Automatic Gesture Recognition for Health Care Using ReliefF and Fuzzy kNN Sriparna Saha, Monalisa Pal, Amit Konar and Diptendu Bhattacharya Abstract This work describes a simple method to detect gestures revealing muscle and joint pain. The data is acquired using Kinect Sensor. For the purpose of feature extraction, the twenty joint coordinates are processed in three dimensional space. From each frame, 171 Euclidean distances are calculated and to reduce the dimension of the feature space, ReliefF algorithm is implemented. The classifica- tion stage is consists of fuzzy k-nearest neighbour classifier. The proposed method is employed to recognize 24 body gestures and yields a high recognition rate of 90.63 % which is comparatively higher than several other algorithms for young person gesture recognition works. Keywords Fuzzy k-nearest neighbour  Gesture recognition  Health care  Kinect sensor  ReliefF 1 Introduction The sphere of gesture recognition encloses the identification of various body gestures and human behaviours. This paper explores the 3D mode of gesture recognition for health care. The gestures as a result of muscle and joint pains due to certain disorders S. Saha (&)  M. Pal  A. Konar Electronics and Tele-Communication Engineering Department, Jadavpur University, Kolkata, India e-mail: sahasriparna@gmail.com M. Pal e-mail: monalisap90@gmail.com A. Konar e-mail: konaramit@yahoo.co.in D. Bhattacharya Computer Science and Engineering Department, NIT Agartala, Agartala, India e-mail: diptendu1@gmail.com © Springer India 2015 J.K. Mandal et al. (eds.), Information Systems Design and Intelligent Applications, Advances in Intelligent Systems and Computing 340, DOI 10.1007/978-81-322-2247-7_72 709