A Smart Glove to Track Fitness Exercises by Reading Hand Palm
A Smart Glove to Track Fitness Exercises by Reading Hand Palm
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DOI：
10.1155/2019/9320145
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发表时间：
2019-01-01
影响因子：
1.9
通讯作者：
Yasumoto, Keiichi
中科院分区：
文献类型：
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作者：
Akpa, A. H. Elder;Fujiwara, Masashi;Yasumoto, Keiichi
医学研究已经充分证明，体育活动可以增强从业人员的身心健康。近年来，健身活动成为激励和吸引人们参与体育活动的最常见方式。最近，人们多次尝试详细阐述基于物联网的理想解决方案，以跟踪和评估这些健身活动。大多数健身活动（除了有氧运动，如跑步）涉及运动员的手掌和身体之间或手掌和锻炼材料之间的一个或多个相互作用。在这项工作中，我们提出了我们的想法，利用手掌的这些生物力学相互作用，通过智能手套跟踪健身活动。我们基于智能手套的系统将力敏电阻（FSR）传感器集成到可穿戴健身手套中，通过分析健身过程中观察到的手掌压力分布的时间序列来识别和计算健身活动。为了评估我们提出的系统的性能，我们进行了一项实验研究，10名参与者超过10个常见的健身活动。对于依赖于用户的活动识别的情况下，实验结果显示，整体活动识别的F分数为88.90%。留一参与者交叉验证的结果显示，F值在58.30%~ 100%之间，平均值为82.00%。对于运动重复计数，系统实现了9.85%的平均计数误差，标准差为1.38。
Medical studies have intensively demonstrated that sports activity can enhance both the mental and the physical health of practitioners. In recent years, fitness activity became the most common way to motivate and engage people in sports activity. Recently, there have been multiple attempts to elaborate on the ideal IoT-based solution to track and assess these fitness activities. Most fitness activities (except aerobic activities like running) involve one or multiple interactions between the athlete's hand palms and body or between the hand palms and the workout materials. In this work, we present our idea to exploit these biomechanical interactions of the hand palms to track fitness activities via a smart glove. Our smart glove-based system integrates force-sensitive resistor (FSR) sensors into wearable fitness gloves to identify and count fitness activity, by analyzing the time series of the pressure distribution in the hand palms observed during fitness sessions. To assess the performance of our proposed system, we conducted an experimental study with 10 participants over 10 common fitness activities. For the user-dependent activity recognition case, the experimental results showed 88.90% of the F score for overall activity recognition. The result of leave-one-participant-out cross-validation showed an F score ranging from 58.30% to 100%, with an average of 82.00%. For the exercise repetition count, the system achieved an average counting error of 9.85%, with a standard deviation of 1.38.