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Physical Activity (PA) is bodily movement that involves energy output from the muscles and is associated with numerous health benefits. However, with the emergence of the COVID-19 pandemic on March 11, 2020, stay-at-home orders and public health measures led to a decreased trend in PA levels throughout the Canadian population. As restrictions began...
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... Recent research shows that regular gym users, even with the loss of a person in their family, see exercising in a gym as a priority in their life to maintain health and wellness [32]. There are many systems based on different technologies that are used to track users and objects in different environments, such as RFID-based indoor tracking for the elderly [33], smart phones for indoor tracking [34], and RSSI-based real-time user location tracking for indoor and outdoor environments [35]. ...
Physical activity is a crucial factor for maintaining not only physical health status, but vast amounts of research have shown its link with better mental health. Supporting the use of gyms for the safety of its practitioners is vital in the new norm and living with COVID-19. Therefore, in this study we show research supporting the development of a framework for a Total Safe-Care Fitness Solution based on a multimodal COVID-19 tracking system integrating computer vision and data from wearable sensors. We propose a framework with three areas that need to be integrated: a COVID-19 vaccine and health status recognition system (QR code scan prior to entry to the gym, and physiological signals monitored by a smart-band and a health questionnaire filled in prior to entry to the gym); an accident detection system (video and smart-band based); and a gym-user digital tracking system (CCTV and smart-band based). We show the proposed architecture for the integration of these systems and provide practical tips on how to implement it in testbeds for feasibility testing. To the best of our knowledge, this is the first proposed COVID-19 tracking system of use in gyms that includes a predictive model for accident detection for safer exercise participation through health monitoring.
