  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.
