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.
Abstract (translated)
持续的压力预测有可能为生活方式的干预提供帮助。本文提出了一种新颖、可解释且个性化的压力预测方法，该方法利用来自消费级智能手表的生理数据来预测压力水平。我们开发了一个时间序列预测模型，该模型利用多变量特征（包括心率变异性、活动模式和睡眠指标），在16个不同的时间窗口内进行预测（历史窗口：3天、5天、7天、9天；预测窗口：1天、3天、5天、7天）。我们的评估涵盖了10到15周监控下的16名参与者。 我们从多个时间窗口角度对这种方法进行了跨16位参与者的评估，并将其与最先进的时间序列模型（包括Informer、TimesNet和PatchTST）以及传统的基础线模型（如CNN、LSTM、CNN-LSTM）进行了对比。在最优设置下（5天输入，1天预测），我们的模型取得了MSE为0.053、MAE为0.190和RMSE为0.226的性能指标。与基线模型相比，在所有条件下，我们的模型都优于TimesNet、PatchTST、CNN-LSTM、LSTM和CNN，分别提高了36.9%、25.5%和21.5%，表明了相对于最佳基础模型的进步。 根据可解释性分析，睡眠指标是最重要的且最一致的压力预测因素（重要度：1.1，一致性：0.9-1.0），而活动特征在不同参与者之间表现出较高的变异性（0.1-0.2）。值得注意的是，该模型能够捕捉到个体特有的模式，在这些模式中相同的特征可以对不同的用户产生相反的影响，从而验证了其个性化能力。 这些发现表明消费级可穿戴设备结合适应性和解释性的深度学习技术可以在现实世界环境中提供与个人生理反应相匹配的相关压力评估。这为大规模、连续且具有解释性的心理健康监测奠定了基础。
URL
https://arxiv.org/abs/2602.18521
