He Shiyi, Qi Dongsheng, Guo Enkai, Wang Liyun, Ouyang Yewei, Zheng Lan
Key Laboratory of Physical Fitness and Exercise Rehabilitation of Hunan Province, Hunan Normal University, Changsha 410006, China.
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China.
Sensors (Basel). 2025 Apr 9;25(8):2377. doi: 10.3390/s25082377.
Monitoring the mental workload of construction workers is effective in detecting risky subjects because cognitive overload may threaten their safety. This study aimed to measure workers' mental workload caused by heat exposure using heart rate variability (HRV) and eye movement features. Inexperienced pipe workers ( = 30) were invited to perform an installation task in a normothermic (26 °C, 50% RH) and a hyperthermic (33 °C, 50% RH) condition. Their HRV and eye movement features were recorded as the inputs of training models classifying mental workload between the two thermal conditions, using supervised machine learning algorithms, including Support Vector Machines (SVM), KNearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF). The results show that applying eight HRV features through the KNN algorithm could obtain the highest classification accuracy of 90.00% (Recall = 0.933, Precision = 0.875, F1 = 0.903, AUC = 0.887). This study could provide a new perspective for monitoring the mental workload of construction workers, and it could also provide a feasible approach for the construction industry to monitor workers' mental workload in hot conditions.
监测建筑工人的心理负荷对于发现有风险的个体很有效，因为认知过载可能会威胁到他们的安全。本研究旨在利用心率变异性（HRV）和眼动特征来测量热暴露引起的工人心理负荷。邀请了30名经验不足的管道工人在常温（26℃，50%相对湿度）和高温（33℃，50%相对湿度）条件下执行安装任务。使用包括支持向量机（SVM）、K近邻（KNN）、线性判别分析（LDA）和随机森林（RF）在内的监督机器学习算法，将他们的HRV和眼动特征记录为训练模型的输入，以对两种热条件下的心理负荷进行分类。结果表明，通过KNN算法应用八个HRV特征可获得90.00%的最高分类准确率（召回率=0.933，精确率=0.875，F1=0.903，AUC=0.887）。本研究可为监测建筑工人的心理负荷提供新的视角，也可为建筑行业在炎热条件下监测工人的心理负荷提供一种可行的方法。
