Semi-supervised Deep Learning for Stress Prediction: A Review and Novel Solutions

• Published in: International journal of advanced computer science & applications Vol. 12; no. 9
• Main Author: Alshamrani, Mazin
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2021
• Subjects: Algorithms; Clustering; Data augmentation; Deep learning; Machine learning; Physiology; Semi-supervised learning; Stress state; Wrist
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2021.0120949

This research introduces a novel self-supervised deep learning model for stress detection using an intelligent solution that detects the stress state using the physiological parameters. The first part of this research represents a concise review of different intelligent techniques for processing physiological data and the emotional states of humans. Also, for all covered methods, special attention is made to semi-supervised learning algorithms. In the second part of the paper, a novel semi-supervised deep learning model for predicting the stress state is proposed. It is the first attempt of using contrastive learning for the stress prediction tasks. The model is based on utilizing generative and contrastive features specially tailored for treating time-series data. A widely popular multimodal WESAD (Wearable Stress and Affect Detection) data set is exploited for experimental purposes. It consists of physiological and motion data recorded from the wrist and chest-worn devices. To provide an intelligent solution that will be widely applicable, only the wrist data recorded from smartwatches is exploited during the model's training. The proposed model in this research is tested on a single subject's data and predicts the stress and non-stress events. Keeping in mind that the initial data was unbalanced with only 11% of the stress data, data augmentation techniques are applied within the model to provide additional reliable training information. The model shows significant potential in clustering stress conditions, and it presents accuracy in the range with other state-of-the-art solutions. The most significant benefits of using this model are its prediction capabilities when dealing with unlabeled data and performances when undersized data cannot be processed optimally by traditional intelligent methods.