# Feature Augmented Hybrid CNN for Stress Recognition Using Wrist-based Photoplethysmography Sensor

arXiv: https://arxiv.org/abs/2108.03166 · HTML: https://mlsanity.com/paper/2108.03166/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [End-to-End Multimodal Emotion Recognition using Deep Neural Networks](https://mlsanity.com/paper/1704.08619/) (2017)

## Built on by (most cited first, 6 of 6)

- [Enhancing Generalization in PPG-Based Emotion Measurement with a CNN-TCN-LSTM Model](https://mlsanity.com/paper/2507.14173/) (2025)
- [SELF-CARE: Selective Fusion with Context-Aware Low-Power Edge Computing for Stress Detection](https://mlsanity.com/paper/2205.03974/) (2022)
- [Real-Time Stress Detection via Photoplethysmogram Signals: Implementation of a Combined Continuous Wavelet Transform and Convolutional Neural Network on Resource-Constrained Microcontrollers](https://mlsanity.com/paper/2410.19776/) (2024)
- [Multimodal signal fusion for stress detection using deep neural networks: a novel approach for converting 1D signals to unified 2D images](https://mlsanity.com/paper/2509.13636/) (2025)
- [PPG-Based Affect Recognition with Long-Range Deep Models: A Measurement-Driven Comparison of CNN, Transformer, and Mamba Architectures](https://mlsanity.com/paper/2604.26078/) (2026)
- [Introducing WARM-VR: Benchmark Dataset for Multimodal Wearable Affect Recognition in Virtual Reality](https://mlsanity.com/paper/2605.00184/) (2026)

## Abstract (arXiv)

Stress is a physiological state that hampers mental health and has serious consequences to physical health. Moreover, the COVID-19 pandemic has increased stress levels among people across the globe. Therefore, continuous monitoring and detection of stress are necessary. The recent advances in wearable devices have allowed the monitoring of several physiological signals related to stress. Among them, wrist-worn wearable devices like smartwatches are most popular due to their convenient usage. And the photoplethysmography (PPG) sensor is the most prevalent sensor in almost all consumer-grade wrist-worn smartwatches. Therefore, this paper focuses on using a wrist-based PPG sensor that collects Blood Volume Pulse (BVP) signals to detect stress which may be applicable for consumer-grade wristwatches. Moreover, state-of-the-art works have used either classical machine learning algorithms to detect stress using hand-crafted features or have used deep learning algorithms like Convolutional Neural Network (CNN) which automatically extracts features. This paper proposes a novel hybrid CNN (H-CNN) classifier that uses both the hand-crafted features and the automatically extracted features by CNN to detect stress using the BVP signal. Evaluation on the benchmark WESAD dataset shows that, for 3-class classification (Baseline vs. Stress vs. Amusement), our proposed H-CNN outperforms traditional classifiers and normal CNN by 5% and 7% accuracy, and 10% and 7% macro F1 score, respectively. Also for 2-class classification (Stress vs. Non-stress), our proposed H-CNN outperforms traditional classifiers and normal CNN by 3% and ~5% accuracy, and ~3% and ~7% macro F1 score, respectively.