传感器输入类型和位置影响户外运行地形通过深度学习方法进行分类
Gabrielle Thibault1, Philippe C Dixon1, David J Pearsall1
1Department of Kinesiology and Physical Education, McGill University, Montreal, QC H2W 1S4, Canada.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
研究人员发现,使用脚上安装的惯性测量单元 (IMU) 传感器的加速信号,结合步态周期分析,可以准确地分类像草地和青这样的跑步表面.
科学领域:
- 生物力学和人类运动分析
- 机器学习在体育科学中的应用
- 可穿戴式传感器技术
背景情况:
- 优化跑步性能和预防伤害需要了解不同的跑步表面如何影响生物力学.
- 深度学习模型,特别是卷积神经网络 (CNN),显示了使用身上的传感器对活动进行分类的前景.
- 没有先前的研究优化了信号类型,传感器位置和模型架构来分类运行表面.
研究的目的:
- 确定信号类型,传感器位置和CNN架构的最佳组合,以准确地分类草地和青跑道.
- 评估预处理步骤和数据分割协议对分类准确性的影响.
主要方法:
- 收集了40名在草地和青表面跑步者的全身惯性测量单元 (IMU) 数据.
- 测试了各种信号类型 (加速,角速度),传感器配置 (全身,下身,骨盆,脚) 和CNN架构.
- 评估了预处理 (步行周期分离,振幅规范化) 和数据分割方法 (离开-n-主体排除,主体依赖) 的影响.
主要成果:
- 加速信号表现优于角速度,将分类提高了3.8%.
- 脚传感器配置实现了最高的准确性 (95.5%),相对于使用的传感器数量.
- 将数据分为步行周期,避免振幅正常化,可显著提高准确度约28%.
结论:
- 已经确定了开发有效的机器学习分类器的关键参数,用于识别人类活动.
- 跑步表面分类工具可以为运动员和教练提供有价值的反,以培训个性化和预防伤害.
- 这项技术有可能通过提供对不同地形的技术和努力的定量见解来提高跑步性能.
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