gesture recognition

The article presents the development of an interactive system for recognizing and classifying human hand gestures based on machine learning technologies. A new approach to gesture representation is proposed, combining spatial and temporal characteristics of the location of key points of the hand, which ensures high accuracy, noise resistance, and adaptability of the system to various conditions of use.

The development of the Internet of Things (IoT) opens up new opportunities for creating intelligent services that enhance user interaction with surrounding devices. Modern IoT systems primarily use touchscreens and mobile applications for control; however, gesture-based methods can significantly expand their functionality. This work proposes a gesture recognition system applied to the control of IoT devices. The core of the system is the classification of finger movement trajectories using a Hidden Markov Model (HMM).