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

Yian Chen, Jien De Sui, Tian Sheuan Chang

研究成果: Conference contribution同行評審

摘要

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.

原文English
主出版物標題2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728173993
DOIs
出版狀態Published - 28 九月 2020
事件7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 - Taoyuan, Taiwan
持續時間: 28 九月 202030 九月 2020

出版系列

名字2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Conference

Conference7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
國家Taiwan
城市Taoyuan
期間28/09/2030/09/20

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