摘要
人-机系统的脑力负荷评估和作业过程中的脑力负荷检测是工效学的重要研究内容,基于生理信号实时脑力负荷监测能够实现根据脑力负荷在人-机系统中在作业人员与自动化系统之间动态分配任务,即自适应自动化,进而能够优化人-机系统设计、避免过高的脑力负荷、降低人误风险。基于生理信号实现脑力负荷的检测研究从最早NASA的探索性研究至今已有40多年,近十多年逐渐成为工效学中新的研究热点,并且基于脑电、心电、功能性近红外光谱的自适应自动化在诸如模拟飞行、模拟无人机控制等任务中已被证明能够改善作业绩效和作业人员的主观感受。但近年来部分研究报告也表明基于生理信号的脑力负荷检测存在跨人、跨时间、跨任务的挑战,未来还有较大发展空间。本综述将回顾基于生理信号的脑力负荷检测和基于脑力负荷的自适应自动化40年来的研究历程和最新研究进展。
Mental workload （MW） assessment of human-machine system （HMS） is one of the most important research field of ergonomics. Mental workload assessment based on physiological measures can dynamically allocate tasks between operator and the automation in HMS. This kind of closed-loop system is called adaptive automation （AA）, which can improve the design of HMS, avoid overload and reduce the risk of human error. It is more than 40 years since the first NASA＇ s psychophysiological measures based mental workload assessment study, and this kind of studies has attracted in- creasing attention in the latest ten years. And AA based on EEG, ECG and functional near infrared spectroscopy （fNIRS） has been proved to be able to improve performance and subjective feeling in tasks like flight simulation, UAV control and so on. But, some studies of recent years suggested that there are challenges in cross-subject, cross-session and cross-task MW assessment. There is a large space for development in future. We will review the 40-year develop- ment and the latest developments of physiological measures based MW assessment and AA in this review.
出处
《电子测量与仪器学报》
CSCD
北大核心
2015年第1期1-13,共13页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(81222021,31271062,61172008,81171423,51007063)
国家科技支撑计划(2012BAI34B02)
教育部新世纪优秀人才支持计划(NCET-10-0618)项目
