一个深度学习系统,以监控和评估在家庭远程和无监督条件下进行康复练习
Ciro Mennella1, Umberto Maniscalco1, Giuseppe De Pietro1
1Institute for High-Performance Computing and Networking (ICAR) - Research National Council of Italy (CNR), Italy.
Computers in biology and medicine
|September 24, 2023
概括
本研究提出了一种具有成本效益的系统,用于实时监测和评估身体康复练习,改善运动质量评估. 它准确地分类了运动范围和补偿模式,增强了患者的护理.
科学领域:
- 生物医学工程 生物医学工程
- 康复科学 康复科学 康复科学
- 医疗保健中的机器学习
背景情况:
- 机器学习和运动捕捉方面的进步使人类运动的自动分析成为可能.
- 目前的康复评估方法可以通过客观的,以技术为导向的评估来补充.
研究的目的:
- 引入一个新的,负担得起的实时监测和质量评估康复练习的系统.
- 评估系统在分类运动范围 (ROM) 和识别补偿模式方面的有效性.
主要方法:
- 开发一个经济上可行的运动监测和分析系统.
- 获取一个独特的数据集,包括6个阻力训练练习.
- 实现ROM分类和补偿模式识别算法.
主要成果:
- 该系统在运动监测和运动评估方面表现出高精度.
- 在ROM类评估中达到89%的平均准确率,在补偿模式分类中达到98%的平均准确率.
- 验证了系统提供精确洞察力的能力,以练习执行偏差.
结论:
- 开发的系统在物理康复评估方面取得了重大进展.
- 它有可能增强临床和家庭康复计划.
- 整合这项技术可以改善患者的治疗结果和获得优质护理的机会.
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