# StressNAS: Affect State and Stress Detection Using Neural Architecture Search

arXiv: https://arxiv.org/abs/2108.12502 · HTML: https://mlsanity.com/paper/2108.12502/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 14 references that are in the map.
- First posted on arXiv 26 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search](https://mlsanity.com/paper/2001.00326/) (2020)
- [Neural Architecture Search without Training](https://mlsanity.com/paper/2006.04647/) (2020)

## Built on by (most cited first, 7 of 7)

- [Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition](https://mlsanity.com/paper/2303.17611/) (2023)
- [A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study](https://mlsanity.com/paper/2308.14245/) (2023)
- [SELF-CARE: Selective Fusion with Context-Aware Low-Power Edge Computing for Stress Detection](https://mlsanity.com/paper/2205.03974/) (2022)
- [Fusion of Physiological and Behavioural Signals on SPD Manifolds with Application to Stress and Pain Detection](https://mlsanity.com/paper/2207.08811/) (2022)
- [An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices](https://mlsanity.com/paper/2203.09663/) (2022)
- [Stress Classification from ECG Signals Using Vision Transformer](https://mlsanity.com/paper/2603.26721/) (2026)
- [MC-QDSNN: Quantized Deep evolutionary SNN with Multi-Dendritic Compartment Neurons for Stress Detection using Physiological Signals](https://mlsanity.com/paper/2410.04992/) (2024)

## Abstract (arXiv)

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).