用可穿戴设备数据预测用户干预后的生理变化，实现个性化压力管理。
Personalized and Context-Aware Transformer Models for Predicting Post-Intervention Physiological Responses from Wearable Sensor Data
- 基于Transformer模型预测干预后15至120分钟内心率等指标的动态变化轨迹
- 准确识别心率、心率变异性等指标在各时间段的上升、下降或无变化方向
- 适用于个性化压力管理工具，适合关注健康监测与智能干预的开发者
消费级可穿戴设备能持续采集与压力和恢复相关的生理数据，但如何将这些数据转化为个性化的、可操作的压力管理建议仍具挑战。实践中，用户往往不清楚某项干预（旨在缓解压力的活动）将在未来15至120分钟内对心率（HR）、心率变异性（HRV）或心跳间隔（BBI）产生何种影响。本文提出一种框架，用于预测干预后的生理指标轨迹及变化方向。方法结合Transformer模型，预测相对于干预前基线的百分比变化多时间步轨迹，以及每个时间点的方向判断（正向、负向或中性）。通过叠加用户标记事件与干预的可穿戴传感器数据进行实证研究，证明个性化干预后预测是可行的。我们呼吁未来在更大规模研究中进一步验证，并在适用情况下通过监管审查，将其集成到个性化干预推荐系统中。
原文摘要 · Abstract (English)
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.
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