隨著使用手機時間越來越長，容易造成大拇指附近的軟組織及關節受損，引發拇指腕掌關節炎，手部副木(Splint)為一種有效的職能治療方式，通常依據治療師的經驗調整副木形狀，缺乏較科學化設計流程或評估方式。本研究開發手部副木客製化設計系統，使用手持式裝置進行三維掃描，透過光學雷達(Light Detection and Ranging, LiDAR)取得深度資料，藉此建立患者三維手模。根據其彩色影像，驅動深度學習模型，產生近似手模與關節點，經由點雲配準演算法，疊合掃描與近似手模，賦予掃描模型關節點資訊，做為副木設計參考依據。接著，經由電腦輔助設計軟體自動生成三維副木模型，在手持式裝置介面上呈現其穿戴效果，並計算副木與手模之間距離，依此渲染成不同顏色，協助評估其設計品質，避免副木與手部相交或縫隙過大。此外結合自由變形技術(Free-Form Deformation, FFD)，提供即時性副木幾何的調整，實現高度客製化功能。以三維列印製作出副木原型，由不同受試者實際穿戴，進行手部靈活度與握力測試合併肌電圖量測，客觀評估對手部動作靈活度與保護性，並進行日常活動感受穿戴舒適度，驗證客製化功能的有效性。本研究整合手持式裝置與多種資通訊技術，實現手部副木之創新性設計概念，改善職能治療復健的服務品質。

Prolonged mobile phone use can easily injury the soft tissues and joints near the thumb, leading to thumb carpometacarpal osteoarthritis (TCO). Splints are an effective occupational therapy method for treating TCO, but their design is traditionally based on the therapist’s experience rather than a rigorous process. To address this issue, this study develops a customized hand splint design system using information technology deployed on a handheld device. A patient's 3D hand model is constructed by 3D scanning using the Light Detection and Ranging (LiDAR) functions of the handheld device. A similar hand model of the patient containing 3D joint points is then generated from a color image using deep learning techniques. The joint points are integrated into the scanned model through 3D registration with the deep learning-generated model. They serve as a reference for designing splints customized for each individual. Next, a 3D splint model, automatically created using a CAD tool on the server side, is transmitted back to the handheld device over a wireless network. The splint model is geometrically overlaid onto the scanned hand model for display and evaluation purposes. The distance between the splint and hand models is calculated for each mesh point and rendered in different colors based on proximity. The visualization results enable the evaluation of the splint design quality by preventing intersections or excessive allowances with respect to the patient's hand. The system also allows real-time adjustment of the splint geometry for improving design quality. Finally, 3D-printed splint prototypes are created and tested by subjects to evaluate hand dexterity and perform grip strength tests with EMG measurements. The results help evaluate the flexibility, protection, and comfort of the splints during daily activities, thereby validating the customized design system. In summary, this study integrates handheld devices with various information and communication technologies to create an innovative design process for customized hand splints, enhancing the quality of occupational therapy and rehabilitation services.

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