本論文旨在開發以慣性感測技術為主之穿戴式無線身體感測網路系統，用以開發在臨床醫學上中風患者量化評估指標。此穿戴式無線身體感測網路系統由配戴在患者上肢之身體感測網路及個人電腦所組成。我們透過上肢身體感測網路來加以獲得上肢各部位在三維空間中的運動訊號；接著進行相關演算法及人機互動介面開發。在上肢作軌跡重建演算法方面，我們開發一基於四元數的無跡式卡爾曼濾波器來降低慣性感測訊號在姿態角、速度及軌跡估測上所造成的積分誤差；在肘關節活動度量測演算法方面，透過上臂與前臂的姿態來加以獲得肘關節活動度；在上肢動作相似度比對演算法方面，我們採用了動態時間扭曲演算法來加以比對患者執行任務型導向動作時健患側手所產生的動作訊號。經由實驗結果已成功地驗證：1)本系統為一不需任何額外參考資訊且具有高度信效度的上肢動作量測工具；2)本系統無跡式卡爾曼濾波器可準確地估測上肢動作之姿態角、速度及軌跡；及3)本系統可準確地量化評估中風患者之肘關節活動度及任務型導向動作之相似度。

This thesis presents a wearable inertial-sensing-based wireless human body sensor network system for quantitative evaluation of upper limb function in patients with stroke. The system consists of an upper limb body sensor network and a personal computer. We developed related algorithms and its user-friendly human-machine interface. An upper limb motion trajectory reconstruction algorithm had been established to estimate accurate velocities, trajectories, and orientations, in which a sensor fusion algorithm based on a quaternion-based unscented Kalman filter is utilized to minimize the cumulative errors of the inertial signals. We used an elbow range of motion (ROM) algorithm to measure the ROM of the elbow joint via the orientation angles of the upper arm and forearm. In order to differentiate the similarity of motions between unaffected and affected upper limbs of patients with stroke during task-specific training in rehabilitation, a dynamic time warping (DTW) method for comparison was adopted. The following conclusions can be drawn from our present experimental results. 1) We proved that the proposed system can be used anywhere without any external reference information and shows good concurrent validity and excellent intratester reliability. 2) The unscented Kalman filter-based sensor fusion algorithm can accurately estimate orientations, velocities, and trajectories of upper limbs in patients with stroke. 3) This system can measure elbow ROM and quantitate the motion similarity for both upper limbs in patients with stroke.

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