Abstract
Hand gestures are part of communication tools that allows people to express their ideas and feelings. Those gestures can be used to insure a communication not only between people but also to replace traditional devices in human-machine interaction (HCI). This last leads us to use this technology in the E-learning domain. COVID’19 pandemic has attest the importance of E-learning. However, the Practical Activities (PA), as an important part of the learning process, are absent in the majority of E-learning plateforms. Therefore, this paper proposes a convolution neural network (CNN) method to ensure the detection of the hand gestures so the user can control and manipulate the virtual objects in the PA environment using a simple camera. To achieve this goal two datasets have been merged. Also the skin model and background subtraction were applied to obtain a performed training and testing datasets for the CNN. Experimental evaluation shows an accuracy rate of 97,2.%.
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Ennaji, F.Z., El Kabtane, H. (2023). A Deep Learning Approach for Hand Gestures Recognition. In: Aboutabit, N., Lazaar, M., Hafidi, I. (eds) Advances in Machine Intelligence and Computer Science Applications. ICMICSA 2022. Lecture Notes in Networks and Systems, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-031-29313-9_11
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DOI: https://doi.org/10.1007/978-3-031-29313-9_11
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