Deep Learning Based Hand Gesture Recognition Using

Abstract

In last couple of decades, much work has been done on Human Activity Recognition (HAR). HAR is a broad research area and excelled in many applications such as security, health care, gaming, intelligent environments and activity of daily living. One of the main applications of HAR is Hand Gesture Recognition (HGR). Hand gesture recognition is a complex classification problem. Previous studies show that different sensor technologies and different classification approaches have been used for gesture recognition. But still certain aspects need to be addressed so that the robustness and reliability of the gesture- based models can be improved. This research work is comprising of four traditional machine learning models SVM, KNN, Random Forest, Decision Tree and three deep learning models RNN-LSTM, CNN-LSTM and CNN. These seven models are developed and implement for the hand gesture data acquired from IMU. These models are then evaluated on the basis of different parameters. The analysis of result shows that deep learning-based models are clearly the best choice to be used for HGR systems.