對於帕金森氏症患者的治療方式多以藥物治療並搭配機能復健為主，但由於醫療資源有限，初期患者經常無法獲得足夠復健，導致錯失復健黃金時期。此外，因往返醫院對身心所造成的消耗且復健療程相對枯燥乏味，病患難免會產生逃避的念頭。因此，本文將針對上述問題提出可能的解決方案，期望改善帕金森氏症患者現有復健環境以及減輕健保醫療負擔。
本研究利用Kinect裝置開發一套專為帕金森氏症患者使用的居家復健運動系統，提供病患於自家環境自行進行復健。系統開發過程中與專業復健研究人員合作，藉由將復健療程數位內容化以遊戲方式呈現，以提高患者復健過程的趣味性，加強復健動機。本系統目前包含兩項遊戲復健療程，分別針對不同臨床需求進行設計。第一項為主動式遊戲，透過激勵患者盡量向外伸展其上肢以移動身體重心進而達到平衡訓練，該遊戲使用無理B-樣條(B-spline)方法擬合伸展最大範圍，並以此建立遊戲控制與評分參考；第二項為被動式遊戲，藉由讓病患模仿專為帕金森氏症所設計的復健動作以減輕僵直與運動徐緩症狀，該遊戲將以動態時間校正作為動作準確度的評估方法。本系統自動彙整運動過程的相關數據進行初步統計與分析後進行雲端儲存，使復健師擁有一個可隨時追蹤多位病患的管道，並獲得量化評比數據，此舉不僅能大幅降低病患來往醫院間的舟車勞頓，更進一步分攤了復健師的工作量。
本研究現階段已招募30位一般年輕人、1位老年人、1位中風病患以及1位帕金森氏症合併中風病患對被動式遊戲進行初步測試，結果顯示肢體遮擋會影響評分穩定度，且年輕人群組與其他3類受試者所組成的群組兩者之間的分數擁有顯著差異。

SUMMARY
We have developed a home-based rehabilitation system for patients with Parkinson’s disease (PD), using Kinect to create a comfortable environment to proceed training activities. During the process of development, we worked closely with professional Parkinson’s therapists. To date, there are two types of training programs are developed aiming at common syndromes of PD, and both are presented as digital games to improve the adherence of the PD patients. We use the system to collect data at every training activities and stores it in database server. After preliminary analysis of the system, it enables the therapists to keep track of the progress of the PD patients. In this study, we have recruited two subjects for the preliminary test to the passive exercises in our developed program.
Key words: Parkinson’s disease, Kinect, Home-based rehabilitation

[1] “內政部統計處”，http://www.moi.gov.tw/stat/news_content.aspx?sn=10225, accessed on May15th 2016.
[2] 朱迺欣，“巴金森病—認識與面對”，健康文化事業股份有限公司，2004。
[3] “台大醫院巴金森症醫療中心”，http://www.pdcenterntuh.org.tw/ContentAspx/Browser00.aspx?isClass=3&, accessed on May15th 2016.
[4] Bhidayasiri, R. and Tarsy, D., “Movement Disorders: A Video Atlas,” Humana Press, 2013.
[5] Goodwin, V.A., Richards, S.H., Taylor, R.S., Taylor, A.H., and Campbell, J.L., “The effectiveness of exercise interventions for people with Parkinson's disease: a systematic review and meta-analysis,” Movement Disorders, Vol.23, No.5, pp.631-640, 2008.
[6] Yeung, L.F., Cheng, K.C., Fong, C.H., Lee, W.C., and Tong, K.Y., “Evaluation of the Microsoft Kinect as a clinical assessment tool of body sway,” Gait & Posture, Vol.40, Issue 4, pp.532-538, 2014.
[7] Clark, R.A., Bryant, A.L., Pua, Y., McCrory, P., Bennell, K., and Hunt, M., “Validity and reliability of the Nintendo Wii Balance Board for assessment of standing balance,” Gait & Posture, Vol.31, Issue 3, pp.307-310, 2010.
[8] Lange, B., Chang, C.Y., Suma, E., Newman, B., Rizzo, A.S., and Bolas, M., “Development and evaluation of low cost game-based balance rehabilitation tool using the Microsoft Kinect sensor,“ Conference Proceedings of the International Conference of IEEE Engineering in Medicine and Biology Society, pp.1831-1834, 2011.
[9] Lange, B., Flynn, S., and Rizzo, A., “Initial usability assessment of off-the-shelf video game consoles for clinical game-based motor rehabilitation,” Physical Therapy Reviews, Vol.14, Issue 5, 2009.
[10] Esculier, J.F., Vaudrin, J., Bériault, P., Gagnon, K., and Tremblay, L.E., “Home-based balance training programme using Wii Fit with balance board for Parkinson’s disease: a pilot study,” Journal of Rehabilitation Medicine, Vol.44, No.2, pp.144-150, 2012.
[11] Su, C.J., Chiang, C.Y., and Huang, J.Y., “Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic,” Applied Soft Computing, Vol.22, pp.652-666, 2014.
[12] Ravenek, K.E., Wolfe, D.L., and Hitzig, S.L., “A scoping review of video gaming in rehabilitation,” Disability and Rehabilitation: Assistive Technology, Vol.11, Issue 6, pp.445-453, 2016.
[13] Barry, G., Galna, B., and Rochester, L., “The role of exergaming in Parkinson’s disease rehabilitation: a systematic review of the evidence,” Journal of NeuroEngineering and Rehabilitation, Vol.11, article 33, 2014.
[14] Yang, W.C., Wang, H.K., Wu, R.M., Lo, C.S., and Lin, K.H., “Home-based virtual reality balance training and conventional balance training in Parkinson's disease: a randomized controlled trial,” Journal of the Formosan Medical Association, 2015.
[15] Herz, N.B., Mehta, S.H., Sethi, K.D., Jackson, P., Hall, P., and Morgan, J.C., “Nintendo Wii rehabilitation (“Wii-hab”) provides benefits in Parkinson’s disease,” Parkinsonism and Related Disorders, Vol.19, Issue 11, pp.1039-1042, 2013.
[16] Sarbolandi, H., Lefloch, D., and Kolb, A., “Kinect range sensing: Structured-light versus Time-of-Flight Kinect,” Computer Vision and Image Understanding, Vol.139, pp.1-20, 2015.
[17] Pompeu, J.E., Arduini, L.A., Botelho, A.R., Fonseca, M.B., Pompeu, S.M., Torriani-Pasin, C., and Deutsch, J.E., “Feasibility, safety and outcomes of playing Kinect Adventures!™ for people with Parkinson's disease: a pilot study,” Physiotherapy, Vol.100, Issue 2, pp.162-168, 2014.
[18] Galna, B., Jackson, D., Schofield, G., McNaney, R., Webster, M., Barry, G., Mhiripiri, D., Balaam, M., Olivier, P., and Rochester, L., “Retraining function in people with Parkinson’s disease using the Microsoft Kinect: game design and pilot testing,” Journal of NeuroEngineering and Rehabilitation, Vol.11, article 60, 2014.
[19] Obdržálek, Š., Kurillo, G., Ofli, F., Bajcsy, R., Seto, E., Jimison, H., and Pavel, M., “Accuracy and robustness of Kinect pose estimation in the context of coaching of elderly population,” Conference Proceedings of the International Conference of IEEE Engineering in Medicine and Biology Society, pp.1188-1193, 2012.
[20] Galna, B., Barry, G., Jackson, D., Mhiripiri, D., Olivier, P., and Rochester, L., “Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson’s disease,” Gait & Posture, Vol.39, Issue 4, pp.1062-1068, 2014.
[21] Xu, X., and McGorry, R.W., “The validity of the first and second generation Microsoft Kinect™ for identifying joint center locations during static postures,” Applied Ergonomics, Vol.49, pp.47-54, 2015.
[22] Xia, L., Chen, C.C., and Aggarwal, J.K., “View Invariant Human Action Recognition Using Histograms of 3D Joints,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp.20-27, 2012.
[23] Yang, X., and Tian, Y.L., “EigenJoints-based action recognition using Naïve-Bayes-Nearest-Neighbor,” IEEE computer society conference on Computer vision and pattern recognition workshops, pp.14-19, 2012.
[24] Waithayanon, C., and Aporntewan, C., “A motion classifier for Microsoft Kinect,” International Conference on Computer Sciences and Convergence Information Technology, pp.727-731, 2011.
[25] Sempena, S., Maulidevi, N.U., and Aryan, P.R., “Human action recognition using Dynamic Time Warping,” International Conference on Electrical Engineering and Informatics, pp.1-5, 2011.
[26] Nirjon, S., Greenwood, C., Torres, C., Zhou, S., Stankovic, J.A., Yoon, H.J., Ra, H.K., Basaran C., Park, C., and Son, S.H., “Kintense: a robust, accurate, real-time and evolving system for detecting aggressive actions from streaming 3D skeleton data,” IEEE International Conference on Pervasive Computing and Communications, pp.2-10, 2014.
[27] Yurtman, A., and Barshan, B., “Automated evaluation of physical therapy exercises using multi-template dynamic time warping on wearable sensor signals,” Computer Methods and Programs in Biomedicine, Vol.117, Issue 2, pp.189-207, 2014.
[28] Yaqin Tao, Huosheng Hu, and Huiyu Zhou, “Integration of vision and inertial sensors for 3D arm motion trackin in home-based rehabilitation,” The International Journal of Robotics Research, Vol.26, No.6, pp.607-624, 2007.
[29] Casiez, G., Roussel, N., and Vogel, D., “1€ filter: A simple speed-based low-pass filter for noisy input in interactive systems,” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp.2527-2530, 2012.
[30] Piegl, L., and Tiller, W., “The NURBS Book,” Springer Verlag, 1997.
[31] “楊東興老師輪椅舒筋健身法”， https://www.youtube.com/watch?v=bga8iEsplXM, accessed on June17th 2016.
[32] Smith, L.I., “A tutorial on Principal Components Analysis”, http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf, accessed on Juy7th 2016.
[33] “線代啟示錄”，https://ccjou.wordpress.com/, accessed on July7th 2016.
[34] Ratanamahatana, C.A., and Keogh, E., “Making time-series classification more accurate using learned constraints,” Proceedings of the Fourth SIAM International Conference on Data Mining, 2004.
[35] Bemdt, D.J., and Clifford, J., “Using dynamic time warping to find patterns in time series,” KDD Workshop, pp.359-370, 1994.
[36] Sakurai, Y., Faloutsos, C., and Yamamuro, M., “Stream monitoring under the time warping distance,” International Conference on Data Engineering, pp.1046-1055, 2007.
[37] Folstein, M.F., Folstein, S.E., and McHugh, P.R., “Mini-mental state: a practical method for grading the cognitive state of patients for the clinician,” Journal of Psychiatric Research, Vol.12, Issue3, pp.189-198, 1975.