Assessing the effort of exercise using low cost sensors

In this work, it is proposed a solution that tries to help the physical education teachers in the assessment of their students, also trying to increase the motivational levels of students with the inclusion of new technologies in class, and with the fact that the presented metric, being estimated for each student from their own effort. It is proposed a solution that uses a sensor, in this case an accelerometer, in order to capture accelerometry data during the execution of four physical activities: volleyball, handball, basketball and futsal. Data was retrieved from two classes of the 9thº Portuguese school year. The device used to capture those accelerometry data was the student’s own smartphones, through a mobile application that collects data and sends it to a server. After that data is uploaded, it is analysed in order to calculate the metric Running Equivalent of Activity (REA). An empiric study was developed aiming at the experimental validation of the metric referred above, having been followed diverse experimental protocols. A set of good practices is suggested for the adoption of the proposed solution, a mean to enable a fair and equitable assessment of the effort applied by the students during the physical education classes.No presente trabalho é proposta uma solução que visa ajudar os professores de educação física a fazer a avaliação dos seus alunos, e os alunos a sentirem-se mais motivados com a inclusão de tecnologia nas aulas de educação física e com o facto da métrica apresentada, ser calculada a partir do esforço de cada um, tentando assim ultrapassar alguns vieses inerentes ao processo de avaliação. Foi proposta uma solução que utiliza um sensor, neste caso o acelerómetro, para a obtenção de dados de acelerometria durante a execução de quatro atividades físicas: voleibol, andebol, basquetebol e futsal. Foram recolhidos dados de duas turmas que frequentam o 9º ano. O dispositivo utilizado para a obtenção desses valores de acelerometria foi o smarpthone dos alunos, através de uma aplicação móvel que é responsável pela recolha dos dados e por os enviar para um servidor. Esses dados foram sintentizados e analisados de forma a calcular a métrica Running Equivalent of Activity (REA). Foi realizado um estudo empírico que visa a validação experimental da referida métrica tendo sido testados diversos protocolos experimentais. São sugeridas um conjunto de boas práticas para a adopção da solução ora proposta, no sentido de permitir uma aferição justa e equitativa do esforço aplicado pelos estudantes nas aulas de Educação Física

Analysis of Optimal Sensor Positions for Activity Classification and Application on a Different Data Collection Scenario

This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Nineteen features are extracted, and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and a dataset collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values greater than 96%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups

Classification of Frailty among Community Dwelling Older Adults Using Parameters of Physical Activity Obtained Independently and Unsupervised

The global population is ageing at an unprecedented rate, with the percentage of those aged over 65 years expected to double and those aged over 80 years expected to treble by the year 2050. With ageing comes biological and physiological changes that affect functional capacity. Frailty is a potentially avoidable, reversible biopsychosocial condition associated with biological but not chronological age, affecting a quarter of all community-dwelling older adults. Frailty results in disability, increased dependency and institutionalisation. Screening for frailty could help reduce its prevalence and mitigate the adverse outcomes however, traditional screening tools are time-consuming to perform, require clinician input and by their subjective nature are flawed. The use of wearable sensors has been proposed as a means of screening for frailty and parameters of mobility and physical activity have been identified as being associated with frailty. The goal of this thesis was to examine if community-dwelling older adults could capture parameters of mobility and physical activity independently in their own home and if these parameters could discriminate between frail and non-frail status. This work provides evidence that a single parameter of mobility and physical activity obtained from a single body-worn sensor correlates with frailty. It also provides evidence that community-dwelling older adults can independently capture parameters of mobility and physical activity, unsupervised in their own home using a consumer-grade wearable device, and that these data can predict pre-frailty and frailty with acceptable accuracy. Thresholds for parameters of physical activity predictive of frailty have been identified. The results of this thesis will guide future work to focus community-dwelling older adults on the importance of frailty screening and guide the development of a user-friendly device or sensor system suitable for use by older adults for continuous data collection relevant to frailty