Citation

Soo, Qian Fu and Tee, Connie and Goh, Michael Kah Ong (2023) Human Age Group Estimation Using Gait Features. International Journal on Advanced Science, Engineering and Information Technology (IJASEIT), 13 (6). pp. 2314-2327. ISSN 2088-5334

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Abstract

In many practical applications, identifying the target age group is essential for marketing products and services. For instance, gaming and entertainment companies need to understand which age groups are most likely to purchase their services. This knowledge allows them to optimize their products and services to better cater to their target audience. This study proposes an age group prediction system using gait features. Gait, in this context, pertains to an individual's unique walking style. A diverse dataset containing subjects from 3 to 70 years old is collected. The age group is classified into three categories: child, adult, and senior. The critical aspect of this research lies in the preprocessing techniques applied to the gait patterns. The gait patterns are extracted from landmark human joint positions' key point values and preprocessed using smoothening techniques. Additionally, dimension reduction techniques enhance computational efficiency and accuracy before feeding the features into a deep learning-based classifier. These preprocessing steps play a pivotal role in the success of the deep learning-based classifier. A promising accuracy of up to 95% is reported for correctly recognizing the human age groups. The outcomes of this investigation underscore the tremendous potential of leveraging machine learning techniques to refine marketing strategies and boost customer satisfaction. The proposed approach can aid companies in aligning their products and services with t

Item Type:	Article
Uncontrolled Keywords:	Machine learning, gait feature
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	22 Feb 2024 06:13
Last Modified:	22 Feb 2024 06:13
URII:	http://shdl.mmu.edu.my/id/eprint/12107

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