在真實世界中，一項運動如果要做得好，必須要人類感知的部份與運動神精能夠協調。因此，視覺、聽覺以及觸覺在運動中都是非常重要的感知能力。這些感知器官中最獨特的一種就是眼睛，它是一個雙向的器官，能接收訊息，也能表達思想。許多的運動員在與對手對抗時，會做出假動作來欺騙敵人，而有經驗的對手往往也會藉由經驗以及對方的眼神來判斷對方的動作。這個細微的互動，在先前的運動模擬中尚未出現，但是，這個因素確實存在於真實事件當中，並充滿著人類智慧的結晶。因此，為了證實這一個現象是可以被導入模擬系統中，就必須定出明確的目標，並找出相關的基礎科學來進一步瞭解，找出有那些問題是有待克服與驗證的，最後經過系統設計與實驗來完成。
再者，各類的運動訓練模擬已發展多年，然而模擬運動競賽的行為不只是在模仿人類的動作而已，更應該要模擬出運動競賽時對手可能的思考與決策。所以本研究不只是要由對手的動作來判斷下一步要怎麼做，更要以對手的細微的生理特徵數據來輔助電腦角色(Virtual Avatar)，做出下一個行為決策。研究的方法是以生理心理學(psychophysiology)、思考模型(human thinking)、行為決策(decision making)三大領域的一些先前研究為基礎。在比較實際運動的模式記錄與初步電腦模擬下的記錄後，發展出一個連結的模型。研究的實例是以武術運動來模擬， 發展出的一套標準流程，做出能夠偵測生理特徵變化並思考的模擬系統。最後，與傳統武術模擬系統，分別對使用者做測試及再修正。結果證實加入偵測生理特徵變化的模擬可以讓受訓練者對系統產生較大的興趣；而訓練的靈活度也相對的提升，系統不再因為容易被熟悉，而太快失去訓練效果。本研究所產生的雛型(proto-type)，最大的貢獻在於取出特定類別的生理特徵並找出連結現實與模擬系統的方法，為將來的其他生理模式或運動模式的導入，做一個很好的樣版。這個研究未來還可以擴大發展至個人電腦遊戲或機械人的智慧系統。

Different types of sports training simulation have been made by the researchers for many years in the past. However, the simulation of a sport game should not aim only at the simulation of human movement, but should also include the simulation of the possible thinking and reaction of the opponent in the game. In this paper, we study how to find a way to further assist the virtual Avatar to generate the next action based on the slight physiological trait of the opponent. We developed the linking model among three fields: psychophysiology, human thinking, and decision making, by comparing the data from the actual sport and the data recorded by the computer model. The actual case of study in this paper is the simulation of the martial-art sport. The test results showed that the simulation incorporated with the function of detecting the physiological variation enables the person being trained to become more interested in the system. Also, the flexibility of training is relatively increased. Since the user soon becomes familiar with the system, the system will no longer lose its training effectiveness. The most valuable contribution of the embryonic model generated in the research is the obtaining of specific physiological traits and finding out the procedure for linking the practical condition and the simulation system that can serve as a useful model for introducing other physiological models or sport models in the future.

1 Introduction……………………………………………….1
1.1 Background and motivation ……………………………..1
1.2 Related work…………………………………………………4
1.2.1 Three relative researches……………………….……4
1.2.2 Equipment………………………………………………..16
1.3 Problems and Solving methods………………………….25
1.4 Purpose and contribution……………………………...28
2 Research Method…………………………………………….29
2.1 Be a smart choice…………………………………....29
2.2 How to build linking model……………………………33
3 Implementation and Results………………………………41
3.1 Data collection………………………………………….41
3.2 Model building...……………………………………….46
3.2.1 From problem to objectives..………………………46
3.2.2 Objectives and consequences..…………………….53
3.2.3 The system of embryonic model ………………….57
3.3 System Implement……..………………………………….61
4 Discussion and Conclusion…………………………………66
4.1 Discussion……………………….…………………………66
4.2 Conclusion………………………………………………...68
5 Bibliography ………………………………………….…..70
6 Appendix……………………………………………………..76
LIST OF FIGURES
Figure 1.1 Repin's picture and eye path…………………………………………….13
Figure 2.1 Process of model building…………………………………………….....36
Figure 2.2 Example from problem to alternatives…………………………………..37
Figure 2.3 Include all kinds of data’s example of linking data……………………...40
Figure 3.1 Layout of positions for the recording…………………………………….43
Figure 3.2 A watches to B’s eye………………...……………………………………44
Figure 3.3 A moves his eye trace to B’s left upper body………………………….....44
Figure 3.4 A attack B, but B defences success………………………………………45
Figure 3.5 Experiment Structure……………………………………………………..54
Figure 3.6 System structure….……………………………………………………….60
LIST OF TABLES
Table 1.1 Comparison with hunan thinking and A.I. model………………………....15
Table 1.2 Comparison of Eye-Tracking Techniques………………………….……...23
Table 2.1 Example of objectives of consequences table for Martial Art
competition…………………………………………..……………………………….32
Table 3.1 Part of Category PP-1 ………………………….…………………………50
Table 3.2 Part of Category PP-2……………………………..………………………51
Table 3.3 Part of Category PP-3……..........................................................................52
Table 3.4 Part of Category PP-4………………………………..……………………52
Table 3.5 The parts of results’ list after experiment………………………………….55
Table 3.6 The decision making process………………………………………………58
Table 3.7 Trainee comment survey/ The numeric values mean the number of trainee
with positive agreement……………...………………………………………………62
Table 3.8 Decision making success rate..……………………………………………62
Table 3.9 Evaluation table of the training……………………………………………63
Table 3.10 Training system with physiological traits detect mechanism (Successful
defense)……………….……………………………………………………………...64
Table 3.11 Training system without physiological traits detect mechanism (traditional
training system)……………….……………………………………………….. 65

[1]  Benjamin, S., and Hank, K., I. E., “Tele-sports and Tele-dance: Full-Body Network Interaction,” Proceedings of the ACM symposium on Virtual reality software and technology, Osaka, Japan, pp. 108-116, (2003).
[2]  Budmerice, Slovakia, “Teaching tennis in virtual environment,” Proceedings of the 18th spring conference on Computer graphics, Budmerice, Slovakia, pp. 49-54, (2002).
[3]  Benoît, B., and Franck, M., I. E., K., Laetitia, F. and Bruno, A., “Virtual reality applied to sports: do handball goalkeepers react realistically to simulated synthetic opponents?,” Proceedings of the 2004 ACM SIGGRAPH international conference on Virtual Reality continuum and its applications in industry, Singapore, pp. 210-216, (2004).
[4]  Viknashvaran, N., and Kok, Wai, W., I. E., “Distinguishing games and simulation games from simulators,” ACM Computers in Entertainment, Vol. 5, No. 2, Pp.1-18, (2006).
[5]  Andreassi, J. L., Psychophysiology: Human Behavior and Physiological Response, 4th Edition. Hillsdale, Lawrence Erlbaum, New York, USA, (2000).
[6]  Vick, R. M. and Ikehara, C. S., “Methodological Issues of Real Time Data Acquisition from Multiple Sources of Physiological Data,” Proceedings of the 36th Hawaii International Conference on System Sciences, Hawaii, USA, (2002).
[7]  Guyton, A. C., Basic human physiology: Normal function and mechanisms of disease, Saunders, Philadephia, USA, (1977).
[8]  Lowenstein, O. and Loewenfield, I. E., Muscular mechanisms, Academic Press, New York, USA, (1962).
[9]  Hess, E. H., and Polt, J. M., “Pupil size as related to interest value of visual stimuli,” Science, Vol.132, pp. 349-350, (1960).
[10] Hess, E. H., and Polt, J. M., “Pupil size in relation to mental activity during simple problem solving,” Science, Vol. 143, pp. 1190-1192, (1964).
[11]  Polt, J. M., “Effect of threat of shock on papillary response in a problem-solving situation,” Perception and Motor Skills, Vol. 31, pp. 587-593, (1970).
[12]  Andreassi, J. L., “Alpha and problem: A demonstration,” Perceptual & Motor Skills, Vol. 36, pp. 905-906, (1973).
[13]  Yarbus, A.L. Eye movements and vision, Plenum, New York, USA, (1967).
[14]  Nathan C., and John D., I.E., “Short talks-Specialized section: gaze and information navigation: Gaze- vs. hand-based pointing in virtual environments,” CHI '03 extended abstracts on Human factors in computing systems, Florida, USA, pp. 772-773, (2003).
[15]  Andrew, T., and Eric M., I.E., “Tracking: Binocular eye tracking in VR for visual inspection training,” Proceedings of the ACM symposium on Virtual reality software and technology, Banff, Alberta, Canada, pp. 1-8, (2001).
[16]  Iijima, A.; and Haida, M.; I.E., “Development of an eye information analysis system with a small display for evaluation of eye tracking functions,” EMBS/BMES Conference, Proceedings of the Second Joint, Houston, TX, USA, Vol. 3, pp. 23-26, (2002).
[17]  Polya, G., How to Solve It, Doubleday Anchor, NY, USA, (1957).
[18]  McGhee, P., Thinking psychologically, Palgrave, Basingstoke, (2001).
[19]  Hammond and John, S., Smart choices: a practical guide to making better decisions, Harvard Business School Press, Boston, USA, (1999).
[20]  Edward, W. and Fasolo, B., “Decision Technology,” Annual Review of Psychology, Vol. 52, pp.581-606, (2001).
[21]  Tecce, J. J., Psychology, physiology and experimental. In McGraw-Hill yearbook of science and technology, New York, USA, (1992).
[22]  Wang, Y., Liu, D. and Ruhe, G., “Formal Description of the cognitive process of decision making,” IEEE International Conference on Cognitive Informatics, (2004).
[23]  Kevin, B., Korb, A. E. and Nicholson, Bayesian Artificial Intelligence, Chapman and Hall/CRC, London, England, (2003).
[24]  Ritter, W., Vaughan, H. G., “Orienting and habituation to auditory stimuli: A study of short term changes in average evoked responses.” Electroencephalography and Clinical Neurophysiology, 25, 550-556, (1968).
[25]  Rohrbaugh, J. W., Donchin, E., “Decision making and the P300 component of the cortical evoked response.” Perception and Psychophysics, 15, 368-374, (1974).
[26]  Cacioppo, J. T., Bush, L. K., “Microexpressive facial actions as a function of affective stimuli: Replication and extension.” Personality and Social Psychology Bulletin, 18, 515-526, (1990).
[27]  Duffy, E., Activation and behavior. 1996New York: Wiley.
[28]  Fowles, DC. “The eccrine system and electrodermal activity.” In Psychophysiology, ed. MGH Coles, E Donchin, SW Porges, 51-96, Guilford Press, New York, (1986).
[29]  Gould, J. D. & Schaffer, A. “Eye-movement parameters in pattern recognition.” Journal of Experimental Psychology, 74, 225-229. (1967).
[30]  Nakano, A. “Eye movements in relation to mental activity of problem solving.” Psychologia: An International Journal of Psychology in the Orient, 14, 200-207. (1971).
[31]  Bakan, P., “Hypnotizability, laterality of eye movement and functional brain asymmetry.” Perceptual and motor skills, 28, 927-932. (1969).
[32]  Scott, D. & Findlay, J. M., “Visual search, eye movements and display units, Human factors report”, University of Durham, South Road, Durham DH1 3LE, UK. (1993).
[33] Baluja, S., and D. Pomerleau, “Non-Intrusive Gaze tracking using artificial neural networks.” Research Paper CMU-CS-94-102, School of Computer Science, Carnegie Mellon University, Pittsburgh PA. (1994).
[34] Salvendy, Handbook of Human Factors and Ergonomics. Second edition, p 744. (1997).