本篇論文目的為研製一基於慣性感測器之步態分析系統，並將此系統作為阿茲海默氏症患者分類之應用。因此，我們透過配戴於受測者腳背上之穿戴式慣性感測器，偵測受測者執行單一任務／多重任務行走測試時的運動訊號。首先，本論文提出自動化步態分析演算法，其利用慣性感測訊號進行訊號前處理、步幅偵測及步態週期偵測後，獲得行走步數、行走時間、步長、步頻、步伐速度、步伐節奏、步幅時間、站立期時間、擺動期時間、步幅時間變異性、站立期時間變異性、擺動期時間變異性、站立期百分比、擺動期百分比等步態參數，藉此分析阿茲海默氏症患者與健康受測者在步態參數上的差異性。其次，本論文開發自動化阿茲海默氏症分類演算法，其利用慣性感測器所收集的訊號，並且藉由機率類神經分類器來加以辨識受測者是否屬於阿茲海默氏症族群，此演算法包含訊號前處理、步幅偵測、特徵擷取、特徵正規化、特徵選取、辨識器建置。結果顯示本論文提出之自動化步態分析演算法於單一任務／多重任務行走所獲得之步態參數在阿茲海默氏症患者與健康受測者間具有顯著的差異性；而利用自動化阿茲海默氏症分類演算法於多重任務行走情況下，其阿茲海默氏症辨識率為82.34%。經由單一任務／多重任務行走測試實驗結果，可以驗證此步態分析系統是一個有效的工具，期盼此系統可以輔助醫生診斷阿茲海默氏症患者。

This thesis develops a useful inertial-sensor-based gait analysis system and applies the proposed instrument for Alzheimer’s disease classification. Therefore, we detected the walking-motion signals through inertial sensor devices mounted on participants’ feet during single- and dual-task walking tests. Firstly, we presented an automatic gait analysis algorithm composed of data collection, signal preprocessing, stride detection, and gait cycle detection to find out significant gait parameters between Alzheimer’s disease patients (AD) and healthy controls (HC) via the statistical analysis results. Secondly, we developed an automatic Alzheimer’s disease classification algorithm for classifying a participant whether is an AD patient or not, which consists of signal preprocessing, stride detection, feature extraction, feature normalization, feature selection, and classifier construction. According to the experimental results, the differences in most of the calculated gait parameters between AD group and HC group were significant. In addition, the proposed automatic classification algorithm achieved the accuracy of 82.34% in the dual-task walking. We could validate the proposed gait analysis system through the walking tests, and expect the system to be a useful tool for diagnosis of AD in the early stage.

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