Hand Gesture recognition and classification by Discriminant and Principal Component Analysis using Machine Learning techniques

This paper deals with the recognition of different hand gestures through machine learning approaches and principal component analysis. A Bio-Medical signal amplifier is built after doing a software simulation with the help of NI Multisim. At first a couple of surface electrodes are used to obtain the Electro-Myo-Gram (EMG) signals from the hands. These signals from the surface electrodes have to be amplified with the help of the Bio-Medical Signal amplifier. The Bio-Medical Signal amplifier used is basically an Instrumentation amplifier made with the help of IC AD 620.The output from the Instrumentation amplifier is then filtered with the help of a suitable Band-Pass Filter. The output from the Band Pass filter is then fed to an Analog to Digital Converter (ADC) which in this case is the NI USB 6008.The data from the ADC is then fed into a suitable algorithm which helps in recognition of the different hand gestures. The algorithm analysis is done in MATLAB. The results shown in this paper show a close to One-hundred per cent (100%) classification result for three given hand gestures.

@article{Gupta2012,
  title     = {Hand Gesture recognition and classification by Discriminant and Principal Component Analysis using Machine Learning techniques},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {1},
  number    = {9},
  year      = {2012},
  publisher = {The Science and Information Organization},
  author    = {Sauvik Das Gupta and Souvik Kundu and Rick Pandey and Rahul Ghosh and Rajesh Bag and Abhishek Mallik},
  doi       = {10.14569/IJARAI.2012.010908},
  url       = {https://doi.org/10.14569/IJARAI.2012.010908}
}