Human Activity Recognition Using Significant Skeletal Joints

The growing development in sensory implementation has facilitated human activity and can be used either as a tool for remote control of the device or as a tool for sophisticated human behaviour analysis. The prime contribution of the proposed system is to harness the potential of the learning approach in order to carry out computational efficiency towards the activity recognition process. A template for an activity recognition system is also provided in which the reliability of the process of recognition and system quality is preserved with a good balance. The research presents a condensed method of extraction of features from spatial and temporal features of event feeds that are further subject to the mechanism of machine learning to enhance the recognition accuracy. The importance of the proposed study is reflected in the results, which, when trained using KNN, show higher accuracy performance. The proposed system demonstrated 10-15% of memory usage over 532 MB of digitized real-time event information with 0.5341 seconds of processing time consumption.