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Updated: Jun 22, 2026
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Movement Retraining using Real-time Feedback of Performance
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用于捕捉人类运动的加速度计技术与直接观测相对验证:范围审查
Elyse Letts1, Josephine S Jakubowski1,2, Sara King-Dowling3
1Child Health & Exercise Medicine Program, Department of Pediatrics, McMaster University, Hamilton, Canada.
Physiological measurement
|April 30, 2024
概括
本综述绘制了加速度计数据分析方法的物理活动,寻找机器学习和切断点方法是常见的. 穿戴位置影响分析和结果,突出了对标准化验证的需要.
科学领域:
- 运动学 运动学
- 生物医学工程 生物医学工程
- 公共卫生 公共卫生
背景情况:
- 加速度计对于测量身体活动和久坐行为至关重要.
- 加速度计技术和分析方法的快速进步给研究人员带来了挑战.
- 需要对加速度计数据处理和分析进行标准化最佳实践.
研究的目的:
- 对验证的加速仪数据分析方法进行范围审查,用于人类运动.
- 确定与直接观察相关的验证技术作为标准措施.
- 提供加速仪数据分析当前实践的概述.
主要方法:
- 对14个学术和5个灰色文学数据库进行了全面的搜索.
- 两个独立的审稿人对标题,摘要和全文进行了选.
- 数据提取和验证由独立审核人员使用Microsoft Excel进行.
主要成果:
- 该审查包括来自1039个初始搜索的115篇论文,使用71个加速度计模型和4217名参与者.
- 分析技术包括机器学习 (22%),现有切割点 (18%),切割点的ROC曲线 (14%) 和其他算法 (8%).
- 直接观察用于验证主要使用实时观察 (55%) 或记录 (42%).
结论:
- 机器学习方法越来越多地用于活动识别,而切断点方法仍然普遍存在.
- 活动强度是最常见的结果,但分析和结果因加速度计磨损位置而异.
- 本综述为研究人员提供了关于加速度计分析和验证技术的全面资源.
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