利用机器学习和腕带对手臂运动进行分类和监测

IF 5 3区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Aamer Bilal Asghar , Maham Majeed , Abdullah Taseer , Muhammad Burhan Khan , Khazina Naveed , Mujtaba Hussain Jaffery , Ahmed Sayed Mohammed Metwally , Krzysztof Ejsmont , Mirosław Nejman
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引用次数: 0

摘要

运动对健康的生活方式至关重要,因此,考虑如何在家中进行手臂运动时保持正确的姿势非常重要。这项工作使用带有 MPU6050 传感器的腕带来解决这些问题,该传感器通过加速度测量来收集运动数据。数据集中的个人在不同的运动范围内完成各种活动。在对收集到的数据进行预处理和特征提取后,将应用基于机器学习的分类方法。我们创建了一个集成了 WiFi 模块和云基础设施的应用程序原型,以实现实时数据收集和存储。Arduino IDE 用于将收集到的数据发送到 ThingSpeak 平台,然后再发送到 MATLAB 进行额外分析。研究数据随后返回 ThingSpeak,由程序显示研究结果。这种方法可以降低人们因姿势不良而受伤的风险,使他们无需私人教练或特定环境就能在家中继续定期锻炼。这项工作的研究结果对利用带有 MPU6050 传感器的腕带进行手臂锻炼的 Boosted Trees、Quadratic SVM、Subspace KNN 和 Fine KNN 算法的性能产生了重要影响。在所有算法中,Fine KNN 的准确率最高,达到 91.3%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification and monitoring of arm exercises using machine learning and wrist-worn band

Exercise is essential for a healthy lifestyle, thus it is important to consider how to keep proper posture when performing arm exercises at home. This work uses wrist-worn bands with the MPU6050 sensor to address these issues, which collects motion data using acceleration measurements. The individuals in the dataset are completing a variety of activities at varying ranges of motion. Machine learning-based classification methods are then applied after the pre-processing and feature extraction of the gathered data. An App prototype integrated with a WiFi module and Cloud infrastructure is created to enable real-time data collecting and storage. The Arduino IDE is used to send the collected data to the ThingSpeak platform, where it is subsequently sent to MATLAB for additional analysis. The studied data is then returned to ThingSpeak, where the program displays the findings. This approach reduces the risk of injuries caused by bad posture by enabling people to continue regular workouts at home without requiring a personal trainer or a particular environment. The findings of this work shed important light on the performance of Boosted Trees, Quadratic SVM, Subspace KNN, and Fine KNN algorithms for arm exercises employing a wrist-worn band with an MPU6050 sensor. The Fine KNN has the highest accuracy of 91.3% among all implemented algorithms.

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来源期刊
Egyptian Informatics Journal
Egyptian Informatics Journal Decision Sciences-Management Science and Operations Research
CiteScore
11.10
自引率
1.90%
发文量
59
审稿时长
110 days
期刊介绍: The Egyptian Informatics Journal is published by the Faculty of Computers and Artificial Intelligence, Cairo University. This Journal provides a forum for the state-of-the-art research and development in the fields of computing, including computer sciences, information technologies, information systems, operations research and decision support. Innovative and not-previously-published work in subjects covered by the Journal is encouraged to be submitted, whether from academic, research or commercial sources.
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