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Stress is a physiological state that hampers mental health and has seriousconsequences to physical health. Moreover, the COVID-19 pandemic has increasedstress levels among people across the globe. Therefore, continuous monitoringand detection of stress are necessary. The recent advances in wearable deviceshave allowed the monitoring of several physiological signals related to stress.Among them, wrist-worn wearable devices like smartwatches are most popular dueto their convenient usage. And the photoplethysmography (PPG) sensor is themost prevalent sensor in almost all consumer-grade wrist-worn smartwatches.Therefore, this paper focuses on using a wrist-based PPG sensor that collectsBlood Volume Pulse (BVP) signals to detect stress which may be applicable forconsumer-grade wristwatches. Moreover, state-of-the-art works have used eitherclassical machine learning algorithms to detect stress using hand-craftedfeatures or have used deep learning algorithms like Convolutional NeuralNetwork (CNN) which automatically extracts features. This paper proposes anovel hybrid CNN (H-CNN) classifier that uses both the hand-crafted featuresand the automatically extracted features by CNN to detect stress using the BVPsignal. Evaluation on the benchmark WESAD dataset shows that, for 3-classclassification (Baseline vs. Stress vs. Amusement), our proposed H-CNNoutperforms traditional classifiers and normal CNN by 5% and 7% accuracy, and10% and 7% macro F1 score, respectively. Also for 2-class classification(Stress vs. Non-stress), our proposed H-CNN outperforms traditional classifiersand normal CNN by 3% and ~5% accuracy, and ~3% and ~7% macro F1 score,respectively.

Feature Augmented Hybrid CNN for Stress Recognition Using Wrist-based Photoplethysmography Sensor