  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).
