Multisensor Intelligent Fall Perception Algorithm considering Precise Classification of Human Behavior Characteristics

Abstract

In order to improve the accuracy and efficiency of human motion perception, a multisensor intelligent fall perception algorithm considering the precise classification of human behavior characteristics is proposed. Multisensor devices (smart watches, smart phones) collect data such as acceleration and heart rate of the human body to obtain human behavior data. On the basis of human behavior data collection, the acceleration characteristics of a falling state are extracted, and the SVM method is used to classify human behavior characteristics. Cuckoo search is used to optimize the width of the SVM kernel and improve the accuracy of human behavior recognition. Finally, based on the behavior recognition results, the intelligent perception of human falling behavior is realized through the exercise preparation potential. The experimental results show that the perceptual accuracy of this method is high, which has reached 90%, and the perception efficiency is higher. The minimum perception time is only 0.56 s, which fully verifies the effectiveness of this method. It can be widely used in human-computer interaction, machine vision, and other fields.