步态分析--医学推断的工具

Sindhu K, A Nidhi Uday, Abhishek S J, Anjali S
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引用次数: 0

摘要

步态分析是进行医学推断、改善行动问题诊断和治疗的重要工具。本项目旨在利用步态分析应对两个重要挑战:检测膝关节外翻和监测帕金森病患者跌倒。该项目建议将步态分析与瑜伽疗法相结合,为矫正膝关节外翻提供一种独特而有效的方法。该项目开发了一个网络用户界面,使个人能够访问该系统,接收有关其步态的准确反馈,并访问专门针对膝关节磕碰的瑜伽姿势。此外,还设计了一个跌倒检测系统,用于监测帕金森病患者,并在患者跌倒时通知护理人员或监护人。实施过程涉及利用深度学习模型,如用于姿势估计的被广泛采用的深度学习框架 OpenPose 模型,以及另一个用于构建多模态应用机器学习管道的公认框架 MediaPipe,来分析步态模式并检测膝关节磕碰和跌倒。该项目旨在增强个人改善步态、矫正膝关节损伤和增强身体健康的能力,最终提高他们的生活质量和幸福感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gait Analysis—A Tool for Medical Inferences
Gait analysis is a valuable tool for making medical inferences and improving the diagnosis and treatment of mobility issues. This project aims to leverage gait analysis in addressing two important challenges: detecting knock knees and monitoring patients with Parkinson’s disease for falls. The project proposes the integration of gait analysis with yoga therapy to provide a unique and effective approach for correcting knock knees. A web user interface is developed to enable individuals to access the system, receive accurate feedback on their gait, and access yoga postures tailored to target knock knees. Additionally, a fall detection system is designed to monitor patients with Parkinson’s disease and notify caregivers or guardians in case of a fall. The implementation involves utilizing deep learning models, such as OpenPose model, a widely adopted deep learning framework for pose estimation and MediaPipe, another recognized framework used for building multimodal applied machine learning pipelines, to analyze gait patterns and detect knock knees and falls. The project aims to empower individuals in improving their gait, correcting knock knees, and enhancing their physical health, ultimately improving their quality of life and well-being.
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