人类测量和诊断意识深度学习用于运动评估
Karla Miriam Reyes Leiva1, Pavla Nikelova1, Martin Cerny1
1Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, Ostrava, Czech Republic.
Frontiers in medical technology
|February 23, 2026
概括
一个新的AI框架,ADA (人体测量和诊断意识),准确地使用传感器数据和个人信息对和罗马尼亚死技术进行分类. 这增强了运动分析以预防伤害和个性化训练反.
科学领域:
- 生物力学 生物力学
- 机器学习 机器学习
- 可穿戴技术可穿戴技术
背景情况:
- 在诸如和罗马尼亚死 (RDL) 等力量练习中,正确的技术对于运动表现和预防伤害至关重要.
- 运动质量评估对于有效的培训和康复计划至关重要.
研究的目的:
- 引入ADA (人类测量和诊断意识),这是一个多式联网深度学习框架,用于分类运动质量和预测受伤风险.
- 整合惯性测量单元 (IMU) 动力学与人体测量和诊断功能,以进行增强的分析.
主要方法:
- 从15名健康受试者收集了17个传感器的IMU数据,他们进行了正确和不正确的坐和RDLs.
- 利用CNN-LSTM网络进行动态序列和完全连接的网络进行静态特征,将它们与注意力加权融合在一起.
主要成果:
- 在动作质量分类中,ADA框架实现了94.8%的准确性,比仅使用动力学的86.5%有所改善.
- 二元期权风险预测准确度达到了97.8%,个性化微调进一步提高了3 - 5%的性能.
结论:
- 特定对象的静态特征显著提高了运动质量分类和风险分层.
- 该ADA框架支持开发基于可穿戴设备的个性化反系统,用于培训和康复.
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