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Published on: August 8, 2019
System for activity-aware fatigue evaluation (SAFE) framework: Predictive fatigue modelling for occupational tasks
Patricia O'Sullivan1, Matteo Menolotto1, Brendan O'Flynn1
1Tyndall National Institute, Co. Cork, Cork, T12 R5CP, Ireland.
Applied Ergonomics
|July 17, 2026
Summary
The SAFE framework uses wearable sensors and biomechanical models to assess operator endurance and ergonomic risk. This system accurately predicts fatigue and identifies high-risk tasks, paving the way for real-time monitoring.
Area of Science:
- Occupational Health and Safety
- Biomechanical Engineering
- Wearable Technology
Background:
- Operator fatigue and ergonomic risks are significant concerns in industrial settings, impacting productivity and worker well-being.
- Existing methods for monitoring operator endurance and ergonomic risk often lack real-time capabilities or require complex setups.
- The need for a feasible, integrated system for continuous assessment of operator status is critical.
Purpose of the Study:
- To evaluate the feasibility of the System for Activity-aware Fatigue Evaluation (SAFE) framework for monitoring operator endurance and ergonomic risk.
- To assess the accuracy and effectiveness of the SAFE framework in a controlled laboratory setting using industry-type tasks.
- To establish a foundation for the real-time deployment of the SAFE framework.
Main Methods:
- The SAFE framework integrated inertial measurement units (IMUs), pressure insoles, a torque-based endurance model, and task classification algorithms.
- A laboratory-based feasibility study was conducted with 10 participants performing industry-type tasks.
- Validation was performed against motion capture systems to assess the accuracy of IMU-derived joint angles.
Main Results:
- IMU-derived joint angles were found to be within acceptable error ranges for endurance estimation when validated against motion capture.
- The SAFE framework successfully distinguished between ergonomically high- and low-risk tasks (P < .05).
- Endurance metrics generated by the framework showed a strong correlation with subjective fatigue ratings (rmm ≥ .878, P < .001).
Conclusions:
- The SAFE framework is a feasible predictive method for online operator monitoring and ergonomic risk assessment.
- The integration of wearable sensing, biomechanical modeling, and task detection provides valuable insights into operator fatigue.
- These findings support the development of real-time applications for enhancing workplace safety and efficiency.