A comparative sensor based multi-classes neural network classifications for human activity recognition

College

Gokongwei College of Engineering

Department/Unit

Manufacturing Engineering and Management

Document Type

Article

Source Title

Journal of Advanced Computational Intelligence and Intelligent Informatics

Volume

22

Issue

5

First Page

711

Last Page

717

Publication Date

9-1-2018

Abstract

Human activity recognition with the smartphone could be important for many applications, especially since most of the people use this device in their daily life. A smartphone is a portable gadget with internal sensors and enough hardware power to accommodate this problem. In this paper, three neural network algorithms were compared to detect six major activities. The data are collected by a smartphone in real life and simulated on the remote server. The results show that MLP and GMDH neural network have better accuracy and performance compared with the LVQ neural network algorithm. © 2018 Fuji Technology Press.All Rights Reserved.

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Digitial Object Identifier (DOI)

10.20965/jaciii.2018.p0711

Disciplines

Computer Sciences | Mechanical Engineering

Keywords

Human activity recognition; Neural networks (Computer science)

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