10.35940/ijsce.F3409.059620
https://zenodo.org/records/5533623
oai:zenodo.org:5533623
Harsh Raj
Harsh Raj
Dept. of Computer Science and Engineering, R.V. College of Engineering, Bengaluru, India.
Aditya Duggal
Aditya Duggal
Dept. of Computer Science and Engineering, R.V. College of Engineering, Bengaluru, India.
Aditya Kumar Shetty M
Aditya Kumar Shetty M
Dept. of Computer Science and Engineering, R.V. College of Engineering, Bengaluru, India.
Sreekanth Uppara
Sreekanth Uppara
Dept. of Computer Science and Engineering, R.V. College of Engineering, Bengaluru, India.
Srividya M S
Srividya M S
Dept. of Computer Science and Engineering, R.V. College of Engineering, Bengaluru, India.
Hand Motion Analysis using CNN
Zenodo
2020
Convolutional Neural Network, Human Computer Interaction, Robust, Testing accuracy
Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
Publisher
2020-05-30
eng
2231-2307
Creative Commons Attribution 4.0 International
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%.