Detection and Classification of Mental Stress Using In-Ear Plethysmography and a Vision Transformer

This study addresses the critical need for effective mental stress monitoring, linked to severe health issues like depression and heart disease. We introduce a robust method using in-ear photoplethysmogram (PPG) signals for detecting and classifying stress levels. The objective of this study is to develop a precise stress monitoring technique using advanced signal processing and deep learning. Raw PPG data were collected from 15 subjects undergoing stress-inducing activities in a controlled setting. The data underwent preprocessing and were transformed into image-like time-frequency representations. We employed vision transformer (ViT) models for classification, which were fine-tuned and compared against other state-of-the-art deep learning models. The ViT classifier significantly outperformed existing models, achieving an average accuracy of 97.78% and an F1-score of 97.79%. While the dataset is relatively small, these results suggest a promising direction for stress monitoring by illustrating the potential of combining in-ear PPG signals with ViT models. The study indicates the efficacy of this novel approach for accurate mental stress diagnosis, which could have significant implications for mental health applications. Future work will focus on validating these findings with a larger sample size and exploring the integration of this technology into wearable devices for real-world stress monitoring.