Movement Artifact-Robust Mental Workload Assessment During Physical Activity Using Multi-Sensor Fusion

2020 
Mental workload assessment is of great importance for safety critical applications, especially in situations that involve physical demands, such as with first responders (e.g., paramedics, firefighters, or police officers). Advancements in physiological signal monitoring with wearable sensors have made way for real-time mental workload assessment using physiological signals. However, these models have typically been conducted in controlled laboratory settings and rely on a single physiological modality. As a result, such models often experience a drop in performance due to movement artifacts introduced in real-life conditions. In this paper, we demonstrate that a multi-modal mental workload model not only improves measurement accuracy, but can also increase robustness against physical activity artifacts. To this end, an experiment was conducted where mental workload and physical activity levels were modulated simultaneously while physiological data was collected from 48 participants using off-the-shelf wearable devices. Results show improved mental workload assessment with multi-modal fusion under varying physical activity conditions.
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