A Multi-source Unsupervised Domain Adaptation Method for Wearable Sensor based Human Activity Recognition

Human Activity Recognition (HAR) refers to recognizing a human's ongoing actions through sensor data. At present, one of the main problems faced by Human Activity Recognition is that different subjects, devices and wearing positions can cause inconsistent sensor data distribution. When a classification model trained using some labeled dataset is used to classify a new unlabeled data with different distributions, there will be a significant performance loss. However, it is difficult to annotate manually sensor data for new subjects. Prior works applying unsupervised domain adaptation methods to solve this problem only used a single source domain. However, in practice, it is common to have multiple labeled source domains. Inspired by a work in the field of computer vision, we propose an unsupervised domain adaptation method for human activity recognition using multiple source domains. Experimental results on a commonly used public HAR dataset show that our model can effectively alleviate the performance loss caused by inconsistent distributions. Moreover, compared with the single-source domain adaptation, the multi-source domain adaptation method can improve the accuracy further.