Home > Archive > 2021 > Volume 11 Number 4 (July 2021) >
IJMLC 2021 Vol.11(4): 298-303 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.4.1051

Smartphone Sensor Accelerometer Data for Human Activity Recognition Using Spiking Neural Network

Nor Surayahani Suriani and Fadilla ‘Atyka Nor Rashid

Abstract—Recognizing human actions is a challenging task and actively research in computer vision community. The task of human activity recognition has been widely used in various application such as human monitoring in a hospital or public spaces. This work applied open dataset of smartphones accelerometer data for various type of activities. The analogue input data is encoded into the spike trains using some form of a rate-based method. Spiking neural network is a simplified form of dynamic artificial network. Therefore, this network is expected to model and generate action potential from the leaky integrate-and-fire spike response model. The leaning rule is adaptive and efficient to present synapse exciting and inhibiting firing neuron. The result found that the proposed model presents the state-of-the-art performance at a low computational cost.

Index Terms—Activity recognition, spiking neural network, accelerometer sensor, spike train, firing rate.

The authors are with the Universiti Tun Hussein Onn Malaysia, Malaysia (Corresponding author: Nor Surayahani Suriani; e-mail: nsuraya@uthm.edu.my, fadilla.atyka@gmail.com).


Cite: Nor Surayahani Suriani and Fadilla ‘Atyka Nor Rashid, "Smartphone Sensor Accelerometer Data for Human Activity Recognition Using Spiking Neural Network," International Journal of Machine Learning and Computing vol. 11, no. 4, pp. 298-303, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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