基于纠错输出代码 (ECOC) 和卷积神经网络 (CNN) 的运动活动 (SA) 识别
Lu Lyu1, Yong Huang2
1Shandong University of Aeronautics, BinZhou, Shandong, 256600, China.
Heliyon
|March 28, 2024
概括
这项研究引入了一种新的机器学习模型,用于使用脚部安装的传感器准确识别运动活动. 该方法达到99.71%的准确性,在区分体育和日常活动方面表现优于现有的技术.
科学领域:
- *体育科学和生物力学
- * 机器学习和人工智能
- * 可穿戴式传感器技术
背景情况:
- *运动传感器越来越多地用于活动监控,特别是在体育运动中.
- *目前基于传感器的系统难以将特定的运动活动与日常活动区分开来.
- *精确的体育活动识别对于运动员监测和训练分析至关重要.
研究的目的:
- * 开发一种新的机器学习模型,用于增强体育活动识别.
- * 准确区分各种体育活动和日常人类运动.
- * 提高基于传感器的活动检测系统的精度和回忆.
主要方法:
- * 用了一台加速度计和陀螺仪连接到脚部收集数据.
- * 应用于信号特征提取的短时间里叶变换 (STFT).
- * 使用卷积神经网络 (CNN) 进行运动特征分析.
- * 用错误纠正输出代码 (ECOC) 模型进行最终活动分类.
主要成果:
- * 拟议的模型在体育活动识别方面实现了99.71%的高精度.
- * 在DSADS数据库上获得了99.72%的精度和99.71%的回忆.
- *与现有方法相比,在区分活动方面表现出优异的表现.
结论:
- *开发的机器学习方法显著提高了体育活动识别准确度.
- *与STFT,CNN和ECOC相结合的脚部安装传感器提供了一个强大的解决方案.
- * 该方法为体育科学和训练中的客观评估提供了可靠的工具.
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