Presentation Type
Article
Location
Kennesaw, Georgia
Start Date
1-4-2026 9:00 AM
End Date
1-4-2026 10:15 AM
Description
Cognitive stress arises from sustained mental demand, information overload, or task complexity, and requires continuous monitoring for timely detection and intervention in real-world settings. It produces subtle physiological changes, including shifts in heart rate and autonomic activity that require continuous monitoring to detect reliably. Wearable sensors such as photoplethysmography (PPG) and electrodermal activity (EDA) enable non-invasive tracking of these responses. However, multi-modal sensor integration remains challenging, as hardware or software-level synchronization does not consider the physiological response delays. To address this, the study collected PPG and EDA sensor data from ten participants under two cognitive stress-inducing conditions. After preprocessing, heart rate and skin conductance response data were extracted from PPG and EDA signals. A cross-correlation framework was then used to estimate the temporal lag between responses of two modalities, capturing participant-specific and condition-dependent delays. The identified lag was applied to phase-shift EDA signals to align them with heart-rate-based responses. Results showed that heart rate responded quickly to stress onset, whereas EDA exhibited consistent but variable delays across individuals and conditions, an average of 2.1 s in Stroop and 3.7 s in PASAT phase. Incorporating these physiological lags improved synchronization quality and enhanced classification performance compared to unsynchronized data. After synchronization, models accuracy increased for RF (0.5067 to 0.6353), kNN (0.6000 to 0.7185), and CatBoost (0.6022 to 0.6241). These findings demonstrate that accounting for individualized physiological delays strengthens multi-modal fusion and increases the reliability of wearable cognitive stress monitoring systems.
Cross Correlation-Based Physiological Synchronization for Multi-Modal Wearable Cognitive Stress Monitoring System
Kennesaw, Georgia
Cognitive stress arises from sustained mental demand, information overload, or task complexity, and requires continuous monitoring for timely detection and intervention in real-world settings. It produces subtle physiological changes, including shifts in heart rate and autonomic activity that require continuous monitoring to detect reliably. Wearable sensors such as photoplethysmography (PPG) and electrodermal activity (EDA) enable non-invasive tracking of these responses. However, multi-modal sensor integration remains challenging, as hardware or software-level synchronization does not consider the physiological response delays. To address this, the study collected PPG and EDA sensor data from ten participants under two cognitive stress-inducing conditions. After preprocessing, heart rate and skin conductance response data were extracted from PPG and EDA signals. A cross-correlation framework was then used to estimate the temporal lag between responses of two modalities, capturing participant-specific and condition-dependent delays. The identified lag was applied to phase-shift EDA signals to align them with heart-rate-based responses. Results showed that heart rate responded quickly to stress onset, whereas EDA exhibited consistent but variable delays across individuals and conditions, an average of 2.1 s in Stroop and 3.7 s in PASAT phase. Incorporating these physiological lags improved synchronization quality and enhanced classification performance compared to unsynchronized data. After synchronization, models accuracy increased for RF (0.5067 to 0.6353), kNN (0.6000 to 0.7185), and CatBoost (0.6022 to 0.6241). These findings demonstrate that accounting for individualized physiological delays strengthens multi-modal fusion and increases the reliability of wearable cognitive stress monitoring systems.
