Convolutional Autoencoder based Stress Detection using Soft Voting

• Published in: Korean Institute of Smart Media Vol. 12; no. 11; pp. 9 - 17
• Main Authors: Choi, Eun Bin, Kim, Soo Hyung
• Format: Journal Article
• Language: English
• Published: 31.12.2023
• ISSN: 2287-1322; 2288-9671
• DOI: 10.30693/SMJ.2023.12.11.9

Stress is a significant issue in modern society, often triggered by external or internal factors that are difficult to manage. When high stress persists over a long term, it can develop into a chronic condition, negatively impacting health and overall well-being. However, it is challenging for individuals experiencing chronic stress to recognize their condition, making early detection and management crucial. Using biosignals measured from wearable devices to detect stress could lead to more effective management. However, there are two main problems with using biosignals: first, manually extracting features from these signals can introduce bias, and second, the performance of classification models can vary greatly depending on the subject of the experiment. This paper proposes a model that reduces bias using convo utional autoencoders, which can represent the key features of data, and enhances generalizability by employing soft voting, a method of ensemble learning, to minimize performance variability. To verify the generalization performance of the model, we evaluate it using LOSO cross-validation method. The model proposed in this paper has demonstrated superior accuracy compared to previous studies using the WESAD dataset.