2021
DOI: 10.3389/fdgth.2021.679630
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Design and Evaluation of an Online Squat Fitness System: Lessons Learned During the Early COVID-19 Pandemic in Japan

Abstract: COVID-19 has changed our lives and limited our ability to have adequate physical activity (PA). It is necessary to replace outdoor PA with home-based fitness. However, people lack access, skills, and even motivation for home-based fitness. To address these issues, we designed a free access self-monitoring and coaching and music-based interactive online squat fitness system. Body weight squat was utilized for fitness exercise and evaluated based on three indices: knee width, hip depth, and rhythm. An online sur… Show more

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Cited by 4 publications
(3 citation statements)
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“…The subjects in that study performed 20 exercise sessions by following the device guide on a laptop and reported that those exercises improved the physical and mental well-being; however, there was no control group. Further, Wang et al [27] reported regarding an original online exercise evaluation system, which effectively estimated the techniques of squat exercise in young, middle-aged, and older populations. Overall, such technologies may motivate general populations and give rise to meaningful health-oriented content within online exercise programs not only for young populations but also for the middle-aged and older populations.…”
Section: Discussionmentioning
confidence: 99%
“…The subjects in that study performed 20 exercise sessions by following the device guide on a laptop and reported that those exercises improved the physical and mental well-being; however, there was no control group. Further, Wang et al [27] reported regarding an original online exercise evaluation system, which effectively estimated the techniques of squat exercise in young, middle-aged, and older populations. Overall, such technologies may motivate general populations and give rise to meaningful health-oriented content within online exercise programs not only for young populations but also for the middle-aged and older populations.…”
Section: Discussionmentioning
confidence: 99%
“…MoveNet is a lightning-fast and highly accurate model that detects the body’s 17 key points; BlazePose can detect 33 keypoints, and PoseNet can detect multiple poses, each of which includes 17 key points. Works on Pose-detection can be found in [ 20 , 32 , 81 , 85 , 92 , 96 , 96 , 98 , 106 , 116 , 124 ] BodyPix This is a body segmentation model that segments 24 body components from a background image or video in real-time, and it also works for multiple people. Its design is based on either MobileNetV1, a smaller but less precise model, or ResNet50, a bigger but more precise model.…”
Section: Front-end Deep Learning Development Approachmentioning
confidence: 99%
“…According to them, a single RGB camera may miss certain essential information, and incorrect body movement during workouts may also be overlooked. A self-monitoring and coaching system for online squat fitness is also designed in [ 124 ].…”
Section: Front-end Deep Learning Web Appsmentioning
confidence: 99%