A Smartphone-Based Application Using Machine Learning for Gesture Recognition: Using Feature Extraction and Template Matching via Hu Image Moments to Recognize Gestures

Abstract

The rapid development of smart devices, such as smartphones and tablets, leads to new challenges and ushers in a new stage of human-computer interaction. In this context, it becomes essential to develop methods and techniques for a better and more natural interaction with these devices. In this article, we address the problem of gesture segmentation and recognition, taking into account the limited computational resources of smartphone devices. We introduce a methodology for designing efficient and useful applications that, by using low-cost and widely diffused technologies, can be used in telemedicine, home-based rehabilitation, and other biomedical applications for patients with specific disabilities. To this end, we have designed a new machine-learning algorithm that is able to identify hand gestures through the use of Hu image moments, due to their invariance to rotation, translation, scaling, and their low computational cost. The experimental results collected from a case study show an excellent gesture recognition performance and an affordable real-time execution speed on smartphones and other mobile devices.