Real-time Fall Detection and Prevention Control Using Intelligent Cane for Human Operator

Abstract

This paper proposes a novel human fall detection and prevention method for a walking aid. A three-wheeled omni-directional cane robot was developed previously for aiding the elderly walking. The relative position between the legs of user and the Center of Gravity (COG) of user play an important role in the fall detection when using the cane robot. The COG of user can be estimated from the angle of an inverted pendulum which represents human model. The angle of the inverted pendulum is computed by the support leg position, the hip position, and the force that the human pushes the cane. The fall direction and relative position between the human and the robot play an important role in the fall prevention. The proposed method is verified through experiments.