Certain Investigations on Hand Gesture Recognition Systems

In recent years, hand gesture recognition has played an important role in human-computer interaction (HCI). The field of HCI has extensively used this technology to improve the user experience. It is a constructive method of interacting with machines that has applications across several areas, such as virtual reality, robot control, deaf-mute communication, medical applications, human-computer interface (HCI), and others. Gestures may originate from a variety of human body parts, but most of them arise from the hand or the face. The methods used in research that use hand gestures range widely, including sensor-based, vision-based, depth-based, motion-based, and skeleton-based methods. A gesture recognition system's primary focus is to make it simpler to create a realistic model that can realize human gestures and utilize them to manage electronic devices. This study analysed the approaches, algorithmic techniques, and models that recognize hand motions, which include Dynamic Time Warping (DTW), Support Vector Machine (SVM), K Nearest Neighbours (KNN), Artificial Neural Networks (ANN), Convolution Neural Network (CNN), and Hidden-Markov models (HMM).