ArticleSensors (Basel, Switzerland)2022
Automated and Continuous Fatigue Monitoring in Construction Workers Using Forearm EMG and IMU Wearable Sensors and Recurrent Neural Network.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 55 citations in OpenAlex.
- Including Eye Movement in the Assessment of Physical Fatigue Under Different Loading Types and Road Slopes.Journal of eye movement research · 2026Article
- Biomechanical Monitoring of Exercise Fatigue Using Wearable Devices: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Review
- Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion.Sensors (Basel, Switzerland) · 2025Article
- Article
- Article
- Evaluation of fatigue progression during overhead tasks and the effects of exoskeleton assistance.Wearable technologies · 2025Article
- Using a wearable system combining inertial and force sensing for simultaneous detection of limb motion and grasping actions in the workplace.Wearable technologies · 2025Article
- A Review on Assisted Living Using Wearable Devices.Sensors (Basel, Switzerland) · 2024Review
- Robust fatigue markers obtained from muscle synergy analysis.Experimental brain research · 2024Article
- Influence of brick laying height on biomechanical load in masons: Cross-sectional field study with technical measurements.Work (Reading, Mass.) · 2024Article
- A Bibliometric Analysis of Neuroscience Tools Use in Construction Health and Safety Management.Sensors (Basel, Switzerland) · 2023Review
- Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- A Wireless Multi-Layered EMG/MMG/NIRS Sensor for Muscular Activity Evaluation.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
About 40% of the US construction workforce experiences high-level fatigue, which leads to poor judgment, increased risk of injuries, a decrease in productivity, and a lower quality of work. Therefore, it is essential to monitor fatigue to reduce its adverse effects and prevent long-term health problems. However, since fatigue demonstrates itself in several complex processes, there is no single standard measurement method for fatigue detection. This study aims to develop a system for continuous workers' fatigue monitoring by predicting the aerobic fatigue threshold (AFT) using forearm muscle activity and motion data. The proposed system consists of five modules: Data acquisition, activity recognition, oxygen uptake prediction, maximum aerobic capacity (MAC) estimation, and continuous AFT monitoring. The proposed system was evaluated on the participants performing fourteen scaffold-building activities. The results show that the AFT features have achieved a higher accuracy of 92.31% in assessing the workers' fatigue level compared to heart rate (51.28%) and percentage heart rate reserve (50.43%) features. Moreover, the overall performance of the proposed system on unseen data using average two-min AFT features was 76.74%. The study validates the feasibility of using forearm muscle activity and motion data to workers' fatigue levels continuously.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.