Minimization of body loads during sit-to-stand movement by continuous genetic algorithm

Abstract

Sit-to-stand (STS) rehabilitation equipment traditionally simulates natural movements of unspecified healthy people. This type of movement may not place the lowest load on a patient's body. These factors let us develop a novel and widely applicable system for rehabilitation purposes that suggests a motion that places low body loads at the lower limb joints. This paper describes the core computation in the system that calculates a STS movement that places a minimum body load by using Continuous Genetic Algorithm (CGA). The minimization process starts from measurement of kinematic and kinetic data of a human subject by using gyroscopic sensors and reaction force plates, and then estimation and evaluation of the body load are followed. The body load which is minimized during STS movement was quantified by an index value. The index value strongly correlates with the chair height. Decreasing this index value by changing the movement during STS transfer could reduce the impact on the body, which is the same amount of load placed on the body while standing up from a higher chair height. The angular displacements of the averaged natural STS movement of the subject were considered as one of the several ways to stand up and the movements were assessed by means of the index value in CGA. A modified STS movement that can minimize the body load was computed after the optimization and it could be a personalized optimum movement allowing users to conduct rehabilitation with lesser unnecessary body load.