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

• URL: http://arxiv.org/abs/2205.03234v1
• Date: Mon, 2 May 2022 09:59:56 GMT
• Title: Real Time On Sensor Gait Phase Detection with 0.5KB Deep Learning Model
• Authors: Yi-An Chen, Jien-De Sui and Tian-Sheuan Chang
• Abstract summary: Gait phase detection with convolution neural network provides accurate classification but demands high computational cost. 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.
• Score: 2.79329087573672
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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