| 摘要: | 隨著生活物質條件的提升，中風的發生率也逐年增加。運用新穎的3D 互動科技以及虛擬實境技術，針對因腦部受損衍生之神經型運動功能障礙，進行系統化的生活技能模擬以及階層式的技巧訓練，以促進腦部塑性化以及神經再組織化；並使用先進的計算技術分析運動資訊，進行復健評估，是神經再生科學和復建醫學的新興領域。同時，為降低因支持中風病人復健所伴隨之龐大社會資源需求(人力照護、交通運輸、其他風險)，結合網路多媒體科技，將醫療介入過程透過遠距系統實施，是亟待開發的方向並亟需驗證其可行性。本計畫可以作為資訊學門以及復建醫學的重要橋梁。本計畫擬對於一中風後上肢臂延伸運動虛擬實境復健系統，進行中風病人醫院測試，同時配合臨床運動損傷評估方法，驗證此系統的復健效果；同時，也對於任務表現進行統計分析，對於運動特徵(穩定性、效率性、不連續性以及震盪性)進行運動分析，並使用機器學習技術建立運動模型及分析技術，開發能夠應用於臨床診斷及評估的新型評估方法。本計畫也擬建構針對遠距醫療介入之網路影音傳輸平台，進行中風病人居家遠距測試，驗證技術的可行性，並經由醫病雙方使用者之社交存在感的量測，探討從使用者角度之醫病遠距互動模式的可行性。 In Taiwan, more and more people annually suffer a stroke and this has been cited as one of the leading causes of severe, long-term disability. Recent advances in the understanding of neural plasticity and motor function retraining clearly suggest that greater quantity, duration and intensity of practice are most effective in modifying neural reorganization. Substantial effort and expertise has been focused on developing innovative technology/interventions that exploit the neuroplastic properties associated with the sensorimotor systems in the adult brain. While there is a shortage of qualified personnel in patient care, and the annual cost increase is outpacing inflation, tele-rehabilitation methods that patients can practice at home are proposed to increase treatment access, time spent doing rehabilitation exercises and when embedded in a game-base context, could enhance engagement and motivation. This project serves as a bridge between engineeing and medical rehabilitation. In this project, we propose to perform a stroke-patient subject test with the use of a virtual reality based post-stroke rehabilitation system dedicated to perform forearm reaching test. To evaluate the effectiveness of the system and the design of rehabilitation protocol, clinical behavioral assessment methods are applied at the time point of pre, middle and post period. While there is a huge amount of information (including motor data and subject performance) collected from the test, we further propose three ways to assess the progress of motor behavior. First, we intend to utilize machine learning techniques as a tool to develop motor patterns at different stages of therapy process. Pattern recognition is performed to identify the progress of motor behaviors. Second, we derive several types of motor characteristics, including stability, consistence, efficiency and oscillation, in order to perform kinematics analysis. Third, performance of each task assigned in the therapy process is analyzed to assess the progress of motor behavior. Besides, we intend to develop a tele-rehabilitation system via the use of networking technologies from which the system can real-time transmit various types of multimedia contents, such as audio, video, motor data…etc. Upon the use of tele-rehabilitation system, we plan to conduct a tele-subject test with which the interactive post-stroke rehabilitation system will be installed at patient’s home and therapists will direct the patient going through complete therapy processes with adequate vocal assistance and visual monitoring in hospital. To investigate the perception of remote interaction from user perspectives, questionnaires are designed and applied to measure the social presence quantitatively. Also, clinical behavioral assessment methods are applied to evaluate the effectiveness of the system. 研究期間：9908 ~ 10007 |
