Yoga Pose Detection and Classification Using MobileNet-LSTM with Hunter-Prey Optimization
Yoga represents an age-old practice that is beneficial for both psychological and physical health. The yoga promotes self-learning and improper poses can seriously harm muscles and ligaments. The accurate recognition of yoga poses from images remains challenging due to high intra-class similarity, background variations, and redundant feature generated by deep learning models. This paper proposed a model that combined the MobileNet with Hunter-Prey Optimization, and Long Short-Term Memory to detect the efficient and accurate yoga poses. In the proposed model, Mobile Net is used as a lightweight backbone to extract high-level unique and spatial features from yoga images. The extracted feature vectors are then optimized using the HPO algorithm that selects the most relevant and discriminative features and remove redundant feature. The optimized feature is fed into an LSTM model. The LSTM model operates on static image inputs as a gated feature refinement and classification rather than modelling temporal pose transitions. The performance of model is measured on a 6-class subset of the Yoga-82 dataset that allows the controlled experimental parameters. The final results shows that the proposed model obtained an accuracy score of 97.97% performed well as baseline models such as MobileNet and MobileNet + LSTM. The HPO-based feature selection reduces the feature dimensionality up to 69.53% to improved computational efficiency and reduced inference time. The result shows when combined the feature optimization with lightweight MobileNet model then enhances classification performance and maintain the efficiency of model. The proposed model is applicable for real-time yoga pose recognition with future work focused on extending the model to full-scale datasets and video-based temporal modelling.