LEVERAGING THE INTERNET OF THINGS BASED ON THE ANALYTICAL STUDY OF THE SKELETON JOINT POSITION IN RECOGNISING THE QUALITY OF HUMAN ACTIVITY

Abstract

To give consequently dissecting and identifying human exercises to offer better help in the medical services area, security reason and so on Strategy: We have utilized UTKinect-Action 3D dataset containing the Position of 20 body joint caught by Kinect sensor. We chose two arrangement of joints J1 and J2; after that, we have shaped a few principles for movement grouping then we have applied SVM classifier, KNN classifier utilizing Euclidean distance and KNN classifier using Minkowski distance for action order. At the point when we have been used joint set J1 we got 97.8% exactness with SVM classifier, 98.8% precision with KNN classifier utilizing Euclidean distance, and 98.9% exactness with KNN classifier utilizing Minkowski distance and for joint set J2 we got 97.7% exactness with SVM classifier, 98.6% exactness with KNN classifier using Euclidean distance, and 98.7% exactness with KNN classifier utilizing Minkowski distance. We have arranged four exercises hand waving, standing, sitting and picking. In future, more exercises can likewise be remembered for this examination. IoT alongside this action acknowledgement strategy can be utilized to lessen overheads