Hand Motion Analysis using CNN
Harsh Raj1, Aditya Duggal2, Aditya Kumar Shetty M3, Sreekanth Uppara4, Srividya M S5
1Harsh Raj*, Dept. of Computer Science and Engineering, R.V. College of Engineering, Bengaluru, India.
2Aditya Duggal., Dept. of Computer Science and Engineering, R.V. College of Engineering, Bengaluru, India.
3Aditya K Shetty M., Dept. of Computer Science and Engineering, R.V. College of Engineering, Bengaluru, India.
4Sreekanth Uppara, Dept. of Computer Science and Engineering, R.V. College of Engineering, Bengaluru, India.
5Srividya M. S., Dept. of Computer Science and Engineering, R.V. College of Engineering, Bengaluru, India.
Manuscript received on May 02, 2020. | Revised Manuscript received on May 05, 2020. | Manuscript published on May 30, 2020. | PP: 26-30 | Volume-9 Issue-6, May 2020. | Retrieval Number: F3409039620/2020©BEIESP | DOI: 10.35940/ijsce.F3409.059620
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Hand motion detection and gesture recognition research has attracted large interest due to its wide range of applications in the field of Human computer interaction such as sign language recognition, 3D printing, virtual reality. There have been several approaches to create a robust algorithm to ease human computer interaction and perform in unfavourable environments.The real time recognition and learning of the model are big challenges. In this work, we use Convolutional Neural Network architecture to detect and classify hand motions, the region of interest of the image is passed through the neural network for the hand motion analysis and detection.Our system has achieved testing accuracy of 98%.
Keywords: Convolutional Neural Network, Human Computer Interaction, Robust, Testing accuracy.