Abstract: Recently, the techniques for monitoring and recognizing human walking patterns have become one of the most important research topics, especially in health applications related to fitness and disease progression. This paper aims at combining machine learning techniques with Smartphone sensors readings (i.e. accelerometer sensor) in order to develop a smart model capable of classifying walking patterns into different categories (fast, normal, slow, very slow or very fast) along with variable of gender, male or female and sensor place, waist, hand or leg. In this paper, we use several machine learning algorithms including: Neural Network, KNN, Random forest, and Tree to train and test extracted data from Smartphone sensors. The results indicate that Smartphone sensor can be exploited in developing a reliable model for identifying the human walking patterns based on accelerometer readings. In addition, results show that Random forest is the best performing classifiers with an accuracy of (92.3%) and (91.8%) when applied on waist datasets for both males and females respectively.

Zaid T. Alhalhouli, “A Method for Predicting Human Walking Patterns using Smartphone’s Accelerometer Sensor” International Journal of Advanced Computer Science and Applications(IJACSA), 11(7), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110750

@article{Alhalhouli2020,
title = {A Method for Predicting Human Walking Patterns using Smartphone’s Accelerometer Sensor},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110750},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110750},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {7},
author = {Zaid T. Alhalhouli}
}