Open Access

ARTICLE

A Three-Dimensional Real-Time Gait-Based Age Detection System Using Machine Learning

Muhammad Azhar^1,*, Sehat Ullah¹, Khalil Ullah², Habib Shah³, Abdallah Namoun⁴, Khaliq Ur Rahman⁵

1 Department of Computer Science & IT, University of Malakand, Chakdara, 18800, Pakistan
2 Department of Software Engineering, University of Malakand, Chakdara, 18800, Pakistan
3 College of Computer Science, King Khalid University, Abha, 62529, Saudi Arabia
4 Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, 42351, Saudi Arabia
5 Department of Statistics, Abdul Wali Khan University, Mardan, 23200, Pakistan

* Corresponding Author: Muhammad Azhar. Email:

Computers, Materials & Continua 2023, 75(1), 165-182. https://doi.org/10.32604/cmc.2023.034605

Received 21 July 2022; Accepted 12 October 2022; Issue published 06 February 2023

Abstract

Human biometric analysis has gotten much attention due to its widespread use in different research areas, such as security, surveillance, health, human identification, and classification. Human gait is one of the key human traits that can identify and classify humans based on their age, gender, and ethnicity. Different approaches have been proposed for the estimation of human age based on gait so far. However, challenges are there, for which an efficient, low-cost technique or algorithm is needed. In this paper, we propose a three-dimensional real-time gait-based age detection system using a machine learning approach. The proposed system consists of training and testing phases. The proposed training phase consists of gait features extraction using the Microsoft Kinect (MS Kinect) controller, dataset generation based on joints’ position, pre-processing of gait features, feature selection by calculating the Standard error and Standard deviation of the arithmetic mean and best model selection using R² and adjusted R² techniques. T-test and ANOVA techniques show that nine joints (right shoulder, right elbow, right hand, left knee, right knee, right ankle, left ankle, left, and right foot) are statistically significant at a 5% level of significance for age estimation. The proposed testing phase correctly predicts the age of a walking person using the results obtained from the training phase. The proposed approach is evaluated on the data that is experimentally recorded from the user in a real-time scenario. Fifty (50) volunteers of different ages participated in the experimental study. Using the limited features, the proposed method estimates the age with 98.0% accuracy on experimental images acquired in real-time via a classical general linear regression model.

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Keywords

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