Never skip leg day: A novel wearable approach to monitoring gym leg exercises

Bo Zhou, Mathias Sundholm, Jingyuan Cheng, H. Cruz, P. Lukowicz
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引用次数: 45

Abstract

We present a wearable textile sensor system for monitoring muscle activity, leveraging surface pressure changes between the skin and an elastic sport support band. The sensor is based on an 8×16 element fabric resistive pressure sensing matrix of 1cm spatial resolution, which can be read out with 50fps refresh rate. We evaluate the system by monitoring leg muscles during leg workouts in a gym out of the lab. The sensor covers the lower part of quadriceps of the user. The shape and movement of the two major muscles (vastus lateralis and medialis) are visible from the data during various exercises. The system registers the activity of the user for every second, including which machine he/she is using, walking, relaxing and adjusting the machines; it also counts the repetitions from each set and evaluate the force consistency which is related to the workout quality. 6 people participated in the experiment of overall 24 leg workout sessions. Each session includes cross-trainer warm-up and cool-down, 3 different leg machines, 4 sets on each machine. Plus relaxing, adjusting machines, and walking, we perform activity recognition and quality evaluation through 2-dimensional mapping and the time sequence of the average force. We have reached 81.7% average recognition accuracy on a 2s sliding window basis, 93.3% on an event basis, and 85.6% spotting F1-score. We further demonstrate how to evaluate the workout quality through counting, force pattern variation and consistency.
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永远不要错过腿部锻炼日:一种新颖的可穿戴方法来监测健身房的腿部锻炼
我们提出了一种可穿戴的纺织品传感器系统,用于监测肌肉活动,利用皮肤和弹性运动支持带之间的表面压力变化。该传感器基于8×16元件织物电阻式压力传感矩阵,空间分辨率为1cm,可以50fps的刷新率读取。我们通过在实验室外的健身房进行腿部锻炼时监测腿部肌肉来评估该系统。传感器覆盖在用户的股四头肌的下部。从各种锻炼的数据中可以看到两块主要肌肉(股外侧肌和股内侧肌)的形状和运动。系统每秒钟记录用户的活动,包括他/她正在使用哪台机器,行走,放松和调整机器;它还计算每组的重复次数,并评估与锻炼质量相关的力量一致性。6人参加了总共24次腿部锻炼的实验。每次训练包括交叉训练热身和冷却,3台不同的腿部机器,每台机器4组。再加上放松、调整机器和行走,我们通过二维映射和平均力的时间序列进行活动识别和质量评估。我们在滑动窗口的基础上达到了81.7%的平均识别准确率,在事件的基础上达到了93.3%,在f1得分的基础上达到了85.6%。我们进一步演示了如何通过计数,力模式变化和一致性来评估训练质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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