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Enhancing Stress Detection for Students: Exploring the Impact of Fine-Tuning and User-Specific Data Calibration in Deep Learning

Rashmi Alur Ramachandra; Jayasankar Santhosh; Andreas Dengel; Shoya Ishimaru
In: International Journal of Activity and Behavior Computing, Vol. 2024, Pages 1-21, J-Stage, 2024.


This study presents a comprehensive investigation into stress detection among students, focusing on multiple levels of stress assessment. This research aims to shed light on the complexities of stress experienced in educational settings by utilizing a physiological sensing wristband to capture the multifaceted nature of stress responses. A user study was conducted to calculate the cognitive stress levels of a group of 25 participants by recording physiological signals on an Empatica E4 wristband. Along with the relaxed or non-stressed condition, the study employed a range of simple to complex arithmetic tasks designed to elicit three levels of response: 1) slightly stressed or easy level, 2) stressed or medium level, and 3) highly stressed or hard level. Upon the implementation of multiple deep learning models, FCN, ResNet, and LSTM models demonstrated promising outcomes in accurately categorizing the three different stress levels (easy, medium and hard). The models were trained using KFold and Leave-One-Participant-Out (LOPO) cross-validation techniques. To improve the prediction accuracy of LOPO, a fine-tuning or user-specific data calibration approach was utilized. This approach resulted in significant improvements in accuracy for LOPO, with the FCN model achieving a spike to 60% (F1=0.578), the ResNet model reaching 85% (F1=0.846), and the LSTM model achieving an impressive 91% (F1=0.911) accuracy for three-class classification. Leveraging the insights gained from the prediction outcomes, a prototype application was developed that effectively portrays the dynamic fluctuations in stress levels. This application incorporates a stress meter, allowing users to visually comprehend their stress levels, and it delivers customized alert messages to individuals based on their respective stress levels, ensuring timely support and intervention.


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