在臨床上，中風病患的步態分析一直是一項常見的研究，尤其步態的不對稱性是很多研究的主要目標。不對稱的原因在於中風患者的患側承重能力較弱，在步態訓練和評估中為主要復健目標。在改善中風病患的不對稱步態訓練中，跑步機結合懸吊系統的步態復健更是常見。但在一般的步態分析實驗，需要較多的人力與實驗儀器，例如攝影機和力板等等。因此本實驗的目的是為了建立一套即時與簡易的步態分析系統，用來代替傳統的步態分析實驗儀器並節省人力。此步態分析系統是結合懸吊系統(SportsArt)所建立的步態對稱性評估軟體。我們在懸吊系統繩索上方架設一顆單軸的荷重元感應器(Load cell)，當受試者在使用懸吊系統的狀況下於跑步機上行走路時，身體中心垂直移動會帶動繩索上下動作，進而藉由軟體量測到走路時懸吊力量的變化。在此軟體中，我們藉由感測器的訊號與人體重心垂直變化的關係，建立了三個步態對稱性評估參數，(1) unweighting ratio: 健患側荷重元力量變化比, (2)COM excursion time ratio: 健患側荷重元力量最大值的時間差比值,和(3)leading limb unweighting ratio: 健患側荷重元力量最大值比值。三者皆是患側與健側的比值。因此在建立這套軟體之後，我們招募15位健康受試者，3位中風病患進行軟體驗證。15位健康受試者的數據主要是進行軟體確效，軟體驗證實驗過程中，架設儀器包含三維動作分析系統、跑步機、懸吊系統和資料擷取器。三維動作分析系統主要建立與三個評估參數相關的垂直重心參數: (1)健患側重心垂直位移量比值，(2) 健患側重心垂直位移時間差比值，和(3) 健患側重心垂直位置最小值比值，並計算出誤差觀察此三項參數和重心相關值的誤差值。並使用不同已知重量的物品以及受試者的步頻驗證軟體中力量的大小與頻率的正確性。而整個實驗過程中，健康受試者測試條件包含20%和40%懸吊重量，而中風病患則20%懸吊重量。將實驗結果分析後，發現重心垂直位移曲線和荷重元的力量曲線呈現負相關。而在軟體確效完後，發現已知重量物品的重量值與軟體量測到的力量值平均誤差為4.85%，步頻與力量頻率的平均誤差為0.63%，並且各項評估參數平均誤差值皆小於4% ，而且中風患者也可以從三項參數看出不對稱的結果。

In the daily activities, gait is the locomotion achieved by repetitive movement of lower limbs of human. In clinical rehabilitation, normal gait is a major goal in the training program for patients. Among the rehabilitation programs, walking with body weight support is one of the approaches which have been commonly used for gait training. In order to evaluate the improvements of patients’ gait performance, several techniques have been developed. However, traditional gait analysis requires complicated experimental setup. Therefore, the current study developed a novel gait analysis system based on an unweighting system. Three parameters were defined in the software: (1) unweighting ratio: the difference ratio of load between paretic and nonparetic side, (2) COM excursion time ratio: the time difference ratio of peak point in the load cell signal between paretic and nonparetic side, (3) leading limb unweighting ratio: the peak value ratio of load between paretic and nonparetic side. Verification of the software was done by verify the load value and frequency with known weights and cadence of subjects. In addition, validation tests were performed by calculating error between three software parameters and relative vertical COM parameters: (1) displacement ratio of vertical COM, (2) time difference ratio of lowest point for vertical COM movement, (3) lowest position ratio of vertical COM movement. In this study, experimental equipment included suspension system with load cell, motion capture system, and treadmill. The test content was walking on the treadmill with 20% and 40% body weight support for healthy and only 20% body weight support for stroke patients. There was a negative correlation between vertical COM displacement and load cell signal in one gait cycle in one gait cycle. The verification result showed the mean error of weight stuff and load value was 4.85%, and the mean of load frequency and cadence were 0.63%. In addition, validation result showed all the mean errors of three software parameters and relative parameters vertical COM were less than 4%. In terms of the relation of vertical COM movement and load cell signal in one gait cycle, it helped us build all evaluation parameters. After verification and validation, we could observe that the mean errors of several parameters compared with parameters calculated from data of motion capture system were less so we maybe were able to use these parameters to evaluate gait asymmetry of patients. In the future, recruiting more subjects to verify the usable characteristic of the software was important and necessary.

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