01965nas a2200241   4500000000100000000000100001008004100002260001200043653003100055653001900086653002000105653002400125100001500149700001500164700001500179700002300194245008000217856009800297300001000395490000600405520129800411022001401709       2016                            d  c06/201610aArtificial Neural Networks10aHuman Activity10aSkeleton Joints10aGesture Recognition1 aAnil Kumar1 aRahul Kala1 aAjay Kumar1 aSatish Kumar Singh00aHuman Activity Recognition in Real-Times Environments using Skeleton Joints  uhttp://www.ijimai.org/journal/sites/default/files/files/2016/05/ijimai20163_7_9_pdf_20470.pdf  a61-690 v33 aIn this research work, we proposed a most effective noble approach for Human activity recognition in real-time environments. We recognize several distinct dynamic human activity actions using kinect. A 3D skeleton data is processed from real-time video gesture to sequence of frames and getter skeleton joints (Energy Joints, orientation, rotations of joint angles) from selected setof frames. We are using joint angle and orientations, rotations information from Kinect therefore less computation required. However, after extracting the set of frames we implemented several classification techniques Principal Component Analysis (PCA) with several distance based classifiers and Artificial Neural Network (ANN) respectively with some variants for classify our all different gesture models. However, we conclude that use very less number of frame (10-15%) for train our system efficiently from the entire set of gesture frames. Moreover, after successfully completion of our classification methods we clinch an excellent overall accuracy 94%, 96% and 98% respectively. We finally observe that our proposed system is more useful than comparing to other existing system, therefore our model is best suitable for real-time application such as in video games for player action/gesture recognition.  a1989-1660