先进的生物机械分析:用于精确监测体育表现健康状况的可穿戴技术
Abdullah Alzahrani1, Arif Ullah2
1Department of Health Rehabilitation Sciences, College of Applied Medical Sciences at Shaqra, Shaqra University, Shaqra, Saudi Arabia.
Digital health
|June 6, 2024
概括
可穿戴技术和机器学习通过提供实时生物机械数据来进行个性化的运动员监测和适应性干预来增强体育物理治疗,从而改善性能指标.
科学领域:
- 运动科学 运动科学 运动科学
- 生物技术是生物技术.
- 物理治疗 物理治疗
背景情况:
- 体育物理治疗越来越多地利用技术来监测运动员.
- 可穿戴式传感器提供先进的生物机械分析.
- 机器学习可以根据运动员数据来个性化干预.
研究的目的:
- 调查可穿戴技术对体育表现监测的影响.
- 评估物理治疗中的机器学习驱动的适应性干预措施.
- 分析关键的性能指标和个体运动员的变化.
主要方法:
- 观测横截面设计与现实世界生物机械数据收集.
- 使用可穿戴传感器,包括惯性,电磁波,压力和触觉反传感器.
- 分析了使用德林体育场的巴哈瓦尔普尔运动员的数据.
主要成果:
- 随着传感器的使用,心率,关节角度和肌肉激活的量化改善.
- 突出了需要个性化策略的个体运动员变化.
- 通过可穿戴技术集成,证明了增强的监控参数.
结论:
- 可穿戴技术与生物机械分析和机器学习相结合,在物理治疗中提高了运动表现.
- 实时监控可以实现精确的,适应性干预调整.
- 机器学习显示了优化体育物理治疗结果的巨大潜力.
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