製造業是台灣目前的主流產業，從業人數約有300萬人，其安全自然成為重要議題。根據勞動部勞工保險局的統計資料顯示，2015年製造業的職業災害案件共15,981筆，居全部行業別之首，職業災害案件數更是排名第二的建築業之兩倍，其中主要的災害類型如「被夾、被捲」、「被刺、割、擦傷」等皆與人為操作失誤有密切的關係，因此分析並改善人為失誤是有其價值與必要性的。本研究以三合一送料整平機為例，首先使用人為失誤分析技術找出失誤機率較高的操作動作，接著再進行作業流程的模擬實驗，計算各項操作動作的失誤率，藉以驗證失誤分析結果的正確性，同時也蒐集主、客觀反應並評估其與人為失誤之間的相關性。
在第一階段的研究中，透過階層式任務分析法（Hierarchical Task Analysis, HTA）建立完整的三合一送料整平機操作流程，再使用人為失誤評估及降低方法（Human Error Assessment and Reduction Technique, HEART）估算各項操作動作的失誤率。為了驗證失誤分析結果的正確性，第二階段招募30位年齡介於21歲至28歲的男性研究參與者，模擬三合一送料整平機的操作流程，一方面記錄過程中發生的各類失誤、計算整體的失誤率，並與第一階段的結果比較，一方面也蒐集20項腦電波（Electroencephalography, EEG）指標、8項心率變異指標( Heart Rate Variability, HRV )與NASA主觀工作負荷評量（Task Load Index）。此模擬實驗共包含6項主作業，且研究參與者區分為兩組，分別給予1小時 (15人，平均年齡23 歲) 及3小時 (15人，平均年齡22.5歲) 的操作訓練。在第二階段是以二因子變異數分析評估訓練時數 (1、3小時)與不同難度的作業 (依照難度分為易、中、難) 在EEG與HRV平均振幅指標的差異。最後， 在第三階段針對失誤前5秒與失誤發生至失誤排除後的區間進行單因子變異數分析，評估EEG與HRV平均振幅指標的差異。
研究結果顯示，使用HEART所估算出來的失誤率和實際失誤發生次數達到顯著的正相關 (相關係數= 0.55)。在EEG方面，結果顯示有過3小時訓練的研究參與者表現較好 (α較高)、心智負荷較低 (α/θ較低)、同時實際操作失誤率也較低。主作業在進行難度的分類後，發現在難度最高 (同時也是失誤率最高) 的作業裡，腦波指標 (β、 θ、 α/θ)顯示研究參與者的心智負荷較高、警覺性較低、反應也較慢；此外，也發現EEG O1-β 在失誤發生後的數值明顯較高。
整體而言，HEART能夠有效地預測失誤的發生機率，同時EEG 指標也能夠有效地評估作業員在受訓後執行作業的操作可靠度，企業亦能使用EEG 指標結果來訂定訓練標準。此外，因作業的難度與腦波有所關聯，也能夠用以評估人為失誤發生的可能性。EEG的 O1-β指標在失誤前相較低，顯示警覺性降低可能是失誤發生的原因。另外， HRV並不適合用來評估人為失誤發生的可能性。

Manufacturing industry is the current mainstream in Taiwan. The safety of three million employees involved in this industry was becoming an important issue. According to the sta-tistical data of occupational injury from Bureau of Labor Insurance in Taiwan, a total of 15,981 cases of occupational disaster occurred in the manufacturing industry and the num-ber was twice as many as the construction industry which ranked 2nd among all industries. This sort of injured such as pinch, scratch and cut were mainly associated with human error. Therefore, it is essential to analyze the occurrence of human error and improve it as well. This study aimed at human error risk quantification of the operation task of TOMAC 3 in 1 precision uncoiler and straightener. An experiment was also carried out to collect the objec-tive and subjective parameters to assess the correlation with human error and to verify the accuracy of the risk quantification simultaneously.
In Phase I of this study, Hierarchical Task Analysis (HTA) was conducted to construct an operational framework and Human Error Assessment and Reduction Technique (HEART) and also was implemented to quantify the human error probability. To verify the result of phase I, 30 participants ranging from 21 to 28 years old were recruited in the experiment. The intention of this experiment was to simulate the operation of TOMAC 3 in 1 precision uncoiler and straightener in a real-time scenario and to record the number of errors occurred as well. Twenty Electroencephalography (EEG) indices, 8 Heart Rate Variability (HRV) in-dices, and NASA-TLX rating were included in the experiment. Participants were separated into two groups. In phase II, fifteen participants (mean age = 23) in novice group were trained for 1 hour and fifteen participants (mean age = 22.5) in experienced group were trained for 3 hours. Two-way ANOVA was used to identify the differences of EEG and HRV in two factors: training hour (1hr & 3hr) and difficulty of the task (easy, medium & hard). Besides, in phase III, the change of EEG and HRV during pre-error (five seconds before the error occurred) to error solved (the moment of error occurred till error was solved) were also investigated by one-way ANOVA.
According to the results of correlation analysis, the number of errors is correlated with the HEART model (r2 = 0.55). With regard to the result of EEG, we found that the experi-enced group had better performance, lower mental workload, and lower error rate than the novice group during machine operation. Besides, the results indicated that participants had higher mental workload, lower level of alertness, and slower response when they were per-forming a hard task. We also found that EEG O1-β would increase after an error occurred.
These findings suggest that the EEG measures are able to help to assess the operator’s operational reliability, as well as to determine the amount of training required for workers. In addition, EEG indices can be used to predict the occurrence of human error, and the oc-currence of human error maybe related to the reduction of alertness. Furthermore, HRV may not be an effective measure to evaluate the occurrence of human error.

摘 要 I
Abstract III
Table of Contents V
List of Tables VII
List of Figures IX
Chapter 1 Introduction 1
1.1 Background and motivation 1
1.2 Objectives 4
1.3 Research framework 4
Chapter 2 Literature Review 6
2.1 Human error 6
2.1.1 Human error classification 7
2.1.2 Human error failure models 8
2.2 Human operational reliability assessment (HRA) 10
2.3 Human Error Assessment and Reduction Technique (HEART) 11
2.4 Concepts of mental workload 15
2.4.1 Electroencephalography (EEG) 16
2.4.2 The application of EEG 18
2.4.3 Event Related Potential (ERP) 18
2.4.4 Measuring mental workload by EEG and ERP 19
2.5 NASA-TLX 20
2.6 Summary 21
Chapter 3 Methodology 22
3.1 TOMAC 3 in 1 precision uncoiler and straightener 24
3.1.1 Sequences of Operation 24
3.2 Participants 33
3.3 Phase 1: Human error assessment 33
3.3.1 Hierarchical Task Analysis (HTA) 33
3.3.2 Quantitative analysis 35
3.4 Phase 2: Applicability of EEG indices in human error research 37
3.4.1 Experimental design and measurements 37
3.4.2 Experiment procedure 39
3.5 Statistical analysis 41
Chapter 4 Results 42
4.1 Phase I: Result of human error identification 42
4.1.1 Quantify human error probability by HEART 43
4.2 Phase II: Result of experiment on training hour and task complexity 46
4.2.1 Training hour 55
4.2.2 Task difficulty 58
4.3 Phase III: The results of human error 62
4.4 Correlation analysis 64
Chapter 5 Discussion 66
Chapter 6 Conclusion 73
References 75
Appendix I 80
Appendix II 87

1. 鄭慶民 (2013)。人為失誤辯識技術整合與應用-以化學品鋼瓶更換作業為例。國立清華大學工業工程與工程管理學系碩士論文。
2. Annett, J. (2004). Hierarchical task analysis. In: Diaper, D., Stanton, N. A. (Eds.). The Handbook of Task Analysis for Human-Computer Interaction. Lawrence Erlbaum Asso-ciates, Mahwah, NJ, 67-82.
3. Annett, J., & Duncan, K. D. (1967). Task analysis and training design. United States.
4. Bureau of Labor Insurance of Taiwan. (2015). Report of occupational permanent disabil-ity benefits, by disability type, sex, various insured laborer and age. Retrieved from http://www.bli.gov.tw/en/sub.aspx?a=mn3fE5kgiec%3d
5. Casali, J. G., & Wierwille, W. W. (1984). Communications-imposed pilot workload - a comparison of sixteen estimation techniques. 2nd Conference of Symposium on Aviation Psychology, Columbus, OH, USA.
6. Cheng, S. Y., & Hsu, H. T. (2011). Mental fatigue measurement using EEG. In Nota G. (Ed.), Rish Management Trends. InTech.
7. Cheng, S. Y., Lee, H. Y., Shu, C. M., & Hsu, H. T. (2007). Electroencephalographic study of mental fatigue in visual display terminal task. Medical and Biological Engi-neering, 27(3), 124-131.
8. Delaney, J. P. A., & Brodie, D. A. (2000). Effects of short-term psychological stress on the time and frequency domains of heart-rate variability. Perceptual and Motor Skills, 91(2), 515-524.
9. Dhillon, B. S. (1989). Human errors: a review. Microelectronics Reliability, 29(3), 299-304.
10. Eggemeier, F. T., Wilson, G. F., Kramer, A. F., & Damos D. L. (1991). Chapter9: Work-load assessment in multi-task environments. In Damos, D. (Ed.), Multiple-task perfor-mance (pp. 207). London, England: Taylor & Francis. ISBN: 0-85066-757-7
11. Eoh, H. J., Chung, M. K., & Kim, S. H. (2005). Electroencephalographic study of drowsiness in simulated driving with sleep deprivation. International Journal of Industri-al Ergonomics, 35(4), 307-320.
12. Falkenstein, M., Hohnsbein J., & Hoormann, J. (1995). Effects of choice complexity on different subcomponents of the late positive complex of the event-related potential. Electroencephalography and Clinical Neurophysiology, 92(2), 148-160.
13. Gopher, D. (1986). Workload: An examination of the concept. In Boff, K. R., Kaufman, L., & Thomas, J. P. (Eds.), Handbook of perception and human performance, Volume 2: Cognitive processes and performance. (pp. 1143). Oxford, England: John Wiley & Sons.
14. Gotman, J. (1982). Automatic recognition of epileptic seizures in the EEG. Electroen-cephalography and Clinical Neurophysiology, 54(5), 530-540.
15. Gregory, B., & Kaprielian, V. S. (2016). Patient safety - quality improvement. Retrieved from http://patientsafetyed.duhs.duke.edu/module_e/swiss_cheese.html.
16. Hales, B. M., & Pronovost, P. J. (2006). The checklist - a tool for error management and performance improvement. Journal of Critical Care, 21(3), 231-235.
17. Harmon-Jones, E., & Allen, J. J. (1998). Anger and frontal brain activity: EEG asym-metry consistent with approach motivation despite negative affective valence. Journal of Personality and Social Psychology, 74(5), 1310-1316.
18. Heinrich, H.W. (1959). Industrial Accidents Prevention. New York, NY: McGraw-Hill.
19. Helmreich, R. L. (2000). On error management: Lessons from aviation. Bitish Medical Journal, 320(7237) , 781-785.
20. Holzer, H. J., Block, R. N., Cheatham, M., & Knott, J. H. (1993). Are training subsidies for firms effective? The Michigan experience. ILR Review, 46(4), 625-636.
21. Hsu, B. W., Wang, M. J. J., Chen, C. Y., & Chen, F. (2015). Effective indices for moni-toring mental workload while performing multiple tasks. Perceptual and motor skills, 121(1), 94-117.
22. Kamiński, J., Brzezicka, A., Gola, M., & Wróbel, A. (2012). Beta band oscillations en-gagement in human alertness process. International Journal of Psychophysiology, 85(1), 125-128.
23. Käthner, I., Wriessnegger, S. C., Müller-Putz, G. R., Kübler, A., & Halder, S. (2014). Ef-fects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain-computer interface. Biological Psychology, 102, 118-129.
24. Klimesch, W., Doppelmayr, M., Russegger, H., Pachinger, T., & Schwaiger, J. (1998). Induced alpha band power changes in the human EEG and attention. Neuroscience Let-ters, 244(2), 73-76.
25. Linden, M., Habib, T., & Radojevic, V. (1996). A controlled study of the effects of EEG biofeedback on cognition and behavior of children with attention deficit disorder and learning disabilities. Biofeedback and Self-Regulation, 21(1), 35-49.
26. Luck, S. J., Woodman, G. F., & Vogel, E. K. (2000). Event-related potential studies of attention. Trends in Cognitive Sciences, 4(11), 432-440.
27. McFadden, K. L., & Towell, E. R. (1999). Aviation human factors: A framework for the new millennium. Journal of Air Transport Management, 5(4), 177-184.
28. Moroney, W. F., Biers, D. W., Eggemeier, F. T., & Mitchell, J. A. (1992). A comparison of two scoring procedures with the NASA Task Load Index in a simulated flight task. In Aerospace and Electronics Conference, 1992. NAECON 1992, Proceedings of the IEEE 1992 National (Vol. 2, pp. 734-740). Dayton, OH: NAECON.
29. Niedermeyer, E. & Lopes da Silva, F. H. (1993). Electroencephalography: Basic princi-ples, clinical applications and related fields, 3rd edition. Philadelphia, PA: Lippincott Williams & Wilkins. ISBN: 0-7817-5126-8
30. Perry, C. M., Sheik-Nainar, M. A., Segall, N., Ma, R., & Kaber, D. B. (2008). The effects of physical workload on situation awareness and cognitive task performance in multi-tasking. Theoretical Issue in Ergonomics Science, 9(2), 95-113.
31. Rasmussen, J. (1983). Skills, rules, and knowledge; signals, signs, and symbols, and oth-er distinctions in human performance models. IEEE Transactions on Systems, Man, and Cybernetics, 13(3), 257-266.
32. Reason, J. (1990). Human error. New York, NY: Cambridge University Press. ISBN: 0-521-31419-4
33. Sauseng, P., Klimesch, W., Schabus, M., & Doppelmayr, M. (2005). Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory. International Journal of Psychophysiology, 57(2), 97-103.
34. Smith, M. E., Gevins, A., Brown, H., Karnik, A., & Du, R. (2001). Monitoring task loading with multivariate EEG measures during complex forms of human-computer in-teraction. Human Factors, 43(3), 366-380.
35. Sterman, M. B., & Mann, C. A. (1995). Concepts and applications of EEG analysis in aviation performance evaluation. Biological Psychology, 40(1), 115-130.
36. Swain, A. D., & Guttmann, H. E. (1983). Handbook of human-reliability analysis with emphasis on nuclear power plant applications. Final report. United States.
37. Teplan, M. (2002). Fundamentals of EEG measurement. Measurement Science Review, 2(2), 1-11.
38. Vanderhaegen. (2001). A non-probabilistic prospective and retrospective human opera-tional reliability analysis method - application to railway system. Reliability Engineering and System Safety, 71(1), 11-13.
39. Williams, J. C. (1986). HEART - a proposed method for assessing and reducing human error. Paper presented at the 9th Advances in Reliability Technology Symposium, Uni-versity of Bradford, UK.
40. Williams, J. C. (1988). A data-based method for assessing and reducing human error to improve operational performance. Proceedings of the IEEE 4th Conference on Human Factors and Power Plants, Monterey, California, 436-450.
41. Yamada, F. (1998). Frontal midline theta rhythm and eye blinking activity during a VDT task and a video game: useful tools for psychophysiology in ergonom-ics. Ergonomics, 41(5), 678-688.
42. Young, M. S., & Stanton, N. A. (2002). Malleable attentional resources theory: A new explanation for the effects of mental underload on performance. Human Factors: The Journal of the Human Factors and Ergonomics Society, 44(3), 365-375.
43. Zhang, L., Samet, J., Caffo, B., Bankman, I., & Punjabi, N. M. (2008). Power spectral analysis of EEG activity during sleep in cigarette smokers. CHEST Journal, 133(2), 427-432.