth and has a correlation coefficient of 0.9957, 0.9860, and 0.9533 for central apnea, obstructive apnea and hypopnea respectively. Furthermore, the average error in computing of rate of apnea and hypopnea events is as low as 1.9 events/hr.

Development and integration of physiological sensors into automotive applications is gaining importance. Assistance systems which possess knowledge about the driver's cognitive state could increase road safety. In this paper we present a flexible framework that enables the development, evaluation and verification of sensors and algorithms for automotive applications using physiological signals under realistic driving conditions. We have integrated a custom capacitive ECG measurement system into a test car and validated its performance in real world driving tests. During first test runs, the capacitive system achieved a sensitivity of up to 95.5% and a precision rate of up to 92.6%. Our system also records synchronized vehicle dynamics. We discuss the road test measurements which suggest that the driving situation highly impacts the quality of ECG signal. Therefore, information on driving dynamics could be used to improve the precision rate of future capacitive ECG measurement.

Atrial fibrillation (AF) is an arrhythmia that can lead to several patient risks. This kind of arrhythmia affects mostly elderly people, in particular those who suffer from heart failure (one of the main causes of hospitalization). Thus, detection of AF becomes decisive in the prevention of cardiac threats. In this paper an algorithm for AF detection based on a novel algorithm architecture and feature extraction methods is proposed. The aforementioned architecture is based on the analysis of the three main physiological characteristics of AF: i) P wave absence ii) heart rate irregularity and iii) atrial activity (AA). Discriminative features are extracted using model-based statistic and frequency based approaches. Sensitivity and specificity results (respectively, 93.80% and 96.09% using the MIT-BIH AF database) show that the proposed algorithm is able to outperform state-of-the-art methods.

The design of a 12.77-MHz on-chip RC relaxation oscillator with digital compensation technique is presented. To maintain the frequency stability versus temperature and supply voltage variations, loop delay tuning by a digital feedback loop is developed in this system. In order to generate an on-chip reference for digital calibration, a replica comparator is added. The on-chip relaxation oscillator is fabricated in 0.18-μm CMOS process. The measured output frequency variation is 31 ppm/°C across -30 to 120 °C temperature range after compensation. The frequency variation over the supply voltage from 0.6 V to 1.1 V is ±0.5%/V. The measured total power consumption is 56.2 μW at 0.9-V supply voltage when the digital compensation blocks are enabled. After digital compensation, the compensation blocks can be shutdown for power saving, and the main oscillator consumes only 12.8 μW.