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Physiological and behavioral data collected from wearable or mobile sensorshave been used to estimate self-reported stress levels. Since the stressannotation usually relies on self-reports during the study, a limited amount oflabeled data can be an obstacle in developing accurate and generalized stresspredicting models. On the other hand, the sensors can continuously capturesignals without annotations. This work investigates leveraging unlabeledwearable sensor data for stress detection in the wild. We first applied dataaugmentation techniques on the physiological and behavioral data to improve therobustness of supervised stress detection models. Using an auto-encoder withactively selected unlabeled sequences, we pre-trained the supervised modelstructure to leverage the information learned from unlabeled samples. Then, wedeveloped a semi-supervised learning framework to leverage the unlabeled datasequences. We combined data augmentation techniques with consistencyregularization, which enforces the consistency of prediction output based onaugmented and original unlabeled data. We validated these methods using threewearable/mobile sensor datasets collected in the wild. Our results showed thatcombining the proposed methods improved stress classification performance by7.7% to 13.8% on the evaluated datasets, compared to the baseline supervisedlearning models.

Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild