METIER

Abstract

Activity recognition (AR) and user recognition (UR) using wearable sensors are two key tasks in ubiquitous and mobile computing. Currently, they still face some challenging problems. For one thing, due to the variations in how users perform activities, the performance of a well-trained AR model typically drops on new users. For another, existing UR models are powerless to activity changes, as there are significant differences between the sensor data in different activity scenarios. To address these problems, we propose METIER (deep multi-task learning based activity and user recognition) model, which solves AR and UR tasks jointly and transfers knowledge across them. User-related knowledge from UR task helps AR task to model user characteristics, and activity-related knowledge from AR task guides UR task to handle activity changes. METIER softly shares parameters between AR and UR networks, and optimizes these two networks jointly. The commonalities and differences across tasks are exploited to promote AR and UR tasks simultaneously. Furthermore, mutual attention mechanism is introduced to enable AR and UR tasks to exploit their knowledge to highlight important features for each other. Experiments are conducted on three public datasets, and the results show that our model can achieve competitive performance on both tasks.