Human Activities of Daily Living Recognition with Graph Convolutional Network

A rapidly growing population presents many challenges to healthcare and security surveillance around the world. Human activity recognition is one of the active research areas to recognizing and understanding the various activities. Many researchers are finding and representing the details of human body gestures to determine human activity or action. The result, however, is still unsatisfactory due to the inclusion of irrelevant images. The model is rather rudimentary and it does not specific enough for representing the meaning of images. In this paper, we propose a methodology for human activities of daily living recognition with 4 steps (1) processes including text-based embedding concept, (2) semi-supervised graph node, (3) graph convolution network, and (4) measurement and evaluation. The experimental results indicate that our proposed approach offers significant performance improvements in data set 2 in 10-fold, with the maximum of 79.34%.