本研究旨在開發一虛擬實境與外骨骼機構整合的上肢復健系統，以應
對肩關節沾黏、肩夾擠症或是肩關節運動傷害會造成的肩、肘關節運動範
圍受限。由於傳統復健方法存在人力資源需求高、以及時間與空間限制等
問題，本系統旨在提供患者虛擬實境復健選項，在硬體部分提出具有遠端
運動中心機構的上肢外骨骼輔具，且具有關節運動角度量測、並可偵測聳
肩等代償動作之功能。硬體方面感測器測得的角度訊號透過 Arduino 介面
的訊號處理與控制系統傳輸以及拉線感測器讀取手部位置與確認是否有動
作代償。軟體方面則透過 Unreal 遊戲引擎設計視覺畫面的動作引導與關節
運動範圍確認回饋，並以遊戲形式引導患者執行復健動作。而遊戲部分測
定肩外展/內收、肩伸展/屈曲、肩外轉/內轉以及肘伸展/屈曲之復健動作，
且將上述動作結合於三個遊戲當中，並將量測的關節極限角度紀錄於後台
檔案之中，在後續可作為醫療的參考依據。藉由遊戲的獎勵機制提高患者
對復健的動力，同時降低對專業醫療人力的依賴。量測之關節運動數據可
便於醫師追蹤患者康復狀況，提高醫療系統的靈活性、可及性和運作效能。
在實驗方面，利用光學影像追蹤軟體換算人體運動角度，並與角度感測器
的讀取數值比對，發現因機構與人體肩關節會自然抬升，導致誤差值上升，
對此利用方程式補正，使最大誤差值降至 8%以下。其後，對兩位受測者進
行相同實驗，以確保方程式和裝置設計的可信度，結果顯示誤差值也在 7%
V
以下。此外，加入肩關節抬舉高度與運動角度的關係式，進一步提高裝置
的準確性。

This research aims to develop an upper limb rehabilitation system that
integrates virtual reality and exoskeleton mechanisms to address limitations in
shoulder and elbow joint mobility caused by conditions such as shoulder adhesion,
shoulder impingement syndrome, or shoulder sports injuries. Due to the high
demand for human resources and time and space constraints in traditional
rehabilitation methods, this system aims to provide patients with a virtual reality
rehabilitation option.
The hardware component includes an upper limb exoskeleton device with
joint motion angle measurement and shoulder shrugging detection capabilities.
The angle signals captured by sensors are processed and controlled through an
Arduino interface, while a string sensor reads hand position and checks for
compensatory movements.
On the software side, the Unreal game engine is employed to design visual
interfaces for guiding movements and providing feedback on joint motion ranges.
The system utilizes a gamified approach to guide patients through rehabilitation
exercises. The games measure shoulder abduction/adduction,shoulder
extension/flexion , shoulder external/internal rotation, and elbow
extension/flexion. These movements are incorporated into three games, with the
measured joint limit angles recorded in backend files for future medical reference.
The game's reward mechanism aims to increase patients' motivation for
rehabilitation while reducing dependence on professional medical personnel. The
VII
collected joint motion data facilitates tracking patient recovery progress and
enhances the flexibility, accessibility, and efficiency of the healthcare system.
In the experimental phase, optical tracking software was used to calculate
human body motion angles and compare them with sensor measurements. It was
found that due to the natural elevation of the shoulder joint during mechanism
movement, error values increased. To address this, a correction equation was
applied, reducing all error values to below 8%. Subsequently, the same
experiment was conducted on two subjects to ensure the reliability of the equation
and device design. Results showed error values below 7%. Additionally, a
relationship between shoulder joint elevation height and motion angle was
incorporated to further improve device accuracy.

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