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論文名稱(中文) 辨識不同程度的心智負荷以及效能在持續注意力之下使用心律變異
論文名稱(英文) Classification of Different Mental Workload and Performance during Sustained Attention using Heart Rate Variability
校院名稱 成功大學
系所名稱(中) 電腦與通信工程研究所
系所名稱(英) Institute of Computer & Communication
學年度 98
學期 2
出版年 99
研究生(中文) 陳國揚
研究生(英文) Guo-Yang Chen
學號 q3697121
學位類別 碩士
語文別 中文
論文頁數 41頁
口試委員 口試委員-張建禕
口試委員-黃正能
口試委員-蔡文祥
口試委員-王振興
指導教授-詹寶珠
中文關鍵字 心智負荷  持續性注意力  心律變異 
英文關鍵字 Mental workload  Sustained attention  Heart rate variability 
學科別分類
中文摘要 各行各業所需之心智負荷程度不同,有的工作需要擁有高心智負荷能力,而有些工作則僅需要低心智負荷能力之工作者即可滿足。舉例來說,對於執行警覺性工作,如司機、雷達觀測員以及工廠操作員而言,集中注意力是此職務必備能力。然而,不是每一位應徵者都能在長工作時間下依舊保持注意力。基於這個理由,假使某工作需承受高心智負荷,那為了安全顧慮,挑選合適人才顯得更加重要。

本論文開發一套方法,利用心律變異去評估應徵者之心智負荷程度與其在不同持續注意力之任務中的表現程度。在此,心智負荷被分成低、中、高三種程度,接著利用心律變異的時間域、頻率域以及其非線性分析中擷取出特徵資訊並進行分類。另外,兩段式分類也被使用於本方法中去改善分類的效能。另外,本論文也可以從心律變異中之特定特徵如VLF、TP、SD12、SD2以及Sample Entropy去觀察應徵者在持續注意力時HRV趨勢的變化。並可將所得到之結果提供作為聘雇參考使用。
英文摘要 Different tasks require different mental workloads; some tasks require mild mental workload while some require high mental workload. For example, during conducting vigilance tasks, such as driving, observing radar system, or manipulating critical operations, it is essential to retain on sustained attention. However, some people have the capability keeping in sustained attention for a long time, while some do not have this capability. Because of this reason, it is important that if a task requires high mental workload, the proper people should be chosen for safety concern.

This thesis explores the use of heart rate variability (HRV) to classify the subjects’ mental workloads and performances in different sustained attention tasks. The mental workloads are categorized into low, medium and high levels. For the classification, features are extracted from time domain、frequency domain and nonlinear analysis of HRV. Furthermore, two-phase classification is also proposed to improve the classification performance. Finally this thesis also explores the trends of various HRV features, including VLF, TP, SD12, SD2 and Sample Entropy, when subject involves sustained attention. Results can be used as reference in properly assigning people to the works.
論文目次 第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 1
1.3 論文架構 2
第二章 文獻探討 3
2.1 背景知識 3
2.2 評估心智負荷方法 4
2.3 辨識方面 7
2.4 總結 8
第三章 實驗設計流程 9
3.1 實驗設計目的 9
3.2 測驗工具 9
第四章 實驗分析流程 13
4.1 前處理 14
4.2 特徵計算 15
4.3 正規劃 19
4.4 兩段式分類法 20
第五章 實驗結果與討論 30
5.1 心智負荷辨識結果 30
5.2 CPT辨識結果 32
5.3 LCT辨識結果 33
5.4 HRV趨勢觀察 34
第六章 結論與未來工作 38
參考文獻 39
參考文獻 [1] Pincus SM.,“ Approximate entropy as a measure of system complexity”, Proc Natl Acad Sci USA, 1991.

[2] Richman JS, Moorman JR. ”Physiological time-series analysis using approximate entropy and sample entropy.” Am J Physiol Heart Circ Physiol 278(6):H2039-H2049 , 2000.

[3] P. Pudil, J. Novovicova, J. Kittler, “Floating Search Method in Feature Selection”, Pattern Recognition Letters, 1994

[4] T. M Cover, P. E.Hart, “Nearest Neighbor Pattern Classification” IEEE Trans. on Information Theory, Vol IT-13, No. 1, pp. 21-27, 1967

[5] Xiaofei He, Partha Niyogi, Locality Preserving Projection, NIPS, 2003.

[6] Xiaofei He, Shuicheng Yan, Yuxiao Hu, Hong-Jiang Zhang, Learning a Locality Preserving Subspace for Visual Recognition, ICCV, 2003.

[7] M. Sugiyama, “Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis,” J. Machine Learning Research, vol. 8, pp. 1027-1061, 2007.

[8] J. Pan and W. J. Tompkins, “A real-time QRS detection algorithm,”IEEE Trans. Biomed. Eng., vol. BME-32, pp. 220–236, Mar. 1985.

[9] P.W. Kamen, H. Krum, and A.M. Tonkin, “Poincare Plot of Heart Rate Variability Allows Quantitative Display of Parasympathetic Nervous Activity,” Clinical Science, vol. 91, pp. 201-208, 1996.

[10] Lake DE, Richman JS, Griffin MP, Moorman JR: Sample entropy analysis of neonatal heart rate variability. Am J Physiol Regul Integr Comp Physiol , 2002,

[11] Noel, J. B., Bauer, K. W., & Lanning, J. W. Improving pilot mental workload classification through feature exploitation and combination, A feasibility study Computers & Operations Research, 32(10), 2713-2730, 2005.

[12] Laine et al., 2002 T.I. Laine, K.W. Bauer, J.W. Lanning, C.A. Russell and G.F. Wilson, Selection of input features across subjects for classifying crewmember workload using artificial neural networks., IEEE Transactions on Systems, Man and Cybernetics A Systems and Humans 32 (6) , pp. 691–704 , 2002.

[13] M. Brennan, M. Palaniswami, and P. Kamen. “Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability?.” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 48, NO. 11, NOVEMBER 2001.

[14] D. L. Swets and J. J. Weng, “Using discriminant eigenfeatures for image retrieval,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 831-836, 1996.

[15] M. Malik, J. T. Bigger, A. J. Camm, R. E. Kleiger, A. Malliani, A. J. Moss, and P. J. Schwartz, “Heart rate variability: standards of measurement, physiological interpretation and clinical use,” European Heart Journal, vol. 17, no. 3, pp. 354-381, 1996.

[16] Hart and Staveland, S.G. Hart and L.E. Staveland, “Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research”, In: P.A. Hancock and N. Meshkati, Editors, Human mental workload, Elsevier Science, Amsterdam , pp. 139–183, 1988.

[17] Valentino et al, D.A. Valentino, J.E. Arruda and S.M. Gold, “Comparison of QEEG and response accuracy in good vs. poorer performers during a vigilance task”, International Journal of Psychophysiology 15, pp. 123–134, 1993.

[18] Hilburn, B.G, ”Free Flight and Air Traffic Controller Mental Workload”, Ninth International Symposium on Aviation Psychology,1997.

[19] DeGangi Georgia and Porges Stephen, ”Neuroscience Foundations of Human Performance”, American Occupational Therapy Association Inc, 1990.

[20] J. A. Healey and R.W. Picard, ”Detecting stress during real world driving tasks using physiological sensors,” IEEE Trans. Intelligent Transportation Systems, vol. 6, no. 2,pp. 156-1566, 2005.

[21] Saeys, Y., Inza, I., Larra.naga, “A review of feature selection techniques in bioinformatics.Bioinformatics”, 23(19), 2507–2517, 2007.

[22] Holland, J. "Genetic algorithms." Scientific American, pp. 66-72, July 1992.

[23] L.Y. Chuang, H.W. Chang and C.J. Tu et al., Improved binary PSO for feature selection using gene expression data, Computational Biology andChemistry ,2008.

[24] J. Kennedy and R. Eberhart, “A discrete binary version of the particle swarm algorithm,” in Proc. IEEE Int. Conf. Systems, Man, Cybernetics,Computational Cybernetics, Simulation, pp. 4104–4108, vol. 5, 1997.

[25] R. W. Picard, E. Vyzas, and J. Healey, “Toward machine emotional intelligence:
Analysis of affective physiological state,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 23, no. 10, pp. 1175–1191, Oct. 2001.

[26] E. Crow, F. A. Davis, and M. W. Max_eld, “Statistics Manual.” Toronto, Canada: General Publishing Company, 2002.

[27] Geldmacher DS. “Effect of stimulus number, and target-to-distractor ratio,on the performance of random array letter-cancellation tasks.” brain and cognition,1996.

[28] Conners, C. K. “The Conners continuous performance test”. Toronto, Canada: Multi-Health Systems, 1994.
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