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
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: firstname.lastname@example.org, email@example.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).