A cross-domain framework for emotion and stress detection using WESAD, SCIENTISST-MOVE, and DREAMER datasets

Emotional and stress-related disorders pose a growing threat to global mental health, emphasizing the critical need for accurate, robust, and interpretable emotion recognition systems. Despite advances in affective computing, existing models often lack generalizability across diverse physiological and behavioral datasets, limiting their practical deployment.

This study presents a dual deep learning-based framework for mental health monitoring and activity monitoring. The first approach introduces a framework for stress classification based on a 1D-CNN trained on the WESAD dataset. This model is then fine-tuned using the ScientISST-MOVE dataset to detect daily life activities based on motion signals, and it is used as transfer learning for a downstream task. An explainable AI technique is used to interpret the model's predictions, while class imbalance is addressed using focal loss and class weighting. The second approach employs a temporal conformer architecture combining CNN and transformer components to model temporal dependencies in continuous affective ratings of emotional states based on valence, arousal, and dominance (VAD) using the DREAMER dataset. This method incorporates feature engineering techniques and models temporal dependencies in ECG signals.

The deep learning classifier trained on WESAD biosignal data achieved 98% accuracy across three classes, demonstrating highly reliable stress classification. The transfer learning model, evaluated on the ScientISST-MOVE dataset, achieved an overall accuracy of 82% across four activity states, with good precision and recall for high-support classes. However, the explanations produced by Grad-CAM appear uninformative and do not clearly indicate which parts of the signals influence the prediction. The conformer model achieved an R² score of 0.78 and a rounded accuracy of 87.59% across all three dimensions, highlighting its robustness in multi-dimensional emotion prediction.

The framework demonstrates strong performance, interpretability, and real-time applicability in personalized affective computing.