本論文主旨在於開發一基於慣性感測器之平衡分析系統，並將其應用於阿茲海默氏症之徵狀評估。首先，本論文設計靜/態動態平衡測試來測試受測者的平衡能力；同時，本論文發展穿戴式慣性感測裝置並將其配戴於受測者腰部、腳踝及腳背上偵測靜/態動態平衡測試時所產生的運動訊號，並透過訊號處理得到受測者在執行測試時維持平衡的能力。接著，本論文提出認知功能評估方法，其可以擷取慣性感測器所偵測到運動訊號的特徵，並依據擷取後的特徵及受測者傳統認知功能測驗分數來建立映射回歸模型，並藉此來建立受測者平衡能力與認知功能的關係。此外，本論文亦提出一基於支持向量機之阿茲海默氏症自動分類演算法，利用慣性感測器所偵測到運動訊號進行訊號前處理、特徵擷取、特徵正規化、特徵選取後，藉由支持向量機分類器來將阿茲海默氏症患者和正常人區分。最後，經由結果顯示，本論文所提出之認知功能評估方法中其平衡能力與認知功能呈現高度相關性(相關係數>0.7)；並且在阿茲海默氏症辨識方面其可達到82.76%的準確率。透過上述結果可以驗證本論文所提出平衡分析系統之有效性。我們希冀此論文中所提出之靜/態動態平衡分析系統在未來能夠成為輔助醫師在臨床上診斷阿茲海默氏症之工具。

This thesis presents an effective inertial-sensor-based balance analysis system for patients with Alzheimer’s disease. At first, we designed accurate balance testing procedures for examining participants’ balance ability. In the meanwhile, the signals generated from balance movements can be acquired via the use of a wearable device mounted on participants’ waist, ankles, or feet. Next, the proposed cognitive function assessment method was presented to establish the relationship between performance in balance tasks and cognitive function by using the parameters or features calculated from the inertial signals, consisting of signal preprocessing, parameter or feature generation, and cognitive function assessment. Afterward, we developed an automatic Alzheimer’s disease classification algorithm including signal preprocessing, feature extraction, feature normalization, feature selection, and support vector machine-based classifier to separate AD patients from healthy people. The results showed that cognitive function and balance ability appear to be highly related (correlation coefficient > 0.7). Furthermore, the average classification accuracy of the proposed automatic classification algorithm is 82.76%. Therefore, our experimental results have successfully validated the proposed balance analysis system. We expect the proposed inertial-sensor-based balance analysis system can identify diagnostic marker(s) for AD in the early stage.

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