Ultra-Short Window Length and Feature Importance Analysis for Cognitive Load Detection from Wearable Sensors (Tervonen et al., 2021)

[DominiqueMakowski]

• Tervonen, J., Pettersson, K., & Mäntyjärvi, J. (2021). Ultra-Short Window Length and Feature Importance Analysis for Cognitive Load Detection from Wearable Sensors. Electronics, 10(5), 613.

This study presents a comparative analysis of ultra-short (30 s or less) window lengths in cognitive load detection with a wearable device. Heart rate, heart rate variability, galvanic skin response, and skin temperature features are extracted at six different window lengths and used to train an Extreme Gradient Boosting classifier to detect between cognitive load and rest. A 25 s window showed the highest accury (67.6%), which is similar to earlier studies using the same dataset.