Citation:

AnNing, Mazida Ahmad, Huda lbrahim, 2024. Mobile Gesture Recognition for Accessibility: A Comparative Study of Machine Learning Algorithms, ESP Journal of Engineering & Technology Advancements  4(2): 28-33.

Abstract:

This paper presents a comparative study of machine learning algorithms for mobile gesture recognition in the context of accessibility. The background of the study is the increasing importance of mobile devices and the need to make them accessible to individuals with different physical abilities. The purpose of this research is to evaluate different machine-learning algorithms and determine their effectiveness in recognizing gestures on mobile devices. The study employs a dataset of gesture samples collected from a diverse group of users. Various machine learning algorithms, including support vector machines, random forests, and neural networks, are implemented and compared based on their accuracy and computational efficiency. The results show that all three algorithms achieve high accuracy in gesture recognition, with support vector machines performing slightly better in terms of both accuracy and efficiency. In conclusion, this study provides valuable insights into the performance of different machine learning algorithms for mobile gesture recognition, contributing to the development of more accessible mobile interfaces.

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Keywords:

Mobile Gesture Recognition, Machine Learning Algorithms, Accessibility.