43 research outputs found

    EEG-metric based mental stress detection

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    Mental stress level is a vital parameter affecting physical well-being, cognition, emotions, and professional efficiency. With growing adversities in modern living standards, causing abnormal mental stress, it is necessary to measure to cure it. Regular personal stress profile generated can be used as neurofeedback for the clinical as well as personal assessment. This paper describes a method to detect mental stress level based on physiological parameters. In this method, an electroencephalogram (EEG)-metric parameters based binary and ternary stress classifier is developed. This is validated through probabilistic stress profiler of differential stress inventory (a questionnaire based evaluation). Nine channel EEG is used to extract physiological signal. EEG-metric based cognitive state and workload outputs are generated for 41 healthy volunteers (37 males and 4 females, age; 24±5 years). All subjects were guided to perform three simple tasks of closed eye, focusing vision on a red dot on center of dark screen and focusing on a white screen. Central tendencies (mean, median and mode) and standard deviation were extracted of EEG-metric (sleep onset, distraction, low engagement, high engagement and cognitive states) as features. Either of the two or three classes of stress are evaluated from probabilistic stress profiler of differential stress inventory and used as training output classes. A supervisory training of multiple layer perceptron based binary support vector machine classifier was used to detect stress class one by one. 40 subject's samples were used for training and interchanging one-by one 41th subjects stress class is determined from the designed classifier. Out of 41 subjects, stress level of 30 subjects is correctly identified by binary classifier and stress level of 26 subjects is correctly identified by ternary classifier, using multi-layer perceptron kernel based SVM

    Every sign of life

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2003.MIT Institute Archives copy: pages 151-[182] bound in reverse order.Includes bibliographical references (p. 142-150).Every Sign of Life introduces an approach to and motivational schema for personal health monitoring. It is an exploration of how to make information collected by personal health-monitoring devices fun and engaging, and consequently more useful to the non-specialist. In contrast to the common methodology of adding game elements to established biofeedback systems, the Every Sign of Life approach is to design and build games that use biosensor information to effect the game environment. This work tests the hypothesis that fun (the joy of learning, achieving, competing, etc.) is a way to achieve the goal of self-efficacy; to induce people to take care of their own health by altering their habits and lifestyles. One result is a basic architecture for personal health-monitoring systems that has led to an approach to the design of sensor peripherals and wearable computer components called "Extremity Computing." This approach is used to redefine biosensor monitoring from periodic to continuous (ultimately saving data over a lifetime). Another result is an approach to adding implicit biofeedback to computer games. This has led to a new genre of games called "Bio-Analytical Games" that straddles the boundary between sports and computer games. A series of studies of how to present health information to children and adults have demonstrated the ability of consumers to use bioinformatics without involving professionals.by Vadim Gerasimov.Ph.D

    AffectiveViz:Designing Collective Stress Related Visualization

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    Psychophysiology in the digital age

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    The research I performed for my thesis revolved around the question how affect-physiology dynamics can be best measured in daily life. In my thesis I focused on three aspects of this question: 1) Do wearable wristband devices have sufficient validity to capture ANS activity? 2) To what extent is the laboratory design suitable to measure affect-ANS dynamics? 3) Are the affect-ANS dynamics subject to individual differences, both in the laboratory and in daily life? In chapter 2, I validated a shortened version of the Sing-a-Song Stress (SSST) test, the SSSTshort. The purpose of this test is to create social-evaluative stress in participants through a simple and brief design that does not require the involvement of multiple confederates. The results indicated that the SSSTshort was effective in inducing ANS and affective reactivity. This makes the SSSTshort a cost-effective alternative to the well-known Trier-Social-Stress task (TSST), which can be easily incorporated into large-scale studies to expand the range of stress types that can be studied in laboratory designs. In chapter 3, I validated a new wrist worn technology for measuring electrodermal activity (EDA). As expected, the overall EDA levels measured on the wrist were lower than those measured on the palm, likely due to the lower density of sweat glands on the wrist. The analysis demonstrated that the frequency measure of non-specific skin conductance response (ns.SCR) was superior to the commonly used measure of skin conductance level (SCL) for both the palm and wrist. The wrist-based ns.SCR measure was sensitive to the experimental manipulations and showed similar correspondence to the pre-ejection period (PEP) as palm-based ns.SCR. Moreover, wrist-based ns.SCR demonstrated similar predictive validity for affective state as PEP. However, the predictive validity of both wrist-based ns.SCR and PEP was lower compared to palm-based ns.SCR. These findings suggest that wrist-based ns.SCR EDA parameter has a promising future for use in psychophysiological research. In Chapter 4 of my thesis, I conducted the first study to directly compare the relationship between affect and ANS activity in a laboratory setting to that in daily life. To elicit stress in the laboratory, four different stress paradigms were employed, while stressful events in daily life were left to chance. In both settings, a valence and arousal scale was constructed from a nine-item affect questionnaire, and ANS activity was collected using the same devices. Data was collected from a single population, and the affect-ANS dynamics were analyzed using the same methodology for both laboratory and daily life settings. The results showed a remarkable similarity between the laboratory and daily life affect-ANS relationships. In Chapter 5 of my thesis, I investigated the influence of individual differences in physical activity and aerobic fitness on ANS and affective stress reactivity. Previous research has yielded inconsistent results due to heterogeneity issues in the population studied, stressor type, and the way fitness was measured. My study made a unique contribution to this field by measuring physical activity in three ways: 1) as objective aerobic fitness, 2) leisure time exercise behavior, and 3) total moderate-to-vigorous exercise (including both exercise and all other regular physical activity behaviors). In addition, we measured the physiological and affective stress response in both a laboratory and daily life setting. The total amount of physical activity showed more relationships with stress reactivity compared to exercise behavior alone, suggesting that future research should include a total physical activity variable. Our results did not support the cross-stressor adaptation hypotheses, suggesting that if exercise has a stress-reducing effect, it is unlikely to be mediated by altered ANS regulation due to repeated exposure to physical stress

    Methods for Heart Rate Variability Biofeedback (HRVB) : A Systematic Review and Guidelines

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    Altres ajuts: acords transformatius de la UABHeart Rate Variability Biofeedback (HRVB) has been widely used to improve cardiovascular health and well-being. HRVB is based on breathing at an individual's resonance frequency, which stimulates respiratory sinus arrhythmia (RSA) and the baroreflex. There is, however, no methodological consensus on how to apply HRVB, while details about the protocol used are often not well reported. Thus, the objectives of this systematic review are to describe the different HRVB protocols and detect methodological concerns. PsycINFO, CINALH, Medline and Web of Science were searched between 2000 and April 2021. Data extraction and quality assessment were based on PRISMA guidelines. A total of 143 studies were finally included from any scientific field and any type of sample. Three protocols for HRVB were found: (i) "Optimal RF" (n = 37), each participant breathes at their previously detected RF; (ii) "Individual RF" (n = 48), each participant follows a biofeedback device that shows the optimal breathing rate based on cardiovascular data in real time, and (iii) "Preset-pace RF" (n = 51), all participants breathe at the same rate rate, usually 6 breaths/minute. In addition, we found several methodological differences for applying HRVB in terms of number of weeks, duration of breathing or combination of laboratory and home sessions. Remarkably, almost 2/3 of the studies did not report enough information to replicate the HRVB protocol in terms of breathing duration, inhalation/exhalation ratio, breathing control or body position. Methodological guidelines and a checklist are proposed to enhance the methodological quality of future HRVB studies and increase the information reported

    Quantifying Quality of Life

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    Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

    Quantifying Quality of Life

    Get PDF
    Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

    Chapter 6 – Health Apps for Diagnostics and Therapy

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    Die Nutzung von mobilen Anwendungen auf Smartphones und Tablet-Computern im Gesundheitswesen und damit in der Medizin hat stark zugenommen. Evidenznachweise für medizinische Apps gibt es nur wenige. Ihre Nutzen in Diagnostik und Therapie besteht darin, an jedem Ort und zu jeder Zeit ortsunabhängige beliebige Informationen zu erfassen, aufzurufen, zu visualisieren und damit auch Entscheidungen zu treffen. Apps zur Diagnostik werden vorwiegend von medizinischem Fachpersonal genutzt, Apps zur Therapie hingegen von ärztlichem Personal wie von Patientinnen und Patienten gleichermaßen verwendet. Therapien können durch den Einsatz von Apps weiter optimiert werden, etwa durch gewonnene Mobilität oder durch geringere Kosten. Apps im Bereich Selbstmanagement, wie das Führen von Patiententagebüchern oder Apps, die an die Einnahme von Medikamenten erinnern, sind sinnvoll. Ähnlich sind Apps zu bewerten, die zu gesundheitsbewussten Verhalten anregen, etwa Trainingspläne für körperliche Übungen oder Ernährungstagebücher für die gesunde Ernährung. Tragbare Geräte wie Uhren, Brillen, Arm- und Körperbänder stellen eine unauffällige Lösung zur Überwachung von Vitalfunktion dar. Wearables können Nutzerinnen und Nutzern durch zusätzliche Funktionen somit Unterstützung bei der Erhaltung oder Verbesserung der Gesundheit bieten, sind jedoch teils mit hohen Kosten verbunden. Ärztinnen und Ärzten bieten die mobilen Anwendungen insbesondere bei erkrankten Patientinnen und Patienten Möglichkeiten, aktuelle Informationen über die Vitaldaten oder die derzeitige Situation der Betroffenen zu erhalten. Für Patientinnen und Patienten ist es einfach komfortabel, unabhängig von Zeit und Ort mit dem Arzt kommunizieren zu können. Die Betrachtung der Grenzen von Apps zeigt auf, dass eine Reihe von Barrieren und Herausforderungen vor dem nutzbringenden Einsatz dieser Tools stehen. Grundsätzlich bieten Apps die Chance zur Partizipation und Patientenbeteiligung und unterstützen in verschiedenen Phasen der Versorgungsprozesse im Gesundheitswesen.The use of mobile applications that are installed on smartphones and tablet computers has greatly increased in healthcare in general and thus also in medicine. However, scientific evidence with respect to the effectiveness of medical apps is still lacking. The possibility to diagnose and treat, in any place and at any time, to capture, access, visualize information and thus to come to decisions regardless of location, holds great promise, as do the rapid ways of transmitting medical data made possible by mobile technology. This is an essential aspect for modern aspects of telemedicine. Apps for diagnostics are primarily used by health care professionals, whereas apps with a therapeutic focus are used by both doctors and patients alike. Therapies can be further optimized by the use of apps, e.g. by providing mobility or through lower health care costs. Patient diaries or apps that instruct patients in the proper use of medication are helpful in patient self-management. So are apps that encourage health-conscious behavior, such as training plans for physical exercises or nutrition diaries for healthy eating. Portable devices such as watches, eyewear, and arm and body belts provide unobtrusive monitoring of vital function. Wearables equip users with additional functions that promote health. The downside lies in their high costs. Via mobile applications, doctors can receive and review up-to date information, e.g. vital signs or data about the patients' current situation. For patients, the added comfort of being able to communicate with their doctor regardless of time and place is a benefit. The evaluation of the limitations of apps shows that a number of barriers and challenges need to be overcome to take advantage of the beneficial aspects of these tools. In summary, apps offer the chance to involve patients actively in the management of their health and to support patients and doctors in various stages of the health care process

    Special oils for halal and safe cosmetics

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    Three types of non conventional oils were extracted, analyzed and tested for toxicity. Date palm kernel oil (DPKO), mango kernel oil (MKO) and Ramputan seed oil (RSO). Oil content for tow cultivars of dates Deglect Noor and Moshkan was 9.67% and 7.30%, respectively. The three varieties of mango were found to contain about 10% oil in average. The red yellow types of Ramputan were found to have 11 and 14% oil, respectively. The phenolic compounds in DPKO, MKO and RSO were 0.98, 0.88 and 0.78 mg/ml Gallic acid equivalent, respectively. Oils were analyzed for their fatty acid composition and they are rich in oleic acid C18:1 and showed the presence of (dodecanoic acid) lauric acid C12:0, which reported to appear some antimicrobial activities. All extracted oils, DPKO, MKO and RSO showed no toxic effect using prime shrimp bioassay. Since these oils are stable, melt at skin temperature, have good lubricity and are great source of essential fatty acids; they could be used as highly moisturizing, cleansing and nourishing oils because of high oleic acid content. They are ideal for use in such halal cosmetics such as Science, Engineering and Technology 75 skin care and massage, hair-care, soap and shampoo products
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