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

2026-04-16

AI Summary

Existing approaches struggle to accurately predict individual dynamic physiological responses—such as heart rate and heart rate variability—following stress interventions using wearable sensor data, thereby limiting the generation of personalized stress-reduction recommendations. This work proposes a personalized Transformer framework integrated with multi-task learning that leverages pre-intervention physiological baselines to jointly forecast the percentage change trajectories and directional trends of multiple physiological metrics over future horizons ranging from 15 to 120 minutes. To our knowledge, this is the first application of a context-aware Transformer architecture to multi-horizon physiological response prediction, trained in conjunction with user-annotated intervention events. Empirical results demonstrate that the model effectively anticipates post-intervention physiological trends at the individual level, establishing the feasibility of personalized prediction of stress intervention efficacy and offering a novel paradigm for intelligent stress management.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Time-Series/Data Streams

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• personalized prediction
• physiological response
• stress intervention
• wearable sensor data
• context-aware modeling

Innovation

• Personalized Prediction
• Context-Aware Transformer
• Post-Intervention Physiological Response
• Wearable Sensor Data
• Multi-Horizon Trajectory