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A Real-Time Intelligent Biofeedback Gait Patterns Analysis System for Knee Injured Subjects

机译：膝关节受伤对象实时智能生物反馈步态模式分析系统

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This study presents a real-time visualization system of gait patterns of knee injured subjects for biofeedback monitoring and classification. The developed system includes non-invasive wireless body-mounted motion sensors for kinematics measurements of lower extremities, surface electromyography (EMG) system for relevant specific muscle activity measurements, a motion capture system for recording trial activities and custom-developed intelligent system software implemented using LabVIEW and MATLAB. The real-time biofeedback system provides a visual monitoring of individual and superimposed signals (kinematics, EMG and video data) in order to identify the knee joint abnormality and muscles strength during various ambulation activities performed by the subjects. It can facilitate the clinicians, physiotherapists and physiatrists in determining the impairments in the gait patterns the knee injured based on the data collected and identifying the subjects lacking behind the desired level of recuperation.
机译：这项研究提出了一种实时可视化的膝盖受伤受试者步态模式的可视化系统，用于生物反馈监测和分类。开发的系统包括用于下肢运动学测量的非侵入性无线人体运动传感器，用于相关特定肌肉活动测量的表面肌电图（EMG）系统，用于记录试验活动的运动捕捉系统以及使用此工具实现的定制开发的智能系统软件LabVIEW和MATLAB。实时生物反馈系统可对单个信号和叠加信号（运动学，EMG和视频数据）进行视觉监控，以便在受试者进行的各种步行活动中识别膝关节异常和肌肉力量。它可以帮助临床医生，物理治疗师和物理治疗师根据收集到的数据确定膝盖受伤的步态模式是否受损，并识别出缺乏理想康复水平的对象。

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