StressNAS : affect state and stress detection using neural architecture search

Huynh, Lam; Nguyen, Tri; Nguyen, Thu; Pirttikangas, Susanna; Siirtola, Pekka (2021-09-21)
 
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https://doi.org/10.1145/3460418.3479320

Huynh, Lam
Nguyen, Tri
Nguyen, Thu
Pirttikangas, Susanna
Siirtola, Pekka
Association for Computing Machinery
21.09.2021

Lam Huynh, Tri Nguyen, Thu Nguyen, Susanna Pirttikangas, and Pekka Siirtola. 2021. StressNAS: Affect State and Stress Detection Using Neural Architecture Search. In Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers (UbiComp '21). Association for Computing Machinery, New York, NY, USA, 121–125. DOI:https://doi.org/10.1145/3460418.3479320

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© 2021 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in UbiComp '21: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, https://doi.org/10.1145/3460418.3479320.
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doi:https://doi.org/10.1145/3460418.3479320
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Abstract

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).

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