Personalizing a smartwatch-based gesture interface with transfer learning

The widespread adoption of mobile devices has lead to an increased interest toward smartphone-based solutions for supporting visually impaired users. Unfortunately the touch-based interaction paradigm commonly adopted on most devices is not convenient for these users, motivating the study of different interaction technologies. In this paper, following up on our previous work, we consider a system where a smartwatch is exploited to provide hands-free interaction through arm gestures with an assistive application running on a smartphone. In particular we focus on the task of effortlessly customizing the gesture recognition system with new gestures specified by the user. To address this problem we propose an approach based on a novel transfer metric learning algorithm, which exploits prior knowledge about a predefined set of gestures to improve the recognition of user-defined ones, while requiring only few novel training samples. The effectiveness of the proposed method is demonstrated through an extensive experimental evaluation.