Application of improved K-Nearest Neighbor algorithm gesture recognition system in air conditioning control

Abstract

Given the inconvenience of air conditioning control methods based on control panels or remote controls, the application of an improved K-Nearest Neighbor (KNN) algorithm gesture recognition system based on the entropy weight method in air conditioning control is carried out. First, establish the correspondence between the ten commonly used gestures from 1 to 10 and the air conditioning control commands, then collect the gesture images from 1 to 10 through the camera, and then perform image preprocessing, gesture contour extraction, and feature calculation. In the Euclidean distance calculation process, the weight coefficient determined by the entropy weight method is added, and the trained improved KNN model is used to recognize the gesture, thereby improving the accuracy of the gesture recognition process. Simulation studies show that the accuracy of the gesture recognition system based on the improved KNN model is over 95%. This result is 9.7%-11.6% higher than that of the conventional KNN model before improvement and 11.8%-12.9% higher than the accuracy of the support vector machine algorithm (SVM) model. The experimental results show that the accuracy of the gesture recognition system based on the improved KNN algorithm is between 77.5% and 87.5%. Therefore, the method proposed in this paper has a good application prospect in air-conditioning control.