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A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect
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Author(s): Rodrigo Ibañez (ISISTAN (UNICEN-CONICET) Research Institute, Argentina), Alvaro Soria (ISISTAN (UNICEN-CONICET) Research Institute, Argentina), Alfredo Raul Teyseyre (ISISTAN (UNICEN-CONICET) Research Institute, Argentina), Luis Berdun (ISISTAN (UNICEN-CONICET) Research Institute, Argentina)and Marcelo Ricardo Campo (ISISTAN (UNICEN-CONICET) Research Institute, Argentina)
Copyright: 2020
Pages: 22
Source title:
Cognitive Analytics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-2460-2.ch056
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Abstract
Progress and technological innovation achieved in recent years, particularly in the area of entertainment and games, have promoted the creation of more natural and intuitive human-computer interfaces. For example, natural interaction devices such as Microsoft Kinect allow users to explore a more expressive way of human-computer communication by recognizing body gestures. In this context, several Supervised Machine Learning techniques have been proposed to recognize gestures. However, scarce research works have focused on a comparative study of the behavior of these techniques. Therefore, this chapter presents an evaluation of 4 Machine Learning techniques by using the Microsoft Research Cambridge (MSRC-12) Kinect gesture dataset, which involves 30 people performing 12 different gestures. Accuracy was evaluated with different techniques obtaining correct-recognition rates close to 100% in some results. Briefly, the experiments performed in this chapter are likely to provide new insights into the application of Machine Learning technique to facilitate the task of gesture recognition.
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