Analysis on pulse rate variability for pilot workload assessment based on wearable sensor

IF 2.2 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Human Factors and Ergonomics in Manufacturing & Service Industries Pub Date : 2024-09-27 DOI:10.1002/hfm.21053
Yunbiao Wang, Chenyang Zhang, Chenglin Liu, Kun Liu, Fang Xu, Jixue Yuan, Chaozhe Jiang, Chuang Liu, Weiwei Cao
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Abstract

The workload levels of pilots directly affect their flight performance and the safety of the whole flight. To explore the real-time workload of pilots in different flight phases (takeoff, cruise, and landing), this paper leveraged National Aeronautics and Space Administration Task Load Index (NASA-TLX), a subjective evaluation scale, and PhotoPlethysmoGraphy (PPG) signals of 21 participants using a flight simulator and a wearable sensor. First, the workloads of pilots under different phases were explored by the NASA-TLX scales; secondly, the pulse rate variability (PRV) features were selected by variance analysis and random forest importance evaluation; finally, the performances of the k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) were compared for workload levels identification. It is shown that the workloads are ranked as follows: landing > takeoff > cruise. SDNN, CVCD, CVNNI, LF, TP, SD2, and SD2/SD1 were used as selected features with significant differences in different flight phases. In addition, machine learning models can effectively identify pilot workloads, and feature selection enhances the performance of both KNN and RF classifiers. The best identification of workload was achieved using the selected PRV features as inputs to the KNN classifier, with an average accuracy of 88.9%. Our results indicate that the KNN classifier and PRV features are suitable for identifying pilot workload. The pilot workload is highest during the landing phase, which provides a reference for flight safety management. The findings from this research could contribute to developing a robust pilot workload detection system and improve current flight operation safety regulations.

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基于可穿戴传感器的飞行员工作量评估脉搏变异性分析
飞行员的工作量水平直接影响其飞行性能和整个飞行的安全性。为了探索飞行员在不同飞行阶段(起飞、巡航和着陆)的实时工作量,本文利用美国国家航空航天局的任务负荷指数(NASA-TLX)、主观评价量表以及21名参与者使用飞行模拟器和可穿戴传感器获得的PPG(PhotoPlethysmoGraphy)信号。首先,通过 NASA-TLX 量表探讨了飞行员在不同阶段的工作负荷;其次,通过方差分析和随机森林重要性评估选择了脉率变异性(PRV)特征;最后,比较了 k-近邻(KNN)、随机森林(RF)和支持向量机(SVM)在工作负荷水平识别方面的性能。结果表明,工作负荷的排序如下:着陆> 起飞> 巡航。在不同飞行阶段,SDNN、CVCD、CVNNI、LF、TP、SD2 和 SD2/SD1 被选作具有显著差异的特征。此外,机器学习模型能有效识别飞行员的工作量,而特征选择能提高 KNN 和 RF 分类器的性能。使用选定的 PRV 特征作为 KNN 分类器的输入,实现了最佳的工作量识别,平均准确率为 88.9%。结果表明,KNN 分类器和 PRV 特征适用于识别飞行员的工作量。飞行员在着陆阶段的工作量最大,这为飞行安全管理提供了参考。这项研究的结果有助于开发一个强大的飞行员工作量检测系统,并改善目前的飞行运行安全法规。
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来源期刊
CiteScore
5.20
自引率
8.30%
发文量
37
审稿时长
6.0 months
期刊介绍: The purpose of Human Factors and Ergonomics in Manufacturing & Service Industries is to facilitate discovery, integration, and application of scientific knowledge about human aspects of manufacturing, and to provide a forum for worldwide dissemination of such knowledge for its application and benefit to manufacturing industries. The journal covers a broad spectrum of ergonomics and human factors issues with a focus on the design, operation and management of contemporary manufacturing systems, both in the shop floor and office environments, in the quest for manufacturing agility, i.e. enhancement and integration of human skills with hardware performance for improved market competitiveness, management of change, product and process quality, and human-system reliability. The inter- and cross-disciplinary nature of the journal allows for a wide scope of issues relevant to manufacturing system design and engineering, human resource management, social, organizational, safety, and health issues. Examples of specific subject areas of interest include: implementation of advanced manufacturing technology, human aspects of computer-aided design and engineering, work design, compensation and appraisal, selection training and education, labor-management relations, agile manufacturing and virtual companies, human factors in total quality management, prevention of work-related musculoskeletal disorders, ergonomics of workplace, equipment and tool design, ergonomics programs, guides and standards for industry, automation safety and robot systems, human skills development and knowledge enhancing technologies, reliability, and safety and worker health issues.
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