Semi-Supervised Learning for Wearable-based Momentary Stress Detection in the Wild
Semi-Supervised Learning for Wearable-based Momentary Stress Detection in the Wild
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DOI：
10.1145/3596246
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发表时间：
2023-06
影响因子：
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通讯作者：
Han Yu;Akane Sano
中科院分区：
文献类型：
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作者：
Han Yu;Akane Sano
从可穿戴或移动传感器收集的生理和行为数据已被用于估计自我报告的压力水平。由于应力注释通常依赖于研究过程中的自我报告，有限的标记数据可能成为开发准确和广义应力预测模型的障碍。另一方面，传感器可以在没有注释的情况下连续捕获信号。这项工作研究利用未标记的可穿戴传感器数据进行野外应力检测。我们提出了一个两阶段的半监督学习框架，利用可穿戴传感器数据来帮助进行应力检测。该结构包括一种用于从未标记数据中学习信息的自编码器预训练方法和一种增强模型鲁棒性的一致性正则化方法。此外，我们提出了一种新的主动采样方法来选择未标记的样本，以避免给模型引入冗余信息。我们使用在野外收集的生理信号和应激标签的两个数据集以及四个人类活动识别（HAR）数据集来验证这些方法，以评估所提出方法的通用性。与基线监督学习模型相比，我们的方法在压力检测数据集上的压力分类性能提高了约7%至10%。此外，我们为HAR任务进行的消融研究支持了我们方法的有效性。我们的方法在应力检测和HAR任务中表现出与最先进的半监督学习方法相当的性能。
Physiological and behavioral data collected from wearable or mobile sensors have been used to estimate self-reported stress levels. Since stress annotation usually relies on self-reports during the study, a limited amount of labeled data can be an obstacle to developing accurate and generalized stress-predicting models. On the other hand, the sensors can continuously capture signals without annotations. This work investigates leveraging unlabeled wearable sensor data for stress detection in the wild. We propose a two-stage semi-supervised learning framework that leverages wearable sensor data to help with stress detection. The proposed structure consists of an auto-encoder pre-training method for learning information from unlabeled data and the consistency regularization approach to enhance the robustness of the model. Besides, we propose a novel active sampling method for selecting unlabeled samples to avoid introducing redundant information to the model. We validate these methods using two datasets with physiological signals and stress labels collected in the wild, as well as four human activity recognition (HAR) datasets to evaluate the generality of the proposed method. Our approach demonstrated competitive results for stress detection, improving stress classification performance by approximately 7% to 10% on the stress detection datasets compared to the baseline supervised learning models. Furthermore, the ablation study we conducted for the HAR tasks supported the effectiveness of our methods. Our approach showed comparable performance to state-of-the-art semi-supervised learning methods for both stress detection and HAR tasks.