Vision Based Human Gesture Analysis for Human Machine Interactive Applications
Triveni P. Kulli1, Maheswari A2
1Triveni P. Kulli, Student, M.Tech, Department of Electronics and Communication Engineering, Sambhram Institute of Technology, M.S Palya, Bangalore (Karnataka), India.
2Maheswari A, Assistant Professor, Department of Electronics and Communication Engineering, Sambhram Institute of Technology, M.S Palya, Bangalore (Karnataka), India.
Manuscript received on 10 July 2015 | Revised Manuscript received on 20 July 2015 | Manuscript Published on 30 July 2015 | PP: 21-27 | Volume-5 Issue-2, July 2015 | Retrieval Number: B2129075215/15©BEIESP
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© The Authors. 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: In human-Machine Interaction (HMI), hand gestures provide an interactive natural communication between human and machines. Specifically, the visual interpretation of hand gestures will improve the usability, efficiency and quality of HMI. Recently, researchers are more interested in exploring computer vision-based analysis and interpretation of hand gestures, especially after the release of Microsoft Kinect depth sensor. Major applications of human gesture analysis are surveillance, medicine, enhanced visual games, man-machine interface and animation. Using the low cost and high reliable sensor ‘Kinect’ with its SDK tool kit for windows gives a way to solve with ease some of the difficult problems encountered when working with conventional cameras. The main objective of this project is to control the Human Machine Interactive applications such as graphical user interface, translating of sign language and stroke rehabilitation using Human Gesture Analysis techniques using the Microsoft’s Kinect sensor and LabVIEW, a powerful software tool from National Instruments.
Keywords: Human Gestures, Kinect Sensor, Skeletal Analysis, NILabVIEW, Data Acquisition.
Scope of the Article: Measurement & Performance Analysis