Posture Recognition and Imitation using Haar Wavelet Transform and Neural Networks
J. Yaser Daanial Khan, M. Khalid Mahmood
Journal of Intelligent Computing, Vol. 9, No. 4, 2018.
DOI: https://doi.org/ 10.6025/jic/2018/9/4/133-143
Link: http://www.dline.info/jic/fulltext/v9n4/jicv9n4_1.pdf
Abstract:
Human postures or gestures which essentially are static orientations of the body usually symbolizing a motive are put to use for bridging the gap between novice and expert users. Both the users are at the same natural level and capability to interact. To achieve an interaction between the machine and its human operator various techniques are suggested in the text. Their implementations though not cheap are also available in the market. Our research and implementation revolves around the use of an ordinary inexpensive camera to acquire live video and hence segment the human forearm locations using various filters. A database containing nearly 2000 images was created for this purpose. Furthermore a neural network for the purpose of pattern recognition among segmented images was trained. All the experimental results are gathered through
testing on a live application in an interactive scenario.