A Comparative Sensor Based Multi-Classes Neural Network Classifications for Human Activity Recognition | Zendy

Ramtin Aminpour | Zendy; Elmer P. Dadios | Zendy

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A Comparative Sensor Based Multi-Classes Neural Network Classifications for Human Activity Recognition

Author(s) -

Ramtin Aminpour,

Elmer P. Dadios

Publication year - 2018

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2018.p0711

Subject(s) - computer science , gadget , artificial neural network , activity recognition , learning vector quantization , artificial intelligence , machine learning , time delay neural network , algorithm

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.

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