Motion classification approach based on biomechanical analysis of human activities

There has been an increased interest in recognition applications of human motion building skeleton models on the recorded video images. Although various methods have been proposed for recognition of human activities obtaining different data from realistic videos, the dependencies and relations among human motions have not been much investigated. We have proposed an approach for efficient human action recognition using relations between motion data taken joint data positions from skeleton sequences in this paper. Firstly, we have collected many action data using a sensor camera that is a practice and cheap capturing device and combined with a biomechanical model achieved by experimental data. Then, determining key frames on different actions we have compared human motions with key joints features for action recognition accuracy. The main contribution of this paper is efficient and suitable method for recognizing human motions with less data and biomechanical model. Experiments validate that our recognition approach, which uses three different actions performed by five different actors with tracing data on video sequences, outperforms most existing methods and the model is computationally efficient.