StressNAS: Affect State and Stress Detection Using Neural Architecture Search
This work addresses stress detection for health monitoring using wearable devices, but it is incremental as it applies NAS to an existing dataset.
The paper tackled stress detection using physiological signals from smartwatches by proposing a neural architecture search (NAS) method, resulting in performance improvements of 8.22% and 6.02% over traditional ML methods for three-state and two-state classifiers, respectively.
Smartwatches have rapidly evolved towards capabilities to accurately capture physiological signals. As an appealing application, stress detection attracts many studies due to its potential benefits to human health. It is propitious to investigate the applicability of deep neural networks (DNN) to enhance human decision-making through physiological signals. However, manually engineering DNN proves a tedious task especially in stress detection due to the complex nature of this phenomenon. To this end, we propose an optimized deep neural network training scheme using neural architecture search merely using wrist-worn data from WESAD. Experiments show that our approach outperforms traditional ML methods by 8.22% and 6.02% in the three-state and two-state classifiers, respectively, using the combination of WESAD wrist signals. Moreover, the proposed method can minimize the need for human-design DNN while improving performance by 4.39% (three-state) and 8.99% (binary).