| 摘要: |
| 目的 对多源生理指标的研究现状进行梳理和分析，旨在为人因测评提供理论支撑。方法 从人因测评和多源生理指标概念出发，结合主客观测评理论模型与技术方法，梳理国内外眼动、脑电以及心率等多源生理指标融合的研究现状，分析归纳人机协同领域的人因测评研究热点与趋势。结论 如何对人机协同进行全面精准的评估是当前学界面临的重要挑战。研究应以多源生理指标融合为着力点，建立跨领域的多维评估指标体系，驱动人机协同向着多源融合的方向发展，以提升人因测评在人机协同应用的可靠性。 |
| 关键词: 人机协同 人因测评 多源生理指标 |
| DOI：10.19554/j.cnki.1001-3563.2025.04.001 |
| 分类号: |
| 基金项目:国家自然科学基金(52175469);核电安全监控技术与装备国家重点实验室课题(K-A2021.419);江苏省自然科学基金(BK20221490) |
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| Research Progress of Multi-source Physiological Indicators for Human-computer Collaboration |
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WU Xiaoli, YAN Biao, WU Yuhan, ZHANG Xinyue,OU Yilin, QU Hao, WU Chuanyu
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(1. Research Centre of Human Cyber Physical Integration and Intelligent Interaction, Nanjing University of Science and Technology, Nanjing 210094, China;2. Key Laboratory of Ministry Industry and Information Technology for Language Information Processing and Application, Nanjing 210094, China)
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| Abstract: |
| The work aims to review and analyze the current research on multi-source physiological indicators, offering theoretical support for human factor evaluation. By examining the concepts of human factor assessment and multi-source physiological indicators and integrating both subjective and objective evaluation models and techniques, the integration of physiological indicators such as eye movement, EEG, and heart rate were sorted out across Chinese and international studies. Furthermore, the key research trends and hotspots in human factor assessment within the field of human-computer collaboration were identified and summarized. A significant challenge that the academic community faces is how to conduct a comprehensive and accurate evaluation of human-computer collaboration. Future research should prioritize the integration of multi-source physiological indicators and the development of a cross-domain, multi-dimensional evaluation indicator system, which can steer human-computer collaboration toward multi-source integration, improving the reliability of human factor assessment applications in human-computer collaboration. |
| Key words: human-computer collaboration human factor assessment multi-source physiological indicators |
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