Research on HMM-based Exoskeleton Robot Falling Prediction Algorithm

Abstract

Exoskeleton robot is essentially a wearable robot. In recent years, the research of exoskeleton robots has become a new hot spot, and has gradually been widely used in military, medical and civilian fields. However, the balance and safety issues of power-assisted exoskeleton robots need to be solved urgently. At present, although there are many researches on exoskeleton robots at home and abroad, the research on the falling problem of assisted lower limb exoskeleton robots is not in-depth. Existing studies on falls are mainly focused on the detection of falls, while the prediction of falls is relatively small, and these studies rely more on a single sensor, and its accuracy needs to be improved. In response to the status quo, This paper proposes a fall prediction algorithm based on HMM to study the fall behaviour of the exoskeleton human-machine system in advance, it provides a reference for the anti-fall measures of the exoskeleton robot and aims to effectively reduce the damage of the human-machine system.