Outlier Detection in Wearable Sensor Data for Human Activity Recognition (HAR) Based on DRNNs

Wearable sensors provide a user-friendly and non-intrusive mechanism to extract user-related data that paves the way to the development of personalized applications. Within those applications, human activity recognition (HAR) plays an important role in the characterization of the user context. Outlier detection methods focus on finding anomalous data samples that are likely to have been generated by a different mechanism. This paper combines outlier detection and HAR by introducing a novel algorithm that is able both to detect information from secondary activities inside the main activity and to extract data segments of a particular sub-activity from a different activity. Several machine learning algorithms have been previously used in the area of HAR based on the analysis of the time sequences generated by wearable sensors. Deep recurrent neural networks (DRNNs) have proven to be optimally adapted to the sequential characteristics of wearable sensor data in previous studies. A DRNN-based algorithm is proposed in this paper for outlier detection in HAR. The results are validated both for intra- and inter-subject cases and both for outlier detection and sub-activity recognition using two different datasets. A first dataset comprising 4 major activities (walking, running, climbing up, and down) from 15 users is used to train and validate the proposal. Intra-subject outlier detection is able to detect all major outliers in the walking activity in this dataset, while inter-subject outlier detection only fails for one participant executing the activity in a peculiar way. Sub-activity detection has been validated by finding out and extracting walking segments present in the other three activities in this dataset. A second dataset using four different users, a different setting and different sensor devices is used to assess the generalization of results.