JACIII Vol.22 No.5 pp. 711-717
doi: 10.20965/jaciii.2018.p0711


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

Ramtin Aminpour and Elmer Dadios

De La Salle University
2401 Taft Avenue, Manila 1004, Philippines

March 16, 2018
June 16, 2018
September 20, 2018
neural network, classification, human activity recognition, LVQ, GMDH

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.

Cite this article as:
R. Aminpour and E. Dadios, “A Comparative Sensor Based Multi-Classes Neural Network Classifications for Human Activity Recognition,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.5, pp. 711-717, 2018.
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Last updated on Oct. 23, 2018