根據衛生署統計過去十幾年來醫療數據發現中風是目前台灣主要疾病之一，中風的發生主要是因為腦部受到損害而使腦神經衰退，近而造成身體活動不方便，也影響到日常生活狀況。所以最近中風的議題不斷被醫學界討論、研究，企圖尋找良好、有效率的醫療復健方式以及能反映出與中風病患身體狀況有相關性的臨床評估、復健成效。
本研究主要針對中風上肢復健遊戲設計各項運動指標，希望透過這些指標能顯示出病患目前動作能力、反應程度，並且分析運動指標是否有明顯效果。另外，目前醫院流行的傳統評估量表: FMA、TEMPA、WMFT有些缺點，不是每一次運動復健都會進行傳統臨床評估，只有第一次復健、最後一次復健以及復健完經過一個月後追蹤評估，而且每次都花費很長時間。還有病患想要知道自己與其他人的差別、在有玩過此系統的人群中屬於哪種等級程度，因此建立新型評估方式希望能減輕物理治療師負擔、滿足病人需求。
實驗結果顯示，部分運動指標對於中風復健遊戲有相關的影響性，透過運動評估與統計分析數據能了解病患有明確的進步程度以及與傳統評估量表分數有相關性，另外以類神經網路、支持向量機驗證新型評估方式的建立是有可行性。

According to the past decades of medical data, Ministry of Health and Welfare has found that stroke was one of Taiwan's major diseases. The occurrence of stroke because of brain injury causing nerve cells are declined. Stroke seriously affects physical movement and the quality of daily life. So the medical profession have discussed and researched the issues of stroke in recent years, trying to find some well efficiency of medical rehabilitations, and performance analysis which can reflect conditions of the stroke patients.
This study is focused on the design of motion indexes which are about rehabilitation systems of upper limbs, hoping to indicating abilities of stroke patients at present and wondering the performance analysis of these systems whether is useful or not. In addition, the hospital use currently traditional assessment scales that have some problems and need to spend long time. Some stroke patients also want to know what differences with other people when playing rehabilitation systems. Therefore, establishing new medical assessment methods to solve these problems, reduce burdens of Physiotherapist and fulfill expectations of stroke patient.
The results of this study shows that part of the motion indexes for assessment method have considerable influences and stroke patients also have definite progress. Thus, using neural network and SVM to verify new medical assessment methods that is a certain degree of feasibility.

參考文獻
[1]V. L. Feigin, “Stroke epidemiology in the developing
world,” The Lancet, Vol. 365, No. 9478, pp. 2160-2161,
2005.
[2]V. L. Feigin, C. M. Lawes, D. A. Bennett, and C. S.
Anderson, “Stroke epidemiology: a review of
population-based studies of incidence, prevalence, and
case-fatality in the late 20th century,” The Lancet
Neurology, Vol. 2, No.1, pp. 43-53, 2003.
[3]G. D. Griffin, “Stroke, mTBI, Infection, Antibiotics
and Beta Blockade: Connecting the Dots,” Medical
Hypotheses, 2015.
[4]Life Talk. [Online].
Available: https://www.life.com.tw/?app=view&no=273613
[5]Z. S. Huang, T. L. Chiang, and T. K. Lee, “Stroke
Prevalence in Taiwan Findings From the 1994 National
Health Interview Survey,” Stroke, Vol. 28, No. 8, pp.
1579-1584, 1997.
[6]C. A. Kernich, “Living with Stroke A Guide for
amilies,” Neurology, Vol. 44, No. 10, pp. 1991-1991,
1994.
[7]M. Kelly-Hayes, A. Beiser, C. S. Kase, A. Scaramucci,
R. B. Agostino, and P. A. Wolf, “The influence of
gender and age on disability following ischemic
stroke: the Framingham study,” Journal of Stroke and
Cerebrovascular Diseases, Vol. 12, No. 3, pp. 119-126,
2003.
[8]B. M. Kissela, J. C. Khoury, K. Alwell, C. J. Moomaw,
D. Woo, O. Adeoye, and D. O. Kleindorfer, “Age at
stroke temporal trends in stroke incidence in a large,
biracial population,” Neurology, Vol. 79, No. 17, pp.
1781-1787, 2012.
[9]H. C. Lin, Y. J. Lin, T. C. Liu, C. S. Chen, and W. T.
Chiu, “Urbanization and stroke prevalence in Taiwan:
analysis of a nationwide survey,” Journal of Urban
Health, Vol. 84, No. 4, pp. 604-614, 2007.
[10]S. E. Chiuve, K. M. Rexrode, D. Spiegelman, G.
Logroscino, J. E. Manson, and E. B. Rimm, “Primary
prevention of stroke by healthy lifestyle,”
Circulation, Vol. 118, No. 9, pp. 947-954, 2008.
[11]M. Fisher, A. Dávalos, A. Rogalewski, A. Schneider,
and W. R. Schäbitz, “Toward a multimodal
europrotective treatment of stroke,” Stroke, Vol. 37,
No. 4, pp. 1129-1136, 2006.
[12]K. J. Greenlund, W. H. Giles,Keenan, J. B. Croft, and
G. A. Mensah, “Physician advice, patient actions, and
health-related quality of life in secondary
prevention of stroke trough diet and exercise,”
Stroke, Vol. 33, No. 2, pp. 565-571, 2002.
[13]S. Ahmed, N. E. Mayo, J. Higgins, N. M. Salbach, L.
Finch, and S. L. Wood-Dauphinée, “The Stroke
Rehabilitation Assessment of Movement (STREAM): a
comparison with other measures used to evaluate
effects of stroke and rehabilitation,” Physical
therapy, Vol. 83, No. 7, pp. 617-630, 2003.
[14]S. L. Wolf, C. J. Winstein, J. P. Miller, E. Taub,
G.Uswatte, D. Morris, and Excite Investigators,
“Effect of constraint-induced movement therapy on
upper extremity function 3 to 9 months after stroke:
the EXCITE randomized clinical trial,” Jama, Vol.
296, No. 17, pp. 2095-2104, 2006.
[15]S. M. Braun, A. J. Beurskens, P. J. Borm, T. Schack,
and D. T. Wade, “The effects of mental practice in
stroke rehabilitation: a systematic review,” Archives
of physical medicine and rehabilitation, Vol. 87, No.
6, pp. 842-852, 2006.
[16]W. S. Lu, C. H. Wang, J. H. Lin, C. F. Sheu, and C.
L. Hsieh, “The minimal detectable change of the
simplified stroke rehabilitation assessment of
movement measure,” Journal of rehabilitation
medicine, Vol. 40, No. 8, pp. 615-619, 2008.
[17]N. B. Lincoln, G. P .Mulley, A. C. Jones, E. McGuirk,
W. Lendrem, and J. R. A. Mitchell, “Effectiveness of
speech therapy for aphasic stroke patients: a
randomised controlled trial,” The Lancet, Vol. 323,
No. 8388, pp. 1197-1200, 1984.
[18]中山醫誌. [Online].
Available:http://medicineinfocsh.blogspot.tw/2015/02/blog-post_33.html
[19]S. Saini, D. R. A. Rambli, S. Sulaiman, M. N.
Zakaria, and S. R. M. Shukri, “A low-cost game
framework for a home-based stroke rehabilitation
system,” ICCIS, Vol. 1, pp. 55-60, Jun 2012.
[20]A. Dinevan, Y. M. Aung, and A. Al-Jumaily, “Human
computer interactive system for fast recovery based
stroke rehabilitation,” HIS, pp. 647-652, Dec 2011.
[21]P. Mirza-Babaei, M. Kamkarhaghighi, and K.
Gerling,“Opportunities in game-based stroke
rehabilitation,” GEM, pp. 1-4, Oct 2014.
[22]C. H. Lee, Y. H. Chiu, H. Y. Kao, I. T. Chen, I. N.
Lee, W. H. Ho, and H. Y.Lu, “A Body-Sensed Motor
Assessment System for Stroke Upper-Limb
Rehabilitation: A Preliminary Study,” SMC, pp. 3819-
3824, Oct 2013.
[23]D. White, K. Burdick, G. Fulk, J. Searleman, and J.
Carroll, “A virtual reality application for stroke
patient rehabilitation,” Mechatronics and Automation,
Vol. 2, pp. 1081-1086, 2005.
[24]R. Akerkar, “Introduction to artificial
intelligence,” PHI Learning Pvt. Ltd, 2014.
[25]S. Miksch, W. Horn, C. Popow, and F. Paky, “Utilizing
temporal data abstraction for data validation and
therapy planning for artificially ventilated newborn
infants,” Artificial intelligence in medicine, Vol.
8, No. 6, pp. 543-576, 1996.
[26]C. Ohmann, V. Moustakis, Q. Yang, K. Lang, and Acute
Abdominal Pain Study Group, “Evaluation of automatic
knowledge acquisition techniques in the diagnosis of
acute abdominal pain,” Artificial intelligence in
medicine, Vol. 8, No. 1, pp. 23-36, 1996.
[27]Y. Shahar, and M. A. Musen,“Knowledge-based temporal
abstraction in clinical domains,” Artificial
intelligence in medicine, Vol. 8, No. 3, pp. 267-298,
1996.
[28]R. Bellazzi, C. Siviero, M. Stefanelli, and G.
Nicolao,“Adaptive controllers for intelligent
monitoring,” Artificial intelligence in medicine,
Vol. 7, No. 6, pp. 515-540, 1995.
[29]R. Davis, B. Buchanan, and E. Shortliffe, “Production
rules as a representation for a knowledge-based
consultation program,” Artificial intelligence, Vol.
8, No. 1, pp. 15-45, 1977.
[30]C. P. Langlotz, L. M. Fagan, S. W. Tu, B. I. Sikic,
and E. H. Shortliffe, “A therapy planning
architecture that combines decision theory and
artificial intelligence techniques,” Computers and
Biomedical Research, Vol. 20, No. 3, pp. 279-303,
1987.
[31]J. S. Aikins, “Prototypical knowledge for expert
systems,” Artificial Intelligence, Vol. 20, No. 2,
pp. 163-210, 1983.
[32]I. Werry, K. Dautenhahn,B. Ogden, and W. Harwin, “Can
social interaction skills be taught by a social
agent? The role of a robotic mediator in autism
therapy,” Cognitive technology, pp. 57-74, 2001.
[33]K. Dautenhahn, and I. Werry, “Towards interactive
robots in autism therapy: Background, motivation and
challenges,” Pragmatics & Cognition, Vol. 12, No. 1,
pp. 1-35, 2004.
[34]J. Fox, M. Beveridge, and D. Glasspool,
“Understanding intelligent agents: analysis and
synthesis,” Aicommunications, Vol. 16, No. 3, pp.
139-152, 2003.
[35]G. L. Clore, and J. Palmer,“Affective guidance of
intelligent agents:How emotion controls cognition,”
Cognitive systems research, Vol. 10, No. 1, pp. 21-
30, 2009.
[36]T. Exell, C. Freeman, K. Meadmore, M. Kutlu, E.
Rogers, A. M. Hughes, and J. Burridge, “Goal
orientated stroke rehabilitation utilising electrical
stimulation, iterative learning and microsoft
Kinect,” ICORR, pp. 1-6, Jun 2013.
[37]L. Shires, S. Battersby, J. Lewis, D. Brown, N.
Sherkat, and P. Standen,“Enhancing the tracking
capabilities of the Microsoft Kinect for stroke
rehabilitation,” SeGAH, pp. 1-8, May 2013.
[38]D. Webster, and O. Celik, “Experimental evaluation of
Microsoft Kinect's accuracy and capture rate for
stroke rehabilitation applications,” HAPTICS, pp.
455-460, 2014.
[39]CODEPROJECT. [Online].
Available:http://www.codeproject.com/Articles/317974/KinectDepthSmoothing
[40]J. M. I. Zannatha, A. J. M. Tamayo, Á. D. G. Sánchez,
J. E. L. Delgado, L. E. R. Cheu, and W. A. S.
Arévalo, “Development of a system based on 3D vision,
interactive virtual environments, ergonometric
signals and a humanoid for stroke rehabilitation,”
Computer methods and programs in biomedicine, Vol.
112, No.2, pp. 239-249, 2013.
[41]R. T. Azuma, “A survey of augmented reality,”
Presence, Vol. 6, No. 4, pp. 355-385, 1997.
[42]NVIDIA. [Online]. Available:http://www.nvidia.com.tw/object/io_1270555014738.html
[43]J. P. Cuthbert, K. Staniszewski, K. Hays, D. Gerber,
A. Natale, and D. O'Dell, “Virtual reality-based
therapy for the treatment of balance deficits in
patients receiving inpatient rehabilitation for
traumatic brain injury,” Brain injury, Vol. 28, No.
2, pp. 181-188, 2014.
[44]H. Sin and G. Lee, “Additional virtual reality
training using Xbox Kinect in stroke survivors with
hemiplegia,” American Journal Of Physical Medicine &
Rehabilitation, Vol. 92, No.10, pp. 871-880, 2013.
[45]B. Wiederhold and G. Riva, “Balance recovery through
virtual stepping exercises using Kinect skeleton
tracking: a follow-up study with chronic stroke
patients,” Annual Review of Cybertherapy and
Telemedicine 2012, Vol. 181, pp. 108, 2012.
[46]K. H. Cho, K. J. Lee, and C. H. Song, “Virtual-
reality balance training with a video-game system
improves dynamic balance in chronic stroke patients,”
The Tohoku journal of experimental medicine, Vol.
228, No. 1, pp. 69-74, 2012.
[47]A. Turolla, M. Dam, L. Ventura, P. Tonin, M.
Agostini, C. Zucconi, and L. Piron, “Virtual reality
for the rehabilitation of the upper limb motor
function after stroke: a prospective controlled
trial,” J Neuroeng Rehabil, Vol. 10, pp. 85, 2013.
[48]I. Pastor, H. A. Hayes, and S. J. Bamberg, “A
feasibility study of an upper limb rehabilitation
system using kinect and computergames,” EMBC, pp.
1286-1289, Aug 2012.
[49]A. Panarese, R. Colombo, I. Sterpi, F. Pisano, and S.
Micera, “Tracking motor improvement at the subtask
level during robot-aided neurorehabilitation of
stroke patients,” Neurorehabilitation and neural
repair, Vol. 26, No. 7, pp. 822-833, 2012.
[50]Q. Ding, I. H. Stevenson, N. Wang, W. Li, Y. Sun, Q.
Wang, and K. Wei, “Motion games improve balance
control in stroke survivors: A preliminary study
based on the principle of constraint-induced movement
therapy,” Displays, Vol. 34, No. 2, pp. 125-131,
2013.
[51]馮恒諺，「虛擬實境中風復健系統之上肢運動成效評估與運動分
析」，國立中央大學資工系，碩士論文，2014。
[52]L. Paul, H. Debbie, B. Jennifer, L. Hervé,“A haptic-
robotic platform for upper-limb reaching stroke
therapy: Preliminary design and evaluation results,”
J Neuroeng Rehabil, Vol. 5, No. 15, 2008.
[53]National Center for Biotechnology Information
[Online]. Available:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2409358/
[54]S. Hesse, A. Waldner, and C. Tomelleri, “Innovative
gait robot for the repetitive practice of floor
walking and stair climbing up and down in stroke
patients,” Journal of Neuro Engineering and
Rehabilitation, Vol. 7, No. 30, 2010.
[55]Journal of NeuroEngineering and Rehabilitation.
[Online].
Available:http://www.jneuroengrehab.com/content/7/1/30
[56]B. Dorey, D. Reid, and T. Chiu, “ Stroke survivor’s
experiences of computer use at home,” Technology and
Disability, Vol. 19, No. 4, pp. 179-188, 2007.
[57]L. Rosenstein, A. L. Ridgel, A. Thota, B. Samame, and
J. L. Alberts, “Effects of combined robotic therapy
and repetitive-task practice on upper-extremity
function in a patient with chronic stroke,” American
Journal of Occupational Therapy, Vol. 62, pp. 28-35,
2008.
[58]H. C. Huang, C. H. Yeh, C. M. Chen, Y. S. Lin, and K.
C. Chung, “Sliding and pressure evaluation on
conventional and V-shaped seats of reclining
wheelchairs for stroke patients with flaccid
hemiplegia: a crossover trial,” Journal of
NeuroEngineering and Rehabilitation, Vol. 8, No. 40,
2011.
[59]WheelChair.com.hk. [Online].
Available:http://www.wheelchair.com.hk/reclining-back-wheelchairs
[60]P. Bagley, M. Hudson, A. Foster, J. Smith, and J.
Young, “A randomized trial evaluation of the Oswestry
Standing Frame for patients after stroke,” Clinical
Rehabilitation, Vol. 19, pp. 354-364, 2005.
[61]H. A. Isma'eel, G. E. Sakr, M. M. Almedawar, J.
Fathallah , T. Garabedian, S. B. Eddine, L.
Nasreddine, and I. H. Elhajj, “Artificial neural
network modeling using clinical and knowledge
independent variables predicts salt intake reduction
behavior,” Cardiovasc Diagn Ther, Vol. 5, No. 3, pp.
219-228, Jun 2015.
[62]H. Karamanli, T. Yalcinoz, M. A. Yalcinoz, and T.
Yalcinoz, “A prediction model based on artificial
neural networks for the diagnosis of obstructive
sleep apnea,” Sleep Breath, 2015.
[63]S. C. Hu, “Texture Analysis for Aided Diagnosis of
Hemorrhage Transformation of Acute Middle Ischemic
Stroke in CT Images,” Department of Bio-Medical
Engineering, Mar 2012.
[64]楊世瑩，「PASW SPSS統計分析即學即用」，碁峯資訊股份有限公
司，Jun 2011。
[65]C. Spearman,“The proof and measurement of association
between two things,” The American journal of
psychology, Vol. 15, pp.72-101, 1904.
[66]蘇木春與張孝德，「機器學習:類神經網路、模糊系統以及基因演算
法則」，全華圖書股份有限公司，2012。
[67]P. J. Rousseeuw, “Silhouettes: a graphical aid to the
interpretation and validation of cluster analysis,”
Journal of computational and applied mathematics,
Vol. 20, pp. 53-65, 1987.
[68]CHRIS MCCORMICK. [Online].
Available:https://chrisjmccormick.wordpress.com/2013/08/15/radial-basis-function-network-rbfn-tutorial/
[69]R. E. Fan, P. H. Chen, and C. J. Lin, “Working Set
Selection Using Second Order Information for
Training Support Vector Machines,” J. Mach. Learn.
Res., Vol. 6, pp. 1889-1918, 2005.