Personalized and Context-Aware Transformer Models for Predicting Post-Intervention Physiological Responses from Wearable Sensor Data

TL;DR

This paper introduces a Transformer-based framework to predict how physiological signals like heart rate respond over time after stress-reduction interventions, enabling personalized stress management recommendations.

Contribution

It presents a novel personalized prediction model for post-intervention physiological trajectories using Transformer architectures and empirical wearable sensor data.

Findings

Personalized predictions of physiological responses are feasible.

The model predicts both the magnitude and direction of change over multiple time horizons.

Proof of concept demonstrated with real-world wearable data.

Abstract

Consumer wearables enable continuous measurement of physiological data related to stress and recovery, but turning these streams into actionable, personalized stress-management recommendations remains a challenge. In practice, users often do not know how a given intervention, defined as an activity intended to reduce stress, will affect heart rate (HR), heart rate variability (HRV), or inter-beat intervals (BBI) over the next 15 to 120 minutes. We present a framework that predicts post-intervention trajectories and the direction of change for these physiological indicators across time windows. Our methodology combines a Transformer model for multi-horizon trajectories of percent change relative to a pre-intervention baseline, direction-of-change calls (positive, negative, or neutral) at each horizon, and an empirical study using wearable sensor data overlaid with user-tagged events and interventions. This proof of concept shows that personalized post-intervention prediction is feasible. We encourage future integration into stress-management tools for personalized intervention recommendations tailored to each person's day following further validation in larger studies and, where applicable, appropriate regulatory review.