膝關節與腳底感測系統及其在步態分析之應用

Abstract

步態分析被用於醫療、運動等許多領域，但易受限於高價實驗室環境設備限制，較難達到隨時監測及普及化。因此本研究設計一套膝關節與腳底感測系統，藉由壓力感測鞋墊及內含慣性感測器之護膝計算步態特徵值，並對此穿戴式裝置進行硬體驗證以確保準確度。同時結合平板電腦及雲端資料庫，可收集使用者資料進行分析，並將步態特徵值(如足底壓力分佈、重心位置、膝關節角度等)視覺化即時呈現於App上，以提供較佳的視覺回饋。 應用部分我們透過此系統收集了130位、50歲以上民眾的步態資料，用以驗證本系統之可行性，並進行數據分析與討論。實驗發現足底壓力分佈跟是否患有退化性膝關節炎無直接關係，針對患有退化性膝關節炎病患而言，於行走時會盡量減少該關節之膝彎曲角度。因重心分佈較難憑直觀得知，此系統藉由壓力感測器量化使用者足底壓力分佈及重心軌跡，亦可輔助一般人正確學習強調重心轉移之運動，如太極拳等。

There is a great deal of general literature on gait analysis and detection, which are widely used in many fields such as medicine and sports. It is difficult to measure gait all the time due to being limited to expensive laboratory-based measurement. Based on this problem, our research develops a Knee-and-Sole Sensing System, which can calculate gait characteristics with wearable devices consisting of pressure sensing insoles and knee braces with 4 motion sensors. To ensure the accuracy, we also evaluate the hardware. Knee-and-Sole Sensing System combines iPad and cloud databases to collect and analyze users’ gait data. Our system also provides a real-time visual feedback to show users’ gait characteristics including plantar pressure distribution, center of pressure and knee angles. Finally, our research analyzes that the difference of gait data between with and without knee osteoarthritis (knee OA). The results show that plantar pressure distribution is no statistically significant between both of them, and if people suffer from knee pain, they will reduce the knee bending angle while they are walking as much as possible. Because plantar pressure distribution is too difficult to be identified by personal insight, we quantify plantar pressure distribution and center of pressure trajectory with force sensors. Therefore, it can be used to assist people in learning some sports which are emphasis on body weight-transfer such as Tai-Chi Chuan.