用智能手表数据实时预测压力，还能解释每个人的不同反应。
AdaptStress: Online Adaptive Learning for Interpretable and Personalized Stress Prediction Using Multivariate and Sparse Physiological Signals
- 基于心率变异、活动和睡眠等多变量时序建模，自适应学习个体差异。
- 在5天历史+1天预测下，误差最低（MSE 0.053，MAE 0.190）。
- 能揭示睡眠是核心压力信号，且识别出用户间特征影响的相反效应。
持续压力预测有望支持生活方式干预。本文提出一种新型可解释、个性化的压力预测方法，利用消费级智能手表的生理数据。构建时序预测模型，融合心率变异性、活动模式与睡眠指标，在16个时间窗口（历史窗：3、5、7、9天；预测窗：1、3、5、7天）上进行压力水平预测。对16名参与者进行10-15周监测，对比最新时序模型（Informer、TimesNet、PatchTST）及传统基线（CNN、LSTM、CNN-LSTM）。最优设置下（5天输入，1天预测），模型取得MSE 0.053、MAE 0.190、RMSE 0.226。相比最佳基线，性能提升分别达36.9%、25.5%和21.5%。可解释性分析显示，睡眠指标为最主导且一致的压力预测因子（重要性1.1，一致性0.9–1.0），而活动特征具高度个体差异（0.1–0.2）。尤为关键的是，模型捕捉到相同特征在不同用户中可能产生相反影响，验证其个性化能力。结果表明，结合自适应可解释深度学习，消费级可穿戴设备可实现面向个体响应的可持续、可解释心理状态监测。
原文摘要 · Abstract (English)
Continuous stress forecasting could potentially contribute to lifestyle interventions. This paper presents a novel, explainable, and individualized approach for stress prediction using physiological data from consumer-grade smartwatches. We develop a time series forecasting model that leverages multivariate features, including heart rate variability, activity patterns, and sleep metrics, to predict stress levels across 16 temporal horizons (History window: 3, 5, 7, 9 days; forecasting window: 1, 3, 5, 7 days). Our evaluation involves 16 participants monitored for 10-15 weeks. We evaluate our approach across 16 participants, comparing against state-of-the-art time series models (Informer, TimesNet, PatchTST) and traditional baselines (CNN, LSTM, CNN-LSTM) across multiple temporal horizons. Our model achieved performance with an MSE of 0.053, MAE of 0.190, and RMSE of 0.226 in optimal settings (5-day input, 1-day prediction). A comparison with the baseline models shows that our model outperforms TimesNet, PatchTST, CNN-LSTM, LSTM, and CNN under all conditions, representing improvements of 36.9%, 25.5%, and 21.5% over the best baseline. According to the explanability analysis, sleep metrics are the most dominant and consistent stress predictors (importance: 1.1, consistency: 0.9-1.0), while activity features exhibit high inter-participant variability (0.1-0.2). Most notably, the model captures individual-specific patterns where identical features can have opposing effects across users, validating its personalization capabilities. These findings establish that consumer wearables, combined with adaptive and interpretable deep learning, can deliver relevant stress assessment adapted to individual physiological responses, providing a foundation for scalable, continuous, explainable mental health monitoring in real-world settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。