Online personalization of cross-subjects based activity recognition models on wearable devices

Human activity recognition using wearable devices is an active area of research in pervasive computing. In our work, we address the problem of reducing the effort for training and adapting activity recognition approaches to a specific person. We focus on the problem of cross-subjects based recognition models and introduce an approach that considers physical characteristics. Further, to adapt such a model to the behavior of a new user, we present a personalization approach that relies on online and active machine learning. In this context, we use online random forest as a classifier to continuously adapt the model without keeping the already seen data available and an active learning approach that uses user-feedback for adapting the model while minimizing the effort for the new user. We test our approaches on a real world data set that covers 15 participants, 8 common activities, and 7 different on-body device positions. We show that our cross-subjects based approach performs constantly +3% better than the standard approach. Further, the personalized cross-subjects models, gained through user-feedback, recognize dynamic activities with an F-measure of 87% where the user has significantly less effort than collecting and labeling data.