Gait identification using a new time-warped similarity metric based on smartphone inertial signals

The evolution of smart devices and ubiquitous computing have paved the way for more intelligent user verification options. Gait pattern recognition has gained prominence due to their noninvasive and seamless verification capabilities without requiring dedicated attention from the user. This paper proposes a new gait identification approach by proposing a new time-warped similarity metric for memory-based gait pattern analysis. The proposed metric is incorporated into a computationally-efficient template matching framework for gait identification. The proposed approach outperforms the conventional approaches by achieving a user recognition error rate of 7.7% and equal error rate of 11.2% based on samples from 50 subjects in two benchmark datasets.