# Stress Classification and Personalization: Getting the most out of the least

arXiv: https://arxiv.org/abs/2107.05666 · HTML: https://mlsanity.com/paper/2107.05666/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Automatic Stress Detection in Working Environments from Smartphones' Accelerometer Data: A First Step](https://mlsanity.com/paper/1510.04221/) (2015)
- [GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data](https://mlsanity.com/paper/2005.01834/) (2020)

## Built on by (most cited first, 4 of 4)

- [A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study](https://mlsanity.com/paper/2308.14245/) (2023)
- [CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments](https://mlsanity.com/paper/2410.03211/) (2024)
- [Human Heterogeneity Invariant Stress Sensing](https://mlsanity.com/paper/2506.02256/) (2025)
- [Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review](https://mlsanity.com/paper/2308.04616/) (2023)

## Abstract (arXiv)

Stress detection and monitoring is an active area of research with important implications for the personal, professional, and social health of an individual. Current approaches for affective state classification use traditional machine learning algorithms with features computed from multiple sensor modalities. These methods are data-intensive and rely on hand-crafted features which impede the practical applicability of these sensor systems in daily lives. To overcome these shortcomings, we propose a novel Convolutional Neural Network (CNN) based stress detection and classification framework without any feature computation using data from only one sensor modality. Our method is competitive and outperforms current state-of-the-art techniques and achieves a classification accuracy of 92.85% and an f1 score of 0.89. Through our leave-one-subject-out analysis, we also show the importance of personalizing stress models.