A multi-task learning method for human motion classification and person identification

Wearable robotic systems have been widely studied in recent years, but it still remains a challenge to design a user-adaptive controller for wearable robotic systems to ensure personalized and accurate human-robot interaction. Accurate human motion classification and person identification are two premises helping design user-adaptive controllers for wearable robotic systems. In this paper, we proposed a multi-task learning method for human motion classification and person identification with a single neural network, which can serve as a solution to personalized human-robot interaction, and can also serve as a benchmark for the following studies in related fields. The multi-task learning neural network was trained and tested on a public human motion data set. The proposed method was capable to classify human motions and identify the person, with 99.13% and 96.51% accuracy, respectively. We also compared the proposed method with a benchmark single task learning method for human motion classification, the results showed that the performance of the multi-task learning method is more superior.