简单:使用可穿戴计算实时分析阻力训练.
William Coates1, Johan Wahlström2
1Independent Researcher, Bath BA1 2TP, UK.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究介绍了LEAN,这是一款新的抵抗训练应用程序,它使用机器学习进行实时运动分析. 它提供了超越简单的体重和重复的高级指标,改进了炼跟踪.
科学领域:
- 体育科学 运动科学 运动科学
- 可穿戴技术可穿戴技术
- 在健身方面使用机器学习.
背景情况:
- 健身应用程序通常会跟踪心脏运动,但缺乏抵抗训练的高级功能.
- 目前的抵抗训练应用程序主要依赖于用户手动输入基本数据,如重量和重复.
- 在抵抗运动跟踪中需要复杂的,自动化的分析.
研究的目的:
- 介绍LEAN,一个用于抵抗训练分析的iPhone和Apple Watch应用程序.
- 开发机器学习驱动的功能,用于实时练习表单分析和重复计数.
- 为了结合先进的运动指标,如运动范围和重复时间.
主要方法:
- 使用机器学习算法开发LEAN应用程序.
- 在资源有限的设备 (iPhone,Apple Watch) 上实时反的轻量级推断方法的实施.
- 通过用户调查和比较地面真相数据和商业应用程序进行绩效评估.
主要成果:
- 瘦提供实时形式分析,自动重复计数,和新的运动指标 (运动范围,重复时间).
- 轻量级推断使得有效的设备内处理可用于立即的用户反.
- 用户调查和基准数据验证了应用程序的性能和实用性.
结论:
- 简单系统在自动化抵抗训练分析方面取得了重大进展.
- 机器学习和高效的算法使可穿戴设备上的实时,详细的运动反成为可能.
- 这项技术有可能提高用户抵抗训练的有效性和准确性.
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