Application of wearable devices in 6G internet of things communication environment using artificial intelligence

Abstract

To study the application of wearable devices in the sixth generation (6G) Internet of things (IoT) communication environment, a human behaviour recognition method of wearable devices is designed by using artificial intelligence technology. First, the information in IoT is analyzed and processed by using data mining technology, and the classifier model is determined though experiments. Second, a human behaviour recognition model is designed by using artificial neural network (ANN), and the learning of neural network parameters is conducted by back propagation algorithm to improve the ability of behaviour recognition. Finally, the classifier and the human behaviour recognition model based on ANN are tested. The experimental results indicate that the classification accuracy of compound Bayes classification is the highest, so the compound Bayes classifier can be used as the data mining technology of wearable devices in IoT. When the number of neuron nodes in the hidden layer is 11, the recognition accuracy of human behaviour is more than 75%. In addition, compared with other algorithms, the overall recognition effect is better. Thus, the designed recognition model can be used for the recognition of human behaviour and it provides a reference for the study of the application of wearable devices in the environment of IoT.