透過生理數據分析的VR戰車訓練系統，評估壓力對認知專注力與穩定性的影響及通過多次訓練表現驗證系統有效性

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姓名

黃鵬緒(Peng-Xu Huang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

透過生理數據分析的VR戰車訓練系統，評估壓力對認知專注力與穩定性的影響及通過多次訓練表現驗證系統有效性
(Evaluating the Impact of Stress on Cognitive Focus and Stability Using a VR WarCar Training System)

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至系統瀏覽論文 (2026-7-29以後開放)

摘要(中)

本研究使用整合生理數據分析的VR WarCar訓練系統，探討壓力對認知專注力和穩定性的影響。主要目的是探索高壓訓練環境如何影響認知功能和駕駛表現。通過模擬高壓情境，我們旨在評估反覆暴露於壓力下是否能改善任務表現和壓力管理，這使軍事人員能夠在高壓條件下有效地運作。先前的研究強調了壓力在各種高風險職業中的關鍵作用，如軍事，壓力引起的認知損害和生理反應可能會顯著影響結果。然而，目前的訓練系統在模擬真實壓力源和精確測量其影響方面存在不足。本研究通過利用VR技術創建沉浸式高壓環境，並結合多模態生理數據分析（包括EEG、HRV和眼動追蹤）來填補這一空白。我們的研究涉及一般組和運動組兩組受試者，他們進行了多次訓練。結果表明，通過VR系統反覆暴露於高壓條件下顯著改善了受試者的認知表現和駕駛穩定性。多模態生理信號與主觀壓力評估的數據融合提供了對壓力反應的可靠評估。研究證實了基於VR的訓練系統在提高高壓環境中的表現效果，並提供了壓力訓練對一般人群和專業運動員不同影響的見解。未來的工作將專注於細化訓練方案，並擴展系統在各種高壓職業中的應用。

摘要(英)

This study investigates the impact of stress on cognitive focus and stability using a VR WarCar training system integrated with physiological data analysis. The primary objective is to explore how high-pressure training environments influence cognitive functions and driving performance. By simulating high-stress conditions, we aim to evaluate the effectiveness of repeated stress exposure in improving task performance and managing stress that enables Military personnel to perform effectively under high-stress conditions. Previous research has highlighted the critical role of stress in various high-stakes professions, such as the military, where cognitive impairments and physiological responses due to stress can significantly affect outcomes, yet there is a gap in training systems that can simulate real-world stressors and accurately measure their effects. This study addresses this gap by utilizing VR technology to create immersive high-pressure environments and combining it with multimodal physiological data analysis, including EEG, HRV, and eye-tracking. Our study involved two participant groups, general and sports groups, underwent multiple training sessions. Results indicate that repeated exposure to high-stress conditions through the VR system significantly improved participants′ cognitive performance and driving stability. Data fusion of multimodal physiological signals with subjective stress assessments provided a robust evaluation of stress responses. The study demonstrates the effectiveness of the VR-based training system in enhancing performance in high-stress environments and offers insights into the differential impacts of stress training on general individuals and professional athletes. Future work will focus on refining the training protocols and expanding the system′s application to various high-stress professions.

關鍵字(中)

★ 壓力訓練
★ 虛擬現實
★ 認知專注力
★ 眼動追蹤
★ 腦電圖
★ 心率變異性

關鍵字(英)

★ Stress Training
★ Virtual Reality
★ Cognitive Focus
★ Eye-Tracking
★ EEG
★ HRV

論文目次

摘要...............I
Abstract...........II
致謝...............IV
Table of Contents..V
List of Figures....VI
List of Tables.....VII
1. Introduction....1
2. Related Work....5
3. Method..........14
4. Result..........31
5. Discussion......59
6. Conclusion......62
Reference..........64

參考文獻

[1] N. Schneiderman, G. Ironson, and S. D. Siegel, “Stress and health: psychological, behavioral, and biological determinants,” Annual Review of Clinical Psychology, vol. 1, pp. 607–628, 2005.
[2] C. K. McClernon, M. E. McCauley, P. E. O’Connor, and J. S. Warm, “Stress training improves performance during a stressful flight,” Human Factors: The Journal of Human Factors and Ergonomics Society, vol. 53, pp. 207–218, 2011.
[3] A. Flood and R. Keegan, “Cognitive resilience to psychological stress in military personnel,” Frontiers in Psychology, vol. 13, p. 809003, Mar 2022.
[4] E.-H. Lee, “Review of the psychometric evidence of the perceived stress scale,” Asian Nursing Research, vol. 6, no. 4, pp. 121–127, 2012. [Online]. Available: https://doi.org/10.1016/j.anr.2012.08.004
[5] L. C. Ohlson and J. Hammermeister, “Effects of concentration disruption on simulated basic rifle marksmanship scores among stryker brigade soldiers,” Journal of Instructional Psychology, vol. 38, no. 2, pp. 105– 108, 2011.
[6] N. Kanaka, T. Matsuda, Y. Tomimoto, Y. Noda, E. Matsushima, M. Matsuura, and T. Kojima, “Measurement of development of cognitive and attention functions in children using continuous performance test,” Psychiatry and Clinical Neurosciences, vol. 62, pp. 135–141, 2008.
[7] S. Hochman, “The effects of stress on stroop color-word performance,” Psychonomic Science, vol. 9, pp. 475–476, 1967.
[8] J. A. Cannon-Bowers and E. Salas, Eds., Making decisions under stress: Implications for individual and team training. American Psychological Association, 1998. [Online]. Available: https://doi.org/10.1037/10278000
[9] T. Driskell, S. Sclafani, and J. E. Driskell, “Reducing the effects of game day pressures through stress exposure training,” Journal of Sport Psychology in Action, vol. 5, no. 1, pp. 28–43, 2014.
[10] N. Munla, M. Khalil, and A. Shahin, “Driver stress level detection using hrv analysis,” in IEEE International Conference on Advances in Biomedical Engineering (ICABME). IEEE, 2015.
[11] M. Prachyabrued, D. Wattanadhirach, and R. B. Dudrow, “Toward virtual stress inoculation training of prehospital healthcare personnel: A stress-inducing environment design and investigation of an emotional connection factor,” in 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). IEEE, 2019.
[12] G. Giannakakis, D. Grigoriadis, and K. Giannakaki, “Review on psychological stress detection using biosignals,” IEEE Transactions on Affective Computing, jul 2019.
[13] S. Gedam and S. Paul, “A review on mental stress detection using wearable sensors and machine learning techniques,” IEEE Access, vol. 9, pp. 84045–84066, 2021.
[14] R. M. Clifford, S. Jung, S. Hoermann, and M. Billinghurst, “Creating a stressful decision making environment for aerial firefighter training in virtual reality,” in 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). IEEE, 2019.
[15] G. Aguilera, Chapter 8 - The Hypothalamic–Pituitary–Adrenal Axis and Neuroendocrine Responses to Stress. Academic Press, 2012, pp. 175– 196.
[16] R. Blanco, J. Calle, and A. Uribe-Quevedo, “Development of a driving vr prototype for distraction awareness employing eye tracking,” in IEEE 6th Global Conference on Consumer Electronics (GCCE), 2017.
[17] P. Tian, Y. Wang, and Y. Lu, “Behavior analysis of indoor escape routefinding based on head-mounted vr and eye tracking,” in International Conference on Internet of Things and IEEE Green Computing and Communications and IEEE Cyber, Physical and Social Computing and IEEE Smart Data, 2019.
[18] H. J. Foy and P. Chapman, “Mental workload is reflected in driver behaviour physiology eye movements and prefrontal cortex activation,” Applied Ergonomics, vol. 73, pp. 90–99, 2018.
[19] G. Jun and K. G. Smitha, “Eeg based stress level identification,” in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Budapest, Hungary: IEEE, 2016, pp. 003270–003274.
[20] A. Hag, D. Handayani, T. Pillai, T. Mantoro, M. Kit, and F. Al-Shargie, “Eeg mental stress assessment using hybrid multi-domain feature sets of functional connectivity network and time-frequency features,” Sensors, vol. 21, p. 6300, 2021.
[21] A. Nemcovˇ a´ et al., “Multimodal features for detection of driver stress and fatigue: Review,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 6, pp. 3214–3233, June 2021.
[22] K. M. Dalmeida and G. L. Masala, “Hrv features as viable physiological markers for stress detection using wearable devices,” Sensors, vol. 21, no. 8, p. 2873, 2021.
[23] D. Hernando, S. Roca, J. Sancho, A. Alesanco, and R. Bail´ on, “Valida-´ tion of the apple watch for heart rate variability measurements during relax and mental stress in healthy subjects,” Sensors, vol. 18, no. 8, p. 2619, 2018.
[24] T. Magaki and M. Vallance, “Developing an accessible evaluation method of vr cybersickness,” in 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). IEEE, 2019.
[25] F. Theurl, M. Schreinlechner, N. Sappler, M. Toifl, T. Dolejsi, F. Hofer, C. Massmann, C. Steinbring, S. Komarek, K. Molgg¨ et al., “Smartwatchderived heart rate variability: a head-to-head comparison with the gold standard in cardiovascular disease,” European Heart Journal - Digital Health, vol. 4, no. 3, pp. 155–164, May 2023.
[26] W. Suess, A. Alexander, D. Smith, H. Sweeney, and R. Marion, “The effects of psychological stress on respiration: A preliminary study of anxiety and hyperventilation,” Psychophysiology, vol. 17, no. 6, pp. 535–540, 1980. [Online]. Available: https://doi.org/10.1111/j.14698986.1980.tb02293.x
[27] M. Elgendi and C. Menon, “Machine learning ranks ecg as an optimal wearable biosignal for assessing driving stress,” IEEE Access, vol. 8, pp. 34362–34374, 2020.
[28] S. Jesmin, M. S. Kaiser, and M. Mahmud, “Towards artificial intelligence driven stress monitoring for mental wellbeing tracking during covid19,” in 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Melbourne, Australia, 2020, pp. 845–851.
[29] K. Tao, Y. Huang, Y. Shen, and L. Sun, “Automated stress recognition using supervised learning classifiers by interactive virtual reality scenes,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 2060–2066, 2022.
[30] R. Vaitheeshwari, S.-C. Yeh, E. H.-K. Wu, J.-Y. Chen, and C.-R. Chung, “Stress recognition based on multiphysiological data in high-pressure driving vr scene,” IEEE Sensors Journal, vol. 22, no. 20, pp. 19897– 19907, Oct. 2022.
[31] V. Hoareau, C. Godin, F. Dutheil, and M. Trousselard, “The effect of stress management programs on physiological and psychological components of stress: The influence of baseline physiological state,” Applied Psychophysiology and Biofeedback, vol. 46, no. 3, pp. 243– 250, Sep 2021, epub 2021 May 12.
[32] J. Wang, H. Wang, J. Jiang, X. Cheng, K. Xu, F. Xia, L. Chang, Y. Ji, and Z. Feng, “The validity of virtual courage for trainees in high-risk occupations,” Psychology Research and Behavior Management, vol. 15, pp. 1783–1796, 2022.
[33] F. Nenna, D. Zanardi, and L. Gamberini, “Enhanced interactivity in vrbased telerobotics: An eye-tracking investigation of human performance and workload,” International Journal of Human-Computer Studies, vol. 177, p. 103079, 2023.
[34] N. Kahlbrock, M. Butz, E. S. May, and A. Schnitzler, “Sustained gamma band synchronization in early visual areas reflects the level of selective attention,” Neuroimage, vol. 59, no. 1, pp. 673–681, 2012.
[35] L. J. Palladino, Find Your Focus Zone. Simon and Schuster, 2008.

指導教授

葉士青吳曉光(Shih-Ching Yeh Hsiao-Kuang Wu)

審核日期

2024-8-1

推文