Human Activity Recognition From Accelerometer Data Using Convolutional Neural Network

被引：0

作者：

Lee, Song-Mi ^{[1
]}

Yoon, Sang Min ^{[1
]}

Cho, Heeryon ^{[1
]}

机构：

[1] Kookmin Univ, Human Comp Interact Lab, Seoul, South Korea

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP) | 2017年

基金：

新加坡国家研究基金会;

关键词：

human activity recognition; convolutional neural network; 3D accelerometer data; random forest;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We propose a one-dimensional (1D) Convolutional Neural Network (CNN)-based method for recognizing human activity using triaxial accelerometer data collected from users' smartphones. The three human activity data, walking, running, and staying still, are gathered using smartphone accelerometer sensor. The x, y, and z acceleration data are transformed into a vector magnitude data and used as the input for learning the 1D CNN. The ternary activity recognition performance of our 1D CNN-based method which showed 92.71% accuracy outperformed the baseline random forest approach of 89.10%.

引用

页码：131 / 134

页数：4

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