Development of a stress-free algorithm for controlling active running platforms

Abstract

The problem of increasing the accuracy of predicting human actions is an urgent task for various human-machine systems. The study examines the solution to the problem of predicting human speed using neural network algorithms, computer vision technologies, and machine learning. The formalization and software implementation of a neural network speed prediction algorithm are presented. To solve the problems of determining the current speed and predicting the upcoming positions of the human body depending on the dynamics of its movement, a comparison of various machine learning models was carried out. The RandomForestRegressor algorithm showed the best position prediction accuracy. The best determination of the current speed was demonstrated by dense multilayer neural networks. The experiment revealed that when predicting a person's position at an interval of 0.6 seconds, his speed is determined with an accuracy of more than 90%. The results obtained can be used to implement neural network algorithms for controlling human-machine systems.