# The Effect of Person-Specific Biometrics in Improving Generic Stress Predictive Models

arXiv: https://arxiv.org/abs/1910.01770 · HTML: https://mlsanity.com/paper/1910.01770/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review](https://mlsanity.com/paper/2209.15137/) (2022)
- [A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study](https://mlsanity.com/paper/2308.14245/) (2023)
- [Ensemble Machine Learning Model Trained on a New Synthesized Dataset Generalizes Well for Stress Prediction Using Wearable Devices](https://mlsanity.com/paper/2209.15146/) (2022)
- [Analysing the Performance of Stress Detection Models on Consumer-Grade Wearable Devices](https://mlsanity.com/paper/2203.09669/) (2022)
- [Stressor Type Matters! -- Exploring Factors Influencing Cross-Dataset Generalizability of Physiological Stress Detection](https://mlsanity.com/paper/2405.09563/) (2024)
- [Approximately optimal domain adaptation with Fisher's Linear Discriminant](https://mlsanity.com/paper/2302.14186/) (2023)
- [An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices](https://mlsanity.com/paper/2203.09663/) (2022)
- [PiRL: Participant-Invariant Representation Learning for Healthcare](https://mlsanity.com/paper/2211.12422/) (2022)
- [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)
- [The Effect of Acute Stress on the Interpretability and Generalization of Schizophrenia Predictive Machine Learning Models](https://mlsanity.com/paper/2410.19739/) (2024)

## Abstract (arXiv)

Because stress is subjective and is expressed differently from one person to another, generic stress prediction models (i.e., models that predict the stress of any person) perform crudely. Only person-specific ones (i.e., models that predict the stress of a preordained person) yield reliable predictions, but they are not adaptable and costly to deploy in real-world environments. For illustration, in an office environment, a stress monitoring system that uses person-specific models would require collecting new data and training a new model for every employee. Moreover, once deployed, the models would deteriorate and need expensive periodic upgrades because stress is dynamic and depends on unforeseeable factors. We propose a simple, yet practical and cost effective calibration technique that derives an accurate and personalized stress prediction model from physiological samples collected from a large population. We validate our approach on two stress datasets. The results show that our technique performs much better than a generic model. For instance, a generic model achieved only a 42.5% accuracy. However, with only 100 calibration samples, we raised its accuracy to 95.2% We also propose a blueprint for a stress monitoring system based on our strategy, and we debate its merits and limitation. Finally, we made public our source code and the relevant datasets to allow other researchers to replicate our findings.