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
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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