Skeleton joint trajectories based human activity recognition using deep RNN

Human Activity Recognition is the act of recognizing activities performed by humans in real-time. This can be done using video data or more advanced forms of data like- inertial, depth maps, or human skeletal joint trajectories. In this work, we perform human action recognition through skeletal joint tracking of the human body using a deep recurrent neural network. Our proposed method was then tested on two standard databases, namely UTD-MHAD and MSR- Daily Activity 3D-Datasets. The judgement on the efficiency of our proposed model was made by comparing it to various, recently published, State-Of-The-Art (SOTA) methods.The evaluations of our model show that our method performs well on both the datasets and achieves an accuracy of 99.07%, and 91%, on UTD-MHAD and MSR Daily Activity databases respectively, and can recognize human activities from a variety of domains.