HUMAN FITNESS POSE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK

This study presents three models to predict human fitness poses. First, we use the MoveNet model to get the human keypoints. The first model is the Feedforward Neural Network to predict the human pose of each frame. Then the second model we have is a Long Short-term Memory Network (LSTM). The last model we use is the Gated Recurrent Unit (GRU). The last two models can use the time series data as the input. As a result, the last two models have a better result than the first model. The accuracy of LSTM is 94.76% and the accuracy GRU model is 97.27%