Monitoring Construction Workers’ Mental Workload Due to Heat Exposure Using Heart Rate Variability and Eye Movement: A Study on Pipe Workers
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. Inexperienc...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/8/2377 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850155041396097024 |
|---|---|
| author | Shiyi He Dongsheng Qi Enkai Guo Liyun Wang Yewei Ouyang Lan Zheng |
| author_facet | Shiyi He Dongsheng Qi Enkai Guo Liyun Wang Yewei Ouyang Lan Zheng |
| author_sort | Shiyi He |
| collection | DOAJ |
| description | 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 (<i>n</i> = 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. |
| format | Article |
| id | doaj-art-ad78cb8f013a446a81ea2cbf295feed7 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ad78cb8f013a446a81ea2cbf295feed72025-08-20T02:25:04ZengMDPI AGSensors1424-82202025-04-01258237710.3390/s25082377Monitoring Construction Workers’ Mental Workload Due to Heat Exposure Using Heart Rate Variability and Eye Movement: A Study on Pipe WorkersShiyi He0Dongsheng Qi1Enkai Guo2Liyun Wang3Yewei Ouyang4Lan Zheng5Key Laboratory of Physical Fitness and Exercise Rehabilitation of Hunan Province, Hunan Normal University, Changsha 410006, ChinaKey Laboratory of Physical Fitness and Exercise Rehabilitation of Hunan Province, Hunan Normal University, Changsha 410006, ChinaKey Laboratory of Physical Fitness and Exercise Rehabilitation of Hunan Province, Hunan Normal University, Changsha 410006, ChinaKey Laboratory of Physical Fitness and Exercise Rehabilitation of Hunan Province, Hunan Normal University, Changsha 410006, ChinaCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, ChinaKey Laboratory of Physical Fitness and Exercise Rehabilitation of Hunan Province, Hunan Normal University, Changsha 410006, ChinaMonitoring 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 (<i>n</i> = 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.https://www.mdpi.com/1424-8220/25/8/2377mental workloadHRVeye movementheat exposurepipe workers |
| spellingShingle | Shiyi He Dongsheng Qi Enkai Guo Liyun Wang Yewei Ouyang Lan Zheng Monitoring Construction Workers’ Mental Workload Due to Heat Exposure Using Heart Rate Variability and Eye Movement: A Study on Pipe Workers Sensors mental workload HRV eye movement heat exposure pipe workers |
| title | Monitoring Construction Workers’ Mental Workload Due to Heat Exposure Using Heart Rate Variability and Eye Movement: A Study on Pipe Workers |
| title_full | Monitoring Construction Workers’ Mental Workload Due to Heat Exposure Using Heart Rate Variability and Eye Movement: A Study on Pipe Workers |
| title_fullStr | Monitoring Construction Workers’ Mental Workload Due to Heat Exposure Using Heart Rate Variability and Eye Movement: A Study on Pipe Workers |
| title_full_unstemmed | Monitoring Construction Workers’ Mental Workload Due to Heat Exposure Using Heart Rate Variability and Eye Movement: A Study on Pipe Workers |
| title_short | Monitoring Construction Workers’ Mental Workload Due to Heat Exposure Using Heart Rate Variability and Eye Movement: A Study on Pipe Workers |
| title_sort | monitoring construction workers mental workload due to heat exposure using heart rate variability and eye movement a study on pipe workers |
| topic | mental workload HRV eye movement heat exposure pipe workers |
| url | https://www.mdpi.com/1424-8220/25/8/2377 |
| work_keys_str_mv | AT shiyihe monitoringconstructionworkersmentalworkloadduetoheatexposureusingheartratevariabilityandeyemovementastudyonpipeworkers AT dongshengqi monitoringconstructionworkersmentalworkloadduetoheatexposureusingheartratevariabilityandeyemovementastudyonpipeworkers AT enkaiguo monitoringconstructionworkersmentalworkloadduetoheatexposureusingheartratevariabilityandeyemovementastudyonpipeworkers AT liyunwang monitoringconstructionworkersmentalworkloadduetoheatexposureusingheartratevariabilityandeyemovementastudyonpipeworkers AT yeweiouyang monitoringconstructionworkersmentalworkloadduetoheatexposureusingheartratevariabilityandeyemovementastudyonpipeworkers AT lanzheng monitoringconstructionworkersmentalworkloadduetoheatexposureusingheartratevariabilityandeyemovementastudyonpipeworkers |