使用深度学习的视频估计的峰值跳跃功率与成年人的肉症和低身体性能有关
Sang Wouk Cho1,2, Sung Joon Cho1, Eun-Young Park1
1Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, Korea.
概括
无标记视频分析准确地估计了峰值跳跃功率 (vJP),显示了与地面真相跳跃功率 (gJP) 的强烈一致. 这项技术与肉症和肌肉健康有关,可用于日常生活监测.
科学领域:
- 生物力学 生物力学
- 老年学是一门学科.
- 人工智能的人工智能
背景情况:
- 低峰跳跃功率与老年人肉症,功能衰退和骨折风险有关.
- 用于测量跳跃功率的地面力板 (GFP) 仅限于研究环境,阻碍了临床应用.
- 基于视频的跳跃功率估计为更广泛的临床应用提供了潜在的解决方案.
研究的目的:
- 使用深度学习评估视频估计的峰值跳跃功率 (vJP) 的准确性.
- 评估vJP与肉症,年龄和肌肉参数的关联.
- 确定无标记跳跃功率监测的临床可行性.
主要方法:
- 在骨质疏松症诊所收集未来数据 (2022年3月至8月).
- 在三次跳跃尝试期间同时录制视频和GFP测量跳跃功率 (gJP).
- 测量手握强度 (HGS),椅子上升5倍 (CRT) 和尾肌肉质量 (ALM).
- 深度学习和机器学习算法用于vJP估计,80%用于培训,20%用于测试.
- 根据"朝鲜麻症工作组2023年标准"定义的麻症.
主要成果:
- 分析了220名患者的658次跳跃运动 (平均年龄62岁,77%是女性,19%是肉症患者).
- 在vJP和gJP之间有很高的一致性 (相关系数为0.93,平均差异为0.27W/kg).
- vJP在检测低跳力时表现出81.8%的灵敏度和91.3%的特异性.
- vJP与年龄,HGS和CRT性能相关,类似于gJP.
- vJP显著与肉,HGS和CRT有关,肩膀,部,膝盖和耳朵是关键的地标.
结论:
- 视频估计的峰值跳跃功率 (vJP) 与地面真相跳跃功率 (gJP) 强烈一致.
- vJP是评估跳跃功率的可行工具,与肉,年龄和肌肉功能参数相关.
- 峰值跳跃功率的无标记视频监控对于日常生活应用是可行的.
相关概念视频
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