Paper ID: 2205.03234

Real Time On Sensor Gait Phase Detection with 0.5KB Deep Learning Model

Yi-An Chen, Jien-De Sui, Tian-Sheuan Chang

Gait phase detection with convolution neural network provides accurate classification but demands high computational cost, which inhibits real time low power on-sensor processing. This paper presents a segmentation based gait phase detection with a width and depth downscaled U-Net like model that only needs 0.5KB model size and 67K operations per second with 95.9% accuracy to be easily fitted into resource limited on sensor microcontroller.

Submitted: May 2, 2022