Care-Chair: Opportunistic health assessment with smart sensing on chair backrest

A vast majority of the population spend most of their time in a sedentary position, which potentially makes a chair a huge source of information about a person\u27s daily activity. This information, which often gets ignored, can reveal important health data but the overhead and the time consumption needed to track the daily activity of a person is a major hurdle. Considering this, a simple and cost-efficient sensory system, named Care-Chair, with four square force sensitive resistors on the backrest of a chair has been designed to collect the activity details and breathing rate of the users. The Care-Chair system is considered as an opportunistic environmental sensor that can track each and every activity of its occupant without any human intervention. It is specifically designed and tested for elderly people and people with sedentary job. The system was tested using 5 users data for the sedentary activity classification and it successfully classified 18 activities in laboratory environment with 86% accuracy. In an another experiment of breathing rate detection with 19 users data, the Care-Chair produced precise results with slight variance with ground truth breathing rate. The Care-Chair yields contextually good results when tested in uncontrolled environment with single user data collected during long hours of study. --Abstract, page iii

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Proceedings of the 6th international conference on disability, virtual reality and associated technologies (ICDVRAT 2006)

The proceedings of the conferenc

Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration

One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy

Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration

One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy

Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation

Proceedings of the 3rd international conference on disability, virtual reality and associated technologies (ICDVRAT 2000)

The proceedings of the conferenc

Assessment of Physical Activity in Adults with Progressive Muscle Disease

Introduction: Insufficient physical activity is a major threat to global health. Physical activity benefits peoples’ physical and mental health. The general population, including people living with disabilities and muscle wasting conditions, are recommended to avoid excessive sedentary time and engage in daily activity. Adults with progressive muscle disease experience barriers to physical activity participation, including muscle weakness, fatigue, physical deconditioning, impairment, activity limitations and participation restrictions (including societal and environmental factors), and fear of symptom exacerbation. More research is required to understand the inter-relationship between health and physical activity for adults with progressive muscle disease, particularly non-ambulant people who are under-represented in the existing research literature. Accurate measurement of FITT (frequency, intensity, time, and type of physical activity) is vital for high-quality physical activity assessment. The aim of this thesis was to assess the physical activity of ambulant and non-ambulant adults with progressive muscle disease.Systematic review findings identified various measures used to assess physical activity in adults with muscular dystrophy, including accelerometers, direct observation, heart rate monitors, calorimetry, positioning systems, activity diaries, single scales, interviews and questionnaires. None of the measures identified in the systematic review had well established measurement properties for adults with muscular dystrophy.Patient and public involvement interviews highlighted the importance of inclusive, remote, and technology-facilitated research design, the potential intrusion of direct observations of physical activity, the familiarity of questionnaires for data collection, and practical considerations to ensure wearing an activity monitor was not too burdensome.A feasibility study using multiple methods in 20 ambulant and non-ambulant adults with progressive muscle disease revealed satisfactory acceptability, interpretability, and usability of Fitbit and activity questionnaires, in both paper and electronic formats. During supervised activity tasks, Fitbit was found to have satisfactory criterion validity, reliability, and responsiveness and measurement properties were strengthened using multisensory measurement.An observational, longitudinal study that included 111 ambulant and non-ambulant adults with progressive muscle disease showed that:Activity monitoring had satisfactory validity, reliability and responsiveness using Fitbit, but there was considerable measurement error between Fitbit and the research grade GENEActiv accelerometer. Fitbit thresholds and multiple metrics (including accelerometer and heart rate data extrapolations of FITT) were appropriate for physical activity assessment in ambulant and non-ambulant adults with progressive muscle disease.Activity self-report had unsatisfactory concurrent validity, test-retest reliability, and responsiveness with substantial activity overestimation using the modified International Physical Activity Questionnaire. However, self-report properties were improved when used concurrently with Fitbit.Observed physical activity in adults with progressive muscle disease was generally low with excessive daily sedentary time. Activity frequencies, intensities and durations were lower, and activity types were more domestic, for wheelchair users and during the COVID-19 lockdown. Lower physical activity was significantly associated with greater functional impairment, less cardiorespiratory fitness, worse metabolic health, and lower quality of life. Activity optimisation thresholds and minimal clinically important differences were established.Discussion: The implications of this thesis include guidance for selection of appropriate physical activity measures by clinicians and researchers working with adults with progressive muscle disease. Fitbit is suitable in clinical practice and research for interactive, weekly remote activity monitoring or to support activity self-management and may represent an appropriate compromise between potential underestimation by accelerometry alone, and overestimation by self-report alone. A draft conceptual framework for physical activity measurement was also proposed. It includes frequency, intensity, time, and type of physical activity, and incorporates wider aspects of the physical activity construct, including somatic factors (relating to progressive muscle disease and underlying fitness) and contextual factors (relating to personal, social, and environmental situations). Future research will build on the knowledge gained in this thesis, furthering understanding of the inter-relationships between physical activity, health and wider contexts. Implementation will include testing a remote physical activity optimisation intervention that is inclusive of ambulant and non-ambulant participants, featuring Fitbit self-monitoring with a focus on optimisation of daily activity frequency and regularly interrupting sedentary time.</div