Exercise Motion Classification from Large-Scale Wearable Sensor Data Using Convolutional Neural Networks

被引：0
|
作者：
Um, Terry Taewoong [1 ]
Babakeshizadeh, Vahid [2 ]
Kulic, Dana [1 ]
机构：
[1] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada
[2] Push Inc, Toronto, ON M5B 2G9, Canada
来源：
2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) | 2017年
基金：
加拿大自然科学与工程研究理事会;
关键词：
HUMAN ACTIVITY RECOGNITION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The ability to accurately identify human activities is essential for developing automatic rehabilitation and sports training systems. In this paper, large-scale exercise motion data obtained from a forearm-worn wearable sensor are classified with a convolutional neural network (CNN). Time-series data consisting of accelerometer and orientation measurements are formatted as images, allowing the CNN to automatically extract discriminative features. A comparative study on the effects of image formatting and different CNN architectures is also presented. The best performing configuration classifies 50 gym exercises with 92.1% accuracy.
引用
收藏
页码：2385 / 2390
页数：6
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