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Fatigue

Fatigue

Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...

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A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

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Activity-Aware Deep Cognitive Fatigue Assessment using Wearables.

Mohammad Arif Ul Alam

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 11, 2021

Summary

Cognitive fatigue monitoring is improved by a new framework, Activity-Aware Recurrent Neural Network (AcRoNN). This method accounts for individual activities, enhancing accuracy in detecting worker fatigue.

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Published on: July 27, 2018

Area of Science:

Human-Computer Interaction
Wearable Technology
Occupational Health

Background:

Cognitive fatigue is a widespread occupational issue with significant global impact.
Current wearable sensor tools for cognitive fatigue often overlook individual activity variations, limiting their effectiveness.
Physiological signals (ECG, PPG, Actigraphy) are commonly used but require context.

Purpose of the Study:

To introduce a novel framework, Activity-Aware Recurrent Neural Network (AcRoNN), for improved cognitive fatigue estimation.
To enhance the generalization of individual activity recognition in fatigue monitoring.
To address the limitations of existing methods by incorporating activity-awareness.

Main Methods:

Development of the Activity-Aware Recurrent Neural Network (AcRoNN) framework.
Integration of multi-modal wearable sensor data with activity recognition.
Evaluation using a real-time collected dataset (5 individuals) and a public dataset (27 individuals).

Main Results:

The AcRoNN framework significantly improves cognitive fatigue estimation by incorporating activity recognition.
Achieved a maximum improvement of 19% over baseline models in fatigue detection accuracy.
Demonstrated the framework's ability to generalize across different individuals and activities.

Conclusions:

Activity-awareness is crucial for accurate and personalized cognitive fatigue monitoring.
The AcRoNN framework offers a promising solution for more effective detection of worker cognitive fatigue.
This approach has the potential to enhance workplace well-being and productivity.