基于传感器的多用途可穿戴重量训练系统

Parinaz Balkhi, M. Moallem
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引用次数: 4

摘要

近年来,人们对体育活动的自动跟踪和检测越来越感兴趣。研究人员已经证明,在个人锻炼过程中提供活动信息可以极大地帮助他们实现锻炼目标。特别是,这些信息将帮助他们最大限度地提高锻炼效率,防止过度伸展和过度训练。本文介绍了一种新型的多用途可穿戴设备的开发,用于自动体重检测,活动类型识别和计数重复,如重量训练等体育活动。该设备通过安装在手套上的惯性测量单元(IMU)、加速度计和三个力传感器来监测体重和活动,并利用开发的机器学习模型对它们进行分类。针对权重检测的目的,研究了不同的分类器,包括线性判别分析(LDA)、支持向量机(SVM)和多层感知器神经网络(MLP)。对于活动识别,我们训练并检验了K近邻(KNN)、决策树(DT)、随机森林(RF)和支持向量机(SVM)模型。实验结果表明,SVM分类器在权重检测方面具有最高的准确率,而RF分类器在活动识别方面优于其他分类器。结果表明,开发一种可穿戴设备的可行性,该设备可以在最小的物理干预下提供有关举起的重量和活动类型的原位准确信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multipurpose Wearable Sensor-Based System for Weight Training
In recent years, there has been growing interest in automated tracking and detection of sports activities. Researchers have shown that providing activity information to individuals during their exercise routines can greatly help them in achieving their exercise goals. In particular, such information would help them to maximize workout efficiency and prevent overreaching and overtraining. This paper presents the development of a novel multipurpose wearable device for automatic weight detection, activity type recognition, and count repetition in sports activities such as weight training. The device monitors weights and activities by using an inertial measurement unit (IMU), an accelerometer, and three force sensors mounted in a glove, and classifies them by utilizing developed machine learning models. For weight detection purposes, different classifiers including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Multi-layer Perceptron Neural Networks (MLP) were investigated. For activity recognition, the K nearest neighbor (KNN), Decision Tree (DT), Random Forest (RF), and SVM models were trained and examined. Experimental results indicate that the SVM classifier can achieve the highest accuracy for weight detection whereas RF can outperform other classifiers for activity recognition. The results indicate feasibility of developing a wearable device that can provide in-situ accurate information regarding the lifted weight and activity type with minimum physical intervention.
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