[论文解读] AdaptStress: Online Adaptive Learning for Interpretable and Personalized Stress Prediction Using Multivariate and Sparse Physiological Signals

Xueyi Wang, Lamoth, Claudine J. C.+1 more|arXiv (Cornell University)|Feb 19, 2026

Digital Mental Health Interventions被引用 0

一句话总结

AdaptStress 通过多变量可穿戴信号、领域自适应和选择性测试时自适应，构建一个可解释、个性化的压力预测模型，面向16名参与者提前预测压力日数。

ABSTRACT

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.

研究动机与目标

• 满足个性化、可解释的消费者可穿戴设备压力预测的需求。
• 开发一个多变量、稀疏、时序框架，处理缺失数据并捕捉跨变量的相互作用。
• 结合领域自适应和测试时自适应以减小参与者间变异性，并实现对不同用户的部署。
• 提供可解释性分析以识别主导预测因子并提升临床信任度。

提出的方法

• 使用基于 Transformer 的编码器学习15维多变量生理时序表示。
• 引入特征层注意力机制，动态权衡信号以进行压力预测。
• 应用对抗性领域自适应与领域判别器以学习跨参与者的领域不变表示。
• 实现选择性 Test-Time Adaptation (TTA)，在新参与者上判断自适应是否有益，结合熵最小化与伪标签。
• 以重叠滑动窗口（历史窗口和预测窗口）处理数据，生成多组训练样本与预测。
• 在16名参与者中进行 Leave-One-Out 交叉验证，并以 MSE、MAE、RMSE、Pearson 相关性、趋势方向准确率，以及用于可解释性的 SHAP 进行评估。

实验结果

研究问题

• RQ1 可变稀疏表示的可穿戴信号是否能预测具有不同基线的个体未来的压力？
• RQ2 领域自适应在减小跨参与者压力预测差距方面的效果如何？
• RQ3 选择性 TTA 是否能在不损害已对齐参与者的前提下改善新参与者的预测？
• RQ4 哪些生理特征在跨个体中最强且最一致地解释压力？
• RQ5 是否出现个性化模式，即相同特征对不同用户有不同作用？

主要发现

• 在最优设置（5天输入、1天预测）下，模型的 MSE 0.053、MAE 0.190、RMSE 0.226。
• AdaptStress 在所有条件下均优于 TimesNet、PatchTST、CNN-LSTM、LSTM、CNN，相对于最佳基线的改进最高达36.9%。
• 睡眠指标成为最具支配性且最具一致性的压力预测因子（重要性1.1，一致性0.9–1.0），而活动特征在跨参与者间变异性较大（0.1–0.2）。
• 模型捕捉到个体特异性模式，在相同特征对不同用户有不同作用，验证了个性化能力。
• Leave-One-Out 评估显示在结合领域自适应和选择性 TTA 时具有跨参与者泛化能力。