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
StressNAS : affect state and stress detection using neural architecture search
|Author:||Huynh, Lam1; Nguyen, Tri2; Nguyen, Thu3;|
1Center for Machine Vision and Signal Analysis, University of Oulu
2Center for Ubiquitous Computing, University of Oulu
3Economics and Business Administration, University of Oulu
4Biomimetics and Intelligent Systems Group, University of Oulu
|Online Access:||PDF Full Text (PDF, 0.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021100750052
Association for Computing Machinery,
|Publish Date:|| 2021-10-07
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).
|Pages:||121 - 125|
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
ACM International Joint Conference on Pervasive and Ubiquitous Computing
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This work is supported by the Academy of Finland 6Genesis Flagship (grant 318927), the Vision-based 3D perception for mixed reality applications project and the TrustedMaaS project by the Infotech institute of the University of Oulu.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© 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.