Joint Motion Classification and Person Identification via Multitask Learning for Smart Homes

Abstract

In a smart home environment, assisted living has been a topic of great research over the past decade. Human motion analysis is considered as a key technology for living states recognition in an assisted living system. Recent research has proved that rich information can be obtained from human movements, such as the motion category, moving patterns, and human identity. In this paper, a nonintrusive human movement sensing system is established with a mono-static ultrawide bandwidth radar. Then, we propose a well-designed joint motion classification (MCL) and person identification (PID) convolutional neural network (named as “JMI-CNN”). To recognize human motions and identities simultaneously, the network employs a multitask learning scheme as well as the attention mechanism and the hierarchical feature reuse strategies. We report the experimental result on the data from 15 individuals, each performing six motions. It shows that the model achieves a promising performance of 80.57% on the joint task, while the accuracy for MCL and PID are 98.50% and 80.92%, respectively. Moreover, we carry out ablation studies to evaluate the design principles of the proposed method. Discussions on the impact of signal noise ratio and slow-time window length are also conducted.