Gait event detection through neuromorphic spike sequence learning

We present a novel sampling and processing method for detecting gait events from an insole pressure sensor. Inspired by how tactile data is processed in the brain, we propose the use of timing, instead of intensity, as our event detection feature. By sacrificing the need for accurate intensity measurements, it is possible to achieve superior temporal resolution, which is arguably more important given the need for timely feedback. In this paper, we demonstrate temporally accurate gait-event detection of 1.2±7ms (mean and standard deviation) for heel-strike and 0.2± 14ms for toe-off events compared to the reference system, and a success rate of above 97% in most trials, using only 1 bit of pressure information per channel. Our method thus has the potential to achieve much lower computational complexity and bandwidth, both of which are key to low-cost, portable solutions for prosthetics, exoskeletons or long-term gait monitoring applications.