Wearable feedback systems for rehabilitation

In this paper we describe LiveNet, a flexible wearable platform intended for long-term ambulatory health monitoring with real-time data streaming and context classification. Based on the MIT Wearable Computing Group's distributed mobile system architecture, LiveNet is a stable, accessible system that combines inexpensive, commodity hardware; a flexible sensor/peripheral interconnection bus; and a powerful, light-weight distributed sensing, classification, and inter-process communications software architecture to facilitate the development of distributed real-time multi-modal and context-aware applications. LiveNet is able to continuously monitor a wide range of physiological signals together with the user's activity and context, to develop a personalized, data-rich health profile of a user over time. We demonstrate the power and functionality of this platform by describing a number of health monitoring applications using the LiveNet system in a variety of clinical studies that are underway. Initial evaluations of these pilot experiments demonstrate the potential of using the LiveNet system for real-world applications in rehabilitation medicine

Non-invasive wearable sensing systems for continuous health monitoring and long-term behavior modeling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.Includes bibliographical references (p. 212-228).Deploying new healthcare technologies for proactive health and elder care will become a major priority over the next decade, as medical care systems worldwide become strained by the aging populations. This thesis presents LiveNet, a distributed mobile system based on low-cost commodity hardware that can be deployed for a variety of healthcare applications. LiveNet embodies a flexible infrastructure platform intended for long-term ambulatory health monitoring with real-time data streaming and context classification capabilities. Using LiveNet, we are able to continuously monitor a wide range of physiological signals together with the user's activity and context, to develop a personalized, data-rich health profile of a user over time. Most clinical sensing technologies that exist have focused on accuracy and reliability, at the expense of cost-effectiveness, burden on the patient, and portability. Future proactive health technologies, on the other hand, must be affordable, unobtrusive, and non-invasive if the general population is going to adopt them.(cont.) In this thesis, we focus on the potential of using features derived from minimally invasive physiological and contextual sensors such as motion, speech, heart rate, skin conductance, and temperature/heat flux that can be used in combination with mobile technology to create powerful context-aware systems that are transparent to the user. In many cases, these non-invasive sensing technologies can completely replace more invasive diagnostic sensing for applications in long-term monitoring, behavior and physiology trending, and real-time proactive feedback and alert systems. Non-invasive sensing technologies are particularly important in ambulatory and continuous monitoring applications, where more cumbersome sensing equipment that is typically found in medical and clinical research settings is not usable. The research in this thesis demonstrates that it is possible to use simple non-invasive physiological and contextual sensing using the LiveNet system to accurately classify a variety of physiological conditions. We demonstrate that non-invasive sensing can be correlated to a variety of important physiological and behavioral phenomenon, and thus can serve as substitutes to more invasive and unwieldy forms of medical monitoring devices while still providing a high level of diagnostic power.(cont.) From this foundation, the LiveNet system is deployed in a number of studies to quantify physiological and contextual state. First, a number of classifiers for important health and general contextual cues such as activity state and stress level are developed from basic non-invasive physiological sensing. We then demonstrate that the LiveNet system can be used to develop systems that can classify clinically significant physiological and pathological conditions and that are robust in the presence of noise, motion artifacts, and other adverse conditions found in real-world situations. This is highlighted in a cold exposure and core body temperature study in collaboration with the U.S. Army Research Institute of Environmental Medicine. In this study, we show that it is possible to develop real-time implementations of these classifiers for proactive health monitors that can provide instantaneous feedback relevant in soldier monitoring applications. This thesis also demonstrates that the LiveNet platform can be used for long-term continuous monitoring applications to study physiological trends that vary slowly with time.(cont.) In a clinical study with the Psychiatry Department at the Massachusetts General Hospital, the LiveNet platform is used to continuously monitor clinically depressed patients during their stays on an in-patient ward for treatment. We show that we can accurately correlate physiology and behavior to depression state, as well as to track changes in depression state over time through the course of treatment. This study demonstrates how long-term physiology and behavioral changes can be captured to objectively measure medical treatment and medication efficacy. In another long-term monitoring study, the LiveNet platform is used to collect data on people's everyday behavior as they go through daily life. By collecting long-term behavioral data, we demonstrate the possibility of modeling and predicting high-level behavior using simple physiologic and contextual information derived solely from ambulatory mobile sensing technology.by Michael Sung.Ph.D

Activity recognition based on thermopile imaging array sensors

With aging population, the importance of caring for elderly people is getting more and more attention. In this paper, a low resolution thermopile array sensor is used to develop an activity recognition system for elderly people. The sensor is composed of a 32x32 thermopile array with the corresponding 33° × 33° field of view. The outputs of the sensor are sequential images in which each pixel contains a temperature value. According to the thermopile images, the activity recognition system first determines whether the target is within the tracking area; if the target is within the tracking area, the location of the target will be detected and three kinds of activities will be identified. Keywords- Activity Recognition, Raspberry Pi, Thermopile, Imaging Processing

Physical Human Activity Recognition Using Machine Learning Algorithms

With the rise in ubiquitous computing, the desire to make everyday lives smarter and easier with technology is on the increase. Human activity recognition (HAR) is the outcome of a similar motive. HAR enables a wide range of pervasive computing applications by recognizing the activity performed by a user. In order to contribute to the multi facet applications that HAR is capable to offer, predicting the right activity is of utmost importance. Simplest of the issues as the use of incorrect data manipulation or utilizing a wrong algorithm to perform prediction can hinder the performance of a HAR system. This study is designed to perform HAR by using two dimensionality reduction techniques followed by five different supervised machine learning algorithms as an aim to receive better predictive accuracy over the existing benchmark research. Correlation analysis (CA) and Principal component analysis (PCA) are used for feature reduction which resulted in 173 and 100 features respectively. Decision Tree, K Nearest Neighbor, Naive Bayes, Multinomial Logistic Regression and Artificial Neural Network algorithms were used to perform the classification task. The repeated random sub-sampling cross validation technique was used to perform the evaluation followed by a Wilcoxon signed rank test to evaluate the significance of the tests. The study resulted in ANN performing the best classification by achieving 97% of accuracy using the CA as feature reduction technique. The KNN and LR also provided satisfactory results and have received predictive results greater than the benchmark test. However, the decision tree and Naive bayes algorithms didn’t prove efficient

A health-shirt using e-textile materials for the continuous monitoring of arterial blood pressure.

Chan, Chun Hung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 77-84).Abstracts in Chinese and English.Acknowledgment: --- p.i摘要 --- p.iiAbstract --- p.ivList of Figure --- p.viList of Table --- p.viiiContent Page --- p.ixChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- The Difficulties --- p.1Chapter 1.2 --- The Solution --- p.2Chapter 1.3 --- Goal of the Present Work --- p.2Chapter Chapter 2 --- Background and Methodology --- p.3Chapter 2.1 --- Hypertension Situation and Problems Around the World --- p.3Chapter 2.1.1 --- Blood Pressure Variability (BPV) --- p.4Chapter 2.2 --- Blood Pressure Measuring Methods --- p.5Chapter 2.2.1 --- Traditional Blood Pressure Meters --- p.6Chapter 2.2.2 --- Limitation of Commercial Blood Pressure Meters --- p.7Chapter 2.2.3 --- Pulse-Transit-Time (PTT) Based Blood Pressure Measuring Watch --- p.7Chapter 2.3 --- Wearable Body Sensors Network / System --- p.8Chapter 2.4 --- Current Status of e-Textile Garment --- p.9Chapter 2.4.1 --- Blood Pressure Measurement in e-Textile Garment --- p.13Chapter 2.5 --- Wearable Intelligent Sensors and System for e-Health (WISSH) --- p.15Chapter 2.5.1 --- "Monitoring, Connection and Display" --- p.15Chapter 2.5.2 --- Treatment --- p.16Chapter 2.5.3 --- Alarming --- p.17Chapter Chapter 3 --- "A h-Shirt to Non-invasive, Continuous Monitoring of Arterial Blood Pressure" --- p.18Chapter 3.1 --- Design and Inner Structure of h-Shirt --- p.18Chapter 3.1.1 --- Choose of e-Textile Material --- p.21Chapter 3.1.2 --- Design of ECG Circuit --- p.23Chapter 3.1.3 --- Design of PPG Circuit --- p.26Chapter 3.2 --- Blood Pressure Estimation Using Pulse-Transit-Time Algorithm --- p.28Chapter 3.2.1 --- Principal --- p.28Chapter 3.2.2 --- Equations --- p.29Chapter 3.2.3 --- Calibration --- p.29Chapter 3.3 --- Performance Tests on h-Shirt --- p.30Chapter 3.3.1 --- Test I: BP Measurement Accuracy --- p.30Chapter 3.3.2 --- Test I: Procedure and Protocol --- p.30Chapter 3.3.3 --- Test I-Results --- p.31Chapter 3.3.4 --- Test II: Continuality BP Estimation Performance --- p.31Chapter 3.3.5 --- Test II - Experiment Procedure and Protocol --- p.32Chapter 3.3.6 --- Test II - Experiment Result --- p.33Chapter 3.3.7 --- Test II 一 Discussion --- p.43Chapter 3.4 --- Follow-up Tests on ECG Circuit --- p.47Chapter 3.4.1 --- Problems --- p.47Chapter 3.4.2 --- Assumptions --- p.48Chapter 3.4.3 --- Experiment Protocol and Setup --- p.48Chapter 3.4.4 --- Experiment Results --- p.53Chapter 3.4.5 --- Discussion --- p.56Chapter Chapter 4: --- Hybrid Body Sensor Network in h-Shirt --- p.59Chapter 4.1 --- A Hybrid Body Sensor Network --- p.59Chapter 4.2 --- Biological Channel Used in h-Shirt --- p.60Chapter 4.3 --- Tests of Bio-channel Performance --- p.62Chapter 4.3.1 --- Experiment Protocol --- p.62Chapter 4.3.2 --- Results --- p.62Chapter 4.4 --- Discussion and Conclusion --- p.63Chapter Chapter 5: --- Conclusion and Suggestions for Future Works --- p.66Chapter 5.1 --- Conclusion --- p.66Chapter 5.1.1 --- Structure of h-Shirt --- p.66Chapter 5.1.2 --- Blood Pressure Estimating Ability of h-Shirt --- p.67Chapter 5.1.3 --- Tests and Amendments on h-Shirt ECG Circuit --- p.67Chapter 5.1.4 --- Hybrid Body Sensor Network in h-Shirt --- p.67Chapter 5.2 --- Suggestions for Future Work --- p.68Chapter 5.2.1 --- Further Development of Bio-channel Biological Model --- p.68Chapter 5.2.2 --- Positioning and Motion Sensing with h-Shirt --- p.69Chapter 5.2.3 --- Implementation of Updated Advance Technology into h-Shirt --- p.69Appendix: Non-invasive BP Measuring Device - Finometer --- p.71Reference: --- p.7

The memory glasses : wearable computing for just-in-time memory support

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2004.Includes bibliographical references (p. 173-181).This thesis documents a body of wearable computing research surrounding the development of the Memory Glasses, a new type of proactive memory support technology. The Memory Glasses combines features of existing memory support technologies (such as PDAs) with a context aware delivery system and a low-attention cuing interface. The goal of the Memory Glasses is to provide effective just-in-time memory support while mitigating some of the distraction and over-reliance problems that can result from the use of more conventional memory support technology. The Memory Glasses research is a synthesis of the author's six years of work on wearable computing. This thesis documents the author's intellectual contributions in the areas of wearable computing hardware architectures, software architectures, and human-computer interaction. Specific topics include the MIThril wearable computing research platform, the Enchantment middlewear, the MIThril Real-Time Context Engine, the author's modified Seven Stages of Action model and five principles of low-attention wearable human computer interaction, as well as the author's research in the use of subliminal cuing for just-in-time memory support. Although memory support is the unifying theme of this dissertation, the author's research has seen application in a number of other areas, including the mapping of social networks, research in human physiology and biomedical applications, and group situation awareness and command, control, and communications. A selection of these applications is briefly presented as support for the importance of the author's intellectual contributions.by Richard W. DeVaul.Ph.D

Validation of two types of textile electrodes for electrocardiography and electromyography measurement applications

Tese de mestrado. Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 201

Biomedical Signal Processing and Inference in Wearable Sensing Applications

With the increase in health care costs, there is a great need to develop a low-cost and efficient health care system with better accessibility. Recent advances in the fields of embedded sensing, mobile computing, and wireless communication have led to the development of several low-cost wearable health sensors that can noninvasively collect different physiological signals such as electrocardiogram (ECG), peripheral oxygen saturation (SpO2), blood pressure, airflow, etc. With the availability of these wearable sensors and powerful smartphones, it is now possible to build a smart health monitoring system that can continuously monitor and track a person's health without the need of a hospital visit. In this research, we primarily focus on the signal processing and statistical inference aspects of the smart health system. First, we develop a biometric recognition system using the ECG signal, which is easily measured by several wearable devices. A multitask learning framework, in which the feature selection and classifier design are combined, is proposed to improve the overall learning efficiency. Experimental results on real ECG data show the effectiveness of the proposed method over other approaches. In the next part, we focus on the mathematical modeling of the local control mechanism of the cardiorespiratory system. We use a nonlinear model of the cardiorespiratory system with heart rate and ventilation rate as the control signals. An iterative algorithm is proposed to calculate the optimal control signals. In the final part, we focus on the detection of a chronic respiratory sleep disorder, sleep apnea, using measurement signals from wearable sensors. A new framework combining multiple sensor measurement data with the cardiorespiratory system model information is proposed. Experimental results on both synthetic and real data show the effectiveness of the proposed framework. Comparisons with purely data-driven apnea detection methods demonstrate the advantage of combining the sensor measurement data with cardiorespiratory model information.Electrical Engineerin

Exploring therapeutic neurogenic tremors with exercise as a treatment for selective motor and non-motor Parkinson's disease symptoms

Thesis (PhD)--Stellenbosch University, 2019.ENGLISH ABSTRACT: Intro: Parkinson‘s disease (PD) is a chronic neurological progressive disorder accompanied by a wide range of symptoms that affect independence and quality of life (QoL) [1]. Individuals with PD (IwPD) experience motor symptoms, including postural instability and gait disturbances, and non-motor symptoms (NMS), including depressive moods, anxiety and autonomic dysregulation [2]. Daily stress further exacerbates PD symptoms [3]. Therefore stress management is of particular importance for IwPD. Relaxation-based exercises might be a viable option, and recently the addition of therapeutic neurogenic tremors (TNT) to exercise have been shown to aid in the reduction of perceived stress as well as improvement in QoL [4–6]. These tremors are theorised to be a genetically-encoded mechanism part of the stress response [7], and a necessary process for the body to function optimally after stressful and traumatic events [8,9]. Therefore, the current study set out to investigate the effects of relaxation-based exercises with and without TNT on selective motor and non-motor symptoms of IwPD. Methods: Thirty-six individuals with idiopathic PD participated in this experimental study, with a double-blinded randomised time-series design. Participants were randomly allocated to three groups: 1)Exercises with TNT (TRE), 2) Exercises without TNT (EAR), and 3) a non-exercising waitlist controlgroup (n = 12, 69.6 ± 8.3 years). Group 1 (n = 14, 72.7 ± 7.5 years) participated in a Trauma and Tension Releasing Exercises (TRE) intervention, while Group 2 (n = 10, 70.3 ± 5.7 years) participated in the Exercise and Relaxation (EAR) intervention. Both interventions followed the same protocol except for the addition of TNT in the TRE group, and took place with tapered supervision over nine weeks. Participants, in all three groups, were tested every three weeks (i.e. baseline, 3, 6 and 9 weeks), and after a three week retention period. Primary outcome measures included postural instability, gait disturbances, domains of NMS, depressive moods, general anxiety, and somatisation. Assessments included the Mini Balance Evaluation Systems Test (BESTest), instrumented 2-Minute Walk (2MW), NMS Questionnaire (NMSQuest) and NMS Symptoms Scale (NMSS), as well as the Patient Health Questionnaire for somatic, anxiety and depressive symptoms (PHQ-SADS). Secondary outcome measures included disease severity (assessed with the Movement Disorder Society’s – Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)), perceived balance confidence (assessed with the Activity-specific Balance Confidence (ABC) scale) and QoL (assessed by the 8-item Parkinson’s disease Questionnaire (PDQ-8)). Results: Groups did not differ in descriptive characteristics or outcome variables at baseline (p > 0.05), except for variability of trunk rotation, mood/cognition and attention/memory domains of NMSS between TRE and CON groups (p 0.48M), while TRE showed additional improvements for frequency stress-related items of NMSS (p 0.49M). Additionally significant practical improvements were observed for MDS-UPDRS II (motor experience of daily living) for TRE (p = 0.02, Hedges’ g = 0.29S) and control group (p = 0.01, Hedges’ g = 0.33S). The retention period showed improvements in Mini BESTest domains for EAR (p =0.04, Hedges’ g = 0.57M) and control (p = 0.02, Hedges’ g = 0.65M) groups, and improvement in NMSQuest for TRE (p = 0.04, Hedges’ g = 0.56M). Conclusion: This exploratory study shows promising preliminary results for relaxation-based exercises with TNT. The findings suggest that relaxation-based exercises were beneficial towards improving gait performance, decreasing the severity of selective NMS and possibly improving QoL. The addition of TNT could have the potential of further improvements in the motor experience of daily living, quality of gait, and the frequency of stress-related NMS. Therapies utilizing TNT could be an essential tool for IwPD to reduce the impact of motor and NMS, and manage stress. However, more research is needed to investigate the effects of TNT on populations vulnerable to stress.AFRIKAANSE OPSOMMING: Inleiding: Parkinson se siekte (PD) is 'n chroniese neurologiese progressiewe versteuring, wat gepaard gaan met 'n wye verskeidenheid simptome wat onafhanklikheid en lewenskwaliteit beïnvloed [1]. Individue met PD (IwPD) ervaar motoriese simptome, insluitende posturale onstabiliteit en loopgangversteurings, en nie-motoriese simptome (NMS), insluitend depressiewe buie, angs en outonomiese-senuweestelsel wanfunksie [2]. Daaglikse stres kan PD-simptome toenemend vererger [3]. Daarom is stresbestuur vir IwPD van besondere belang. Ontspanningsgebaseerde oefeninge kan 'n moontlike opsie van waarde wees, en onlangs het die toevoeging van terapeutiese neurogene bewing (TNT) tot oefeninge getoon dat dit help met die vermindering van waargenome stres asook verbetering in lewenskwaliteit [4–6]. Dit word teoreties voorgestel dat hierdie bewinge 'n geneties-gekodeerde meganisme deel van die stresrespons vorm [7], en 'n noodsaaklike proses van die liggaam is om optimaal te funksioneer na stresvolle en traumatiese gebeure [8,9]. Daarom het die huidige studie ondersoek ingestel na die uitwerking van ontspanninggebaseerde oefeninge met en sonder TNT op selektiewe motoriese en nie-motoriese simptome van IwPD. Metodes: Ses-en-dertig individue met idiopatiese PD het aan hierdie eksperimentele studie deelgeneem, met 'n dubbelblinde willekeurige tydreeksontwerp. Deelnemers is willekeurig toegewys aan een van drie groepe: 1) Oefeninge met TNT, 2) Oefeninge sonder TNT (EAR), en 3) 'n nie-oefenende waglys kontrole groep (n = 12, 69.6 ± 8.3 jaar). Groep 1 (n = 14, 72.7 ± 7.5 jaar) het deelgeneem aan ʼn “Trauma and Tension Releasing Exercises” (TRE) intervensie, terwyl die EAR-groep (n = 10, 70.3 ± 5.7 jaar) aan ʼn oefening en ontspanningsintervensie (EAR) deelgeneem het. Beide intervensies het dieselfde protokol gevolg, behalwe vir die byvoeging van TNT in die TRE-groep, en het oor nege weke met afnemende toesig plaasgevind. Deelnemers, in al drie groepe, was elke drie weke getoets (dws basislyn, 3, 6 en 9 weke), en na 'n drie weke retensieperiode. Primêre uitkomsmates het posturale onstabiliteit, loopgangversteurings, areas van NMS, depressiewe buie, angs en somtiese simptome ingesluit. Assesserings het die Minibalansevalueringstoets (Mini BESTest), Instrumentiewe 2-Minute-Stap (2MW), NMS-vraelys (NMSQuest) en NMS-simptoomskaal (NMSS) ingesluit, sowel as die “Patient Health Questionnaire” vir somatiese, angs en depressiewe simptome (PHQ-SADS). Sekondêre uitkomsmates het siekte-erns (gemeet deur die “Movement Disorder Society’s – Unified Parkinson’s Disease Rating Scale” (MDS-UPDRS)), waargeneome balance vertroue (gemeet deur die Aktiwiteits-spesifieke Balansvertroue (ABC) skaal) en lewenskwaliteit (gemeet deur die 8-item Parkinson se siekte vraelys (PDQ-8)) ingesluit. Resultate: Groepe het nie verskil in beskrywende eienskappe of uitkomsveranderlikes by basislyn nie (p > 0.05), behalwe vir variasie van romprotasie, gemoedstoestand/kognitiewe en aandag/geheue areas van die NMSS tussen TRE and CON groepe (p 0.48M), terwyl TRE bykomende verbeterings teweeg gebring het vir die frekwensie van stresverwante items van NMSS (p 0.49M). . Addisionele verbeteringe was gevind vir MDS-UPDRS II (motoriese ervaring van daaglikse lewe) vir die TRE (p = 0.02, Hedges’ g = 0.29S) en kontrole groep (p = 0.01, Hedges’ g = 0.33S). Die retensieperiode het verbeteringe in Mini BESTest aspekte vir die EAR groep (p = 0.04, Hedges’ g = 0.57M) en die kontrolegroep (p = 0.02, Hedges’ g = 0.65M) getoon, asook verbetering in NMSQuest vir die TRE groep (p = 0.04, Hedges’ g = 0.56M). Gevolgtrekking: Hierdie verkennende studie toon belowende voorlopige resultate vir ontspanninggebaseerde oefeninge met TNT. Die bevindinge dui daarop dat ontspanningsgebaseerde oefeninge voordelig is vir die verbetering van loopgangprestasie, die vermindering in intensiteit van selektiewe NMS en moontlik verbeterde lewenskwaliteit. Die toevoeging van TNT het moontlik die potensiaal om die motoriese ervaring van die daaglikse lewe, loopangkwaliteit, en die frekwensie van stresverwante NMS te verbeter. Terapieë wat TNT gebruik, kan 'n noodsaaklike hulpmiddel vir IwPD wees om die impak van motoriese simptome en NMS te verminder en stres te bestuur. Meer navorsing is egter nodig om die effekte van TNT op bevolkinggroepe, wat meer vatbaar is vir stres, te ondersoek.wa20190