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研究生: 曾立凱
Li-kai Tseng
論文名稱: 身心障礙者電腦輔具選用決策樹
A decision tree of selecting computer-related assistive devices for the disabled user
指導教授: 紀佳芬
Chia-Fen Chi
口試委員: 王茂駿
none
黃雪玲
none
梁瓊如
none
張彧
none
劉伯祥
none
學位類別: 博士
Doctor
系所名稱: 管理學院 - 工業管理系
Department of Industrial Management
論文出版年: 2012
畢業學年度: 100
語文別: 中文
論文頁數: 78
中文關鍵詞: 產生法則決策樹電腦輔具
外文關鍵詞: production rule, decision tree, assistive technology
相關次數: 點閱:301下載:8
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  • 如果沒有專家的協助,身心障礙者很難自行找到合適自己的電腦輔具。Anson曾經發展一種決策樹方法,使用49個評估身心障礙者機能程度的選項,以建議其選用26種電腦輔具類別。職能治療師或身心障礙者可以透過這些問題的評估結果來找尋適合的電腦輔具。然而Anson所發展的決策樹沒有明確區分出點選輸入與文數字輸入輔具,且同一命題常重複不斷的出現;因此,本研究擬切割Anson的決策樹成為互斥的獨立子樹來配合身心障礙者的實際需求,這包括身心障礙的電腦使用者可能需要數字輸入、點選、感官輸出和績效提昇等設備。
    本研究使用6個身心障礙使用者對分割後的決策樹進行測試以驗證新的決策樹可以使用,較少的評估問題可以有效率的提供一組更完整的電腦輔具組合,經過分割後的子樹,也被證實有能力將新的輔具類別增加到決策樹中以擴增原決策樹來反應最新的科技輔具狀況。諸如本研究這樣修整決策樹和決策表的過程和結果也可以被應用來發展其它決策支援系統。


    Disable users without expertise in assistive technology have problems in finding an appropriate assistive device for computer access. Anson developed a decision tree of 49 evaluative questions to access functional capabilities of the disable user and come up with a total of 26 assistive devices for computer access. The occupational therapists or disabled users had to go through repetitive questions in order to find an appropriate device. Therefore, the current research divide Anson’s decision tree multiple independent subtrees to meet the actual demand of disable users. That is a disabled user may require alphanumeric and pointing device, output device and performance enhancement. The decision tree was tested by seven disable users to prove that the new decision tree can provide a complete set of assistive devices for computer access with a smaller number of evaluative questions. How to insert new categories of computer related assistive devices were elaborated to make sure the decision tree can be expanded and updated. The process and results of trimming the decision tree and decision table can be applied to the development other types of decision support systems.

    目錄 第一章緒論8 第一節研究背景與動機8 第二節研究目的10 第三節研究範圍與限制11 第四節研究架構12 第二章文獻探討13 第一節電腦輔具選用13 第二節決策樹16 一.樹狀結構16 二.決策樹16 三.建立決策樹的方式17 第三節 Anson的決策樹18 一.Anson的決策樹邏輯架構19 二.Anson的電腦輔具選用決策樹評估邏輯22 第四節決策樹的瑕疵種類25 一.結構性的瑕疵 (Structure Flaws)25 二.語意性瑕疵(Semantic Flaws)25 三.總體性瑕疵(Global Flaws)26 四.選擇要處理的瑕疵節點27 五.類化說明與消除瑕疵的動作28 第五節決策表29 第六節決策表知識的檢驗與修正31 第七節電腦輔具選用與ICF的關係32 第三章研究方法34 第一節確認Anson電腦輔具選用決策樹的錯誤34 第二節重新定義電腦輔具選用問題35 第三節檢驗電腦輔具選用決策樹知識36 第四節研究流程34 第四章 Anson電腦輔具選用決策樹的問題37 第一節 Anson’s 決策樹的瑕疵37 第二節建立電腦輔具選用決策表41 第三節修正選用電腦輔具的命題45 第四節修正決策表知識的正確性48 第五章建立新的電腦輔具選用決策樹56 第一節重新建立決策樹56 第二節新決策樹的使用績效60 第六章結論與建議66 第一節建議66 第二節結論70 參考文獻71 附錄75 A. UNUMPrident輔具選用決策樹75 B. Anson的選用輔具決策問項78

    1.AbleData. (2009). Available: http://www.abledata.com
    2.Abascal, Julio. and Nicolle, Colette. (2005) Moving towards inclusive design guidelines for socially and ethically aware HCI, Interacting with Computers, vol. 17, 5, 484-505.
    3.Anson,D. K.(1994) Finding your way in the maze of computer access technology, Am J Occup Ther., vol. 48, pp. 121-129.
    4.Anson,D. K. (1997) Alternative Computer Access: A Guide to Selection," F. A. Davis Company; 1st edition.
    5.Arthanat, S .and Lenker , J. A.(2008) Evaluating the ICF as a framework for clinical assessment of persons for assistive technology device recommendation in Focus on Disability: Trends in Research and Application. Kroll, T (Edi), pp. 31-38, Nova Science Publishers, Inc.
    6.Brehmer, B. (1988) Organization of decision making in complex systems. In L.P. Goodstein, H.B. Anderson, & S.E. Olesen [Eds], Tasks, Errors, and Mental Models. London: Taylor & Francis.
    7.Brodwin, M. G., Cardoso, E., Star, T. (2004) Computer Assistive Technology for People who Have Disabilities: Computer Adaptations and Modifications. Journal of Rehabilitation. Vol 70, No 3, pp 28-33.
    8.Butterfield, T. M. & Ramseur, J. H.(2004) Research and case study findings in the area of workplace accommodations including provisions for assistive technology: A literature review, Technology and Disability 16, 201-210
    9.Cho,V.& Ngai, E. W. T. (2003) Data mining for selection of insurance sales agents. Expert Systems vol. 20, No. 3 (July), 123-132
    10.Colombet, I., Aguirre-Junco, A.R., Zunino, S., Jaulent, M.C., Leneveut, L.,& Chatellier, G.,(2005) Electronic implementation of guidelines in the EsPeR system: a knowledge specification method, Int J Med Inform, vol. 74, pp. 597-604.
    11.Cragun, B. J. & Steudel, H.J. (1987) A decision-table-based processor for checking completeness and consistency in rule-based expert systems," Int. J. Man-Machine Studies, vol. 26, pp. 633-648.
    12.Davalli, A., Sacchetti, R., Pacetti, A. (2003) Service for the Evaluation and Provision of Computer and Domotic Aids for the Victims of Occupational Accidents, Assistive Technology – Shaping the Future: AAATE 2003 Conference Proceedings. Vol. 11, No. 1, 361-365.
    13.De Jonge, D. M.& Rodger, S.A. (2006)Consumer-identified barriers and strategies for optimizing technology use in the workplace, Disabil Rehabil Assist Technol, vol. 1, pp. 79-88.
    14.Elouedi,Z., Mellouli, K. and Smets,P.(2001) Belief decision trees: theoretical foundations, International Journal of Approximate Reasoning,vol. 28, 91-124.
    15.Folino, G., Pizzuti, C., Spezzano, G. (1999) A cellular genetic programming approach to classification, Proc. Of The Genetic and Evolutionary Computation Conference GECCO99, vol. 2, 1015-1020.
    16.Gagne, E. D., Yekovich, C., and Yekovich, F. (1993) The cognitive psychology of school learning. New York: Harper Collins.
    17.Gamble, M. J., Dowler, D. L., Hirsh, A. E. (2004) Informed decision making on assistive technology workplace accommodations for people with visual impairments, Work, vol. 23, 123-130.
    18.Glinert, E. P. and York, B. W. (1992) Computers and people with disabilities, Comm. ACM 35, 5(May), 32-35.
    19.Grott, R. (2000) Using the Power of Database Software and Dial-Up Networking to Promote the Employment of People with Multiple Disabilities, RESNA 2000: Technology for the New Millenium, vol. 20, No. 1, 107-109.
    20.Heian, B. C., Gale, J. R. (1988) Mortgage Selection Using a Decision-Tree Approach: An Extension. Interfaces, 18:4(July-August), 72-83.
    21.Tsujino , K., Dabija, V. D., Nishida, S. (1996) Interactive improvement of decision trees through flaw analysis and interpretation. Int . J . Human – Computer Studies, 45, 499-526.
    22.Kakusho, O. & Mizoguchi, R. (1983) A new algorithm for non-linear mapping withapplications to dimension and cluster analyses . Pattern Recognition , 16 , 109 – 117.
    23.Kennedy, P. R. &Adams,K.D. (2003) A decision tree for brain-computer interface devices.," IEEE Trans Neural Syst Rehabil Eng, vol. 11, pp. 148-150.
    24.Kirchner,K., Tölle,K.-H., Krieter, J. (2004) Decision tree technique applied to pig farming datasets. Livestock Production Science 90, 191-200
    25.Kruskal, J. B. (1964) Multidimensional scaling by optimizing goodness of fit to a non-metrichypothesis . Psychometrika , 29 , 1 – 27.
    26.Kweku-Muata, Osei-Bryson. (2004) Evaluation of decision trees: a multi-criteria approach. Computers & Operations Research 31, 1993-1945
    27.Liu, N. K. & Dillion,T. (1987) Detection of Consistency and Completeness in Expert Systems using Numerical Petri Nets," Proceedings of AI'87, 170-185.
    28.Magnusson, L., Hanson,E., Borg, M.(2004) A literature review study of Information and Communication Technology as a support for frail older people living at home and their family carers, Technology and Disability 16, 223-235
    29.Mann,W. C., Belchior, P., Tomita, M. R. & Kemp, B. J. (2005) Computer use by middle-aged and older adults with disabilities, Technology and Disability 17, 1-9
    30.Mazer, B., Dumont, C., Vincent, C.(2003) Validation of the assessment of computer task performance for children, Technology and Disability,vol. 15, 35-43.
    31.Merlevede,P. & Vanthienen, J. (1991) A structured approach to formalization and validation of knowledge " Developing and Managing Expert System Programs, 1991., Proceedings of the IEEE/ACM International Conference on pp. 149 -158.
    32.Neumann, A.,Holstein, J.,Gall, J. L.,&Lepage, E.(2004) Measuring performance in health care: case-mix adjustment by boosted decision trees. Artificial Intelligence in Medicine, vol. 32, 97-113.
    33.Nguyen, T. A., Perkins, W. A., Laffey, T. J.,& Pecora,D. (1987) Knowledge base verification," AI Magazine, vol. 8, 69-75.
    34.Nochajski, S. M. & Oddo, C.R. (1995) Technology in the workplace, in Mann WC, Lane JP (eds):Assistive Technology for People with Disabilities," The American Occupational Therapy Association. Betheda, Maryland, 197-210.
    35.Pabarskaite, Z.(2003) Decision trees for web log mining. Intelligent Data Analysis 7, 141-154
    36.Quinlan,J. R. (1999) Simplifying decision trees. Int. J. Human-Computer Studies. 51, 497-510.
    37.Senge, J. C. (1997) How technology has improved my access to information, Technology and Disability, vol. 6, 191-198.
    38.Shneiderman, B. (1983), Direct manipulation: a step beyond programming language, IEEE Computer, 16(8), 57-69
    39.Smith J. Q. (1988) Decision analysis: a Bayesian approach, Chapman and Hall.
    40.Smith, R.O. (2002). Assistive technology outcome assessment prototypes: Measuring "INGO" variables of "outcomes". In R. Simpson (Ed.), RESNA 25th Annual Conference Proceedings (pp. 239-241). Minneapolis, MN: RESNA Press
    41.Srivastava,A., Han, E.H., Kumar, V. & Singh,V. (1999) Parallel Formulations of Decision-Tree Classification Algorithms, Data Mining and Knowledge Discovery, vol. 3, pp. 237-261.
    42.Steel, E., Gelderblom, G. J. and Witte, L.P. (2011) Development of an AT selection tool using the ICF model, Technology and Disability, 23, 1–6.
    43.Steriadis, C. E. & Constantinoy, P. (2003) Designing human-computer interfaces for quadriplegic people, ACM Transactions on Computer-Human Interaction (TOCHI), vol. 10, No. 2, 87-118.
    44.Threats, T. T. (2006) Towards an international framework for communication disorders: Use of the ICF, Journal of Communication Disorders. 39, 251–265.
    45.Tsujino, K., Dabija, V. G. &Nishida, S. (1996)Interactive improvement of decision trees throughflaw analysis and interpretation, Int . J . Human – Computer Studies 45 , 499 – 526.
    46.UNUMProvident, (1999) Assistive Technology Decision Tree.
    47.Wessels,R.,Dijcks,B.,Soede,M.,Gelderblom,G.J. &Write, L. D.(2003) Non-use of provided assistive technology devices, a literature overview, Technology and Disability 15, 231-238
    48.World Health Organization [ICF: International Classification of Functioning, Disability and Health (English full version) Online] [updated June 15 2001]. Available at: http://www.who.int/classifications/icf/site/online browser/icf.cfm

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