Physiological and behavioral data collected from wearable or mobile sensors have been used to detect human health conditions. Sometimes the health-related annotation relies on self-reported surveys during the study, thus a limited amount of labeled data can be an obstacle in developing accurate and generalized predicting models. On the other hand, the sensors can continuously capture signals without labels. This work investigates leveraging unlabeled wearable sensor data for health condition detection. We first applied data augmentation techniques to increase the amount of training data by adding noise to the original physiological and behavioral sensor data and improving the robustness of supervised stress detection models. Second, to leverage the information learned from unlabeled samples, we pre-trained the supervised model structure using an auto-encoder and actively selected unlabeled sequences to filter noisy data. Then, we combined data augmentation techniques with consistency regularization, which enforces the consistency of prediction output based on augmented and original unlabeled data. We validated these methods in sensor-based in wild stress detection tasks using 3 wearable/mobile sensor datasets collected in the wild. Our results showed that the proposed methods improved stress classification performance by 5.3% to 13.8%, compared to the baseline supervised learning models. In addition, our method showed competitive performances compared to state-of-the-art semi-supervised learning methods in the literature.