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智能物理治疗:通过PoseNet和ensemble模型推进基于手臂的运动分类

Shahzad Hussain¹, Hafeez Ur Rehman Siddiqui¹, Adil Ali Saleem¹

¹Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan.

Sensors (Basel, Switzerland)

|October 16, 2024

概括

这项研究通过使用AI姿势估计来准确分类练习来增强远程物理治疗. 一个新的RandomLightHist Fusion模型实现了99.6%的准确性,改善了远程患者监控.

科学领域:

康复医学康复医学康复医学
计算机科学计算机科学
人工智能的人工智能

背景情况:

远程物理治疗对于远程医疗提供至关重要,特别是在COVID-19后.
准确的运动分类对于有效的远程物理治疗至关重要.
现有的方法需要改进,以适应不同的用户和练习.

研究的目的:

开发一个人工智能系统,准确实时分类理疗练习.
评估各种机器学习模型对此任务的有效性.
引入新型组合模型,以提高分类性能.

主要方法:

收集了49名参与者执行七种不同的运动的运动数据.
利用PoseNet和谷歌MediaPipe提取了12个解剖学地标和每个地标的四个特征.
使用并比较基于树的分类器 (随机森林,XGBoost等). 和两个新的组合模型 (随机LightHist融合,堆叠XLightRF).

主要成果:

通过RandomLightHist Fusion模型实现了99.6%的优异分类准确度.
该系统在不同的身体类型和炼变化中表现出强度.
整体模型显著提高了个人分类器的性能.

结论:

开发的系统为远程物理治疗中实时运动分类提供了高度准确和有效的解决方案.
这项技术可以显著提高远程患者监测和反.
这些发现支持人工智能的整合,以改善远程康复的结果.

关键词:

谷歌的MediaPipe可以使用.这是PoseNet的PoseNet.组合模型组合模型组合模型练习分类进行分类.医疗保健技术医疗保健技术医疗保健技术机器学习是机器学习.远程物理治疗治疗

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