背景:由于社交距离和户外活动受到抑制而导致的身体活动不足会增加对心血管疾病等疾病的易感性,少肌症,和严重的COVID-19。当体重锻炼时,比如蹲下,有效地促进身体活动,不正确的姿势有异常肌肉激活关节劳损的风险,导致无效的会议甚至受伤。对于没有专家指导的新手来说,避免不正确的姿势是具有挑战性的。用于远程指导和计算机辅助姿势校正的现有解决方案通常证明是昂贵或低效的。
目的:这项研究旨在使用深度神经网络开发一种个人锻炼助手,该助手仅使用移动设备-智能手机和平板电脑提供有关下蹲姿势的反馈。深度学习模仿专家对正确运动姿势的视觉评估。通过将其与运动视频进行比较来评估移动应用程序的有效性,一个受欢迎的在家锻炼的选择。
方法:招募了20名没有深蹲运动经验的参与者,并根据随机对照试验将其分为实验组(EXP),其中10名年龄为21.90(SD2.18)岁,平均BMI为20.75(SD2.11),对照组(CTL),其中10名年龄为22.60(SD1.95)岁,平均BMI为18.72(SD1.23)。创建了一个包含超过20,000个由专家注释的深蹲视频的数据集,并使用姿势估计和视频分类来训练深度学习模型,以分析锻炼姿势。随后,移动锻炼助手应用程序,独自在家锻炼,被开发,以及为期2周的介入研究,其中EXP使用该应用程序,而CTL仅遵循锻炼视频,展示了该应用程序如何帮助人们改善深蹲运动。
结果:EXP在2周后显着改善了应用评估的下蹲姿势(Pre:0.20vsMid:4.20vsPost:8.00,P=.001),而CTL(无应用)显示下蹲姿势无明显变化(Pre:0.70vsMid:1.30vsPost:3.80,P=.13)。在左(Pre:75.06vsMid:76.24vsPost:63.13,P=.02)和右(Pre:71.99vsMid:76.68vsPost:62.82,P=.03)膝关节角度在运动前后,左(前:73.27vs中:74.05vs后:70.70,P=.68)和右(前:70.82vs中:74.02vs后:70.23,P=.61)膝关节角度。
结论:使用该应用程序进行培训的EXP参与者经历了更快的改善,并了解了深蹲运动的更细微细节。拟议的移动应用程序,提供具有成本效益的自我发现反馈,有效地教给用户深蹲练习,而无需昂贵的当面教练课程。
背景:临床研究信息服务KCT0008178(回顾性注册);https://cris。nih.走吧。kr/cris/search/detailSearch.do/24006。
BACKGROUND: Insufficient physical activity due to social distancing and suppressed outdoor activities increases vulnerability to diseases like cardiovascular diseases, sarcopenia, and severe COVID-19. While bodyweight exercises, such as squats, effectively boost physical activity, incorrect postures risk abnormal muscle activation joint strain, leading to ineffective sessions or even injuries. Avoiding incorrect postures is challenging for novices without expert guidance. Existing solutions for remote coaching and computer-assisted posture correction often prove costly or inefficient.
OBJECTIVE: This
study aimed to use deep neural networks to develop a personal workout assistant that offers feedback on squat postures using only mobile devices-smartphones and tablets. Deep learning mimicked experts\' visual assessments of proper exercise postures. The effectiveness of the mobile app was evaluated by comparing it with exercise videos, a popular at-home workout choice.
METHODS: Twenty participants were recruited without squat exercise experience and divided into an experimental group (EXP) with 10 individuals aged 21.90 (SD 2.18) years and a mean BMI of 20.75 (SD 2.11) and a control group (CTL) with 10 individuals aged 22.60 (SD 1.95) years and a mean BMI of 18.72 (SD 1.23) using randomized controlled trials. A data set with over 20,000 squat videos annotated by experts was created and a deep learning model was trained using pose estimation and video classification to analyze the workout postures. Subsequently, a mobile workout assistant app, Home Alone Exercise, was developed, and a 2-week interventional
study, in which the EXP used the app while the CTL only followed workout videos, showed how the app helps people improve squat exercise.
RESULTS: The EXP significantly improved their squat postures evaluated by the app after 2 weeks (Pre: 0.20 vs Mid: 4.20 vs Post: 8.00, P=.001), whereas the CTL (without the app) showed no significant change in squat posture (Pre: 0.70 vs Mid: 1.30 vs Post: 3.80, P=.13). Significant differences were observed in the left (Pre: 75.06 vs Mid: 76.24 vs Post: 63.13, P=.02) and right (Pre: 71.99 vs Mid: 76.68 vs Post: 62.82, P=.03) knee joint angles in the EXP before and after exercise, with no significant effect found for the CTL in the left (Pre: 73.27 vs Mid: 74.05 vs Post: 70.70, P=.68) and right (Pre: 70.82 vs Mid: 74.02 vs Post: 70.23, P=.61) knee joint angles.
CONCLUSIONS: EXP participants trained with the app experienced faster improvement and learned more nuanced details of the squat exercise. The proposed mobile app, offering cost-effective self-discovery feedback, effectively taught users about squat exercises without expensive in-person trainer sessions.
BACKGROUND: Clinical Research Information Service KCT0008178 (retrospectively registered); https://cris.nih.go.kr/cris/search/detailSearch.do/24006.