# A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study

arXiv: https://arxiv.org/abs/2308.14245 · HTML: https://mlsanity.com/paper/2308.14245/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 60 references that are in the map.
- First posted on arXiv 28 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations](https://mlsanity.com/paper/2005.04120/) (2020)
- [A Transformer Architecture for Stress Detection from ECG](https://mlsanity.com/paper/2108.09737/) (2021)
- [A Survey on Physiological Signal Based Emotion Recognition](https://mlsanity.com/paper/2205.10466/) (2022)
- [A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism](https://mlsanity.com/paper/2303.03577/) (2023)
- [The Effect of Person-Specific Biometrics in Improving Generic Stress Predictive Models](https://mlsanity.com/paper/1910.01770/) (2019)
- [StressNAS: Affect State and Stress Detection Using Neural Architecture Search](https://mlsanity.com/paper/2108.12502/) (2021)
- [Stress Classification and Personalization: Getting the most out of the least](https://mlsanity.com/paper/2107.05666/) (2021)
- [Semi-Supervised Generative Adversarial Network for Stress Detection Using Partially Labeled Physiological Data](https://mlsanity.com/paper/2206.14976/) (2022)
- [Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data](https://mlsanity.com/paper/2307.03337/) (2023)

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

- [Feasibility of Detecting Cognitive Impairment and Psychological Well-being among Older Adults Using Facial, Acoustic, Linguistic, and Cardiovascular Patterns Derived from Remote Conversations](https://mlsanity.com/paper/2412.14194/) (2024)
- [Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility](https://mlsanity.com/paper/2409.12116/) (2024)
- [Driver-Intention Prediction with Deep Learning: Real-Time Brain-to-Vehicle Communication](https://mlsanity.com/paper/2601.05084/) (2026)
- [A Comparative Study of Deep Learning Architectures for Multi-Horizon Behavioural Forecasting for Mobile Health](https://mlsanity.com/paper/2606.14604/) (2026)
- [State-specific respiratory signatures for affective and stress recognition: Interpretable respiratory markers, autocorrelation lags, and compact CNN models](https://mlsanity.com/paper/2606.26723/) (2026)
- [Interpretable Stress Detection from ECG Signals Using Motif-Based Anomaly Analysis](https://mlsanity.com/paper/2609.22179/) (2026)
- [Artifact Annotations Partially Substitute for Per-User Calibration: SAFE-EDA and a Normalization-Controlled Evaluation of Wrist-EDA Affect Recognition](https://mlsanity.com/paper/2610.01692/) (2026)

## Abstract (arXiv)

Background: Studies have shown the potential adverse health effects, ranging from headaches to cardiovascular disease, associated with long-term negative emotions and chronic stress. Since many indicators of stress are imperceptible to observers, the early detection and intervention of stress remains a pressing medical need. Physiological signals offer a non-invasive method of monitoring emotions and are easily collected by smartwatches. Existing research primarily focuses on developing generalized machine learning-based models for emotion classification. Objective: We aim to study the differences between personalized and generalized machine learning models for three-class emotion classification (neutral, stress, and amusement) using wearable biosignal data. Methods: We developed a convolutional encoder for the three-class emotion classification problem using data from WESAD, a multimodal dataset with physiological signals for 15 subjects. We compared the results between a subject-exclusive generalized, subject-inclusive generalized, and personalized model. Results: For the three-class classification problem, our personalized model achieved an average accuracy of 95.06% and F1-score of 91.71, our subject-inclusive generalized model achieved an average accuracy of 66.95% and F1-score of 42.50, and our subject-exclusive generalized model achieved an average accuracy of 67.65% and F1-score of 43.05. Conclusions: Our results emphasize the need for increased research in personalized emotion recognition models given that they outperform generalized models in certain contexts. We also demonstrate that personalized machine learning models for emotion classification are viable and can achieve high performance.