抵抗训练中的运动分类:对技术方法的系统审查
Tayah R Brennan1, Jonathon Weakley2,3,4, Rich D Johnston1,5,6
1School of Behavioural and Health Sciences, Australian Catholic University, Brisbane, 1100 Nudgee Road, Banyo, QLD, 4014, Australia.
Sports medicine (Auckland, N.Z.)
|July 31, 2025
概括
传感器技术可以准确地分类阻力训练练习,惯性测量单位显示最有前途. 然而,消费级设备往往缺乏科学验证,因此需要谨慎.
科学领域:
- 运动科学 运动科学
- 生物机械工程 生物机械工程
- 运动技术技术 运动技术
背景情况:
- 耐力训练的目标跟踪正在通过传感器技术进步.
- 当前传感器在分类特定练习中的准确性各不相同.
- 运动变量表征的商业要求需要证据综合.
研究的目的:
- 系统地审查用于抵抗训练中运动预测的方法.
- 为了比较各种传感器技术和模型的预测性能.
主要方法:
- 在四个数据库中系统搜索相关研究.
- 包括测量抵抗训练动力学/动力学的开发/验证研究.
- 使用CHARMS进行数据提取,使用PROBAST进行偏差风险评估.
- 包括44项研究 (2项验证,42项开发/验证).
主要成果:
- 评估的技术包括IMU,加速度计,EMG,摄像头等.
- 惯性测量单位 (IMU),特别是在手腕上,在各种练习中表现出高精度.
- 像EMG和智能材料这样的可穿戴传感器也显示出很好的准确性.
- 外部设备具有准确性,但实际限制.
- 练习相似性显著影响了分类准确性.
结论:
- 标准化分类问题对于明确性和未来的研究至关重要.
- 开发强大的技术,用于多样化,类似的演习是一个优先事项.
- 精确的基于传感器的运动分类是可行的,但最终用户的可用性是有限的.
- 消费者应警未经验证的商业传感器技术.
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