Real-Time Control of Quasi-Active Hip Exoskeleton Based on Gaussian Mixture Model Approach

Abstract

Lower back pain is a major cause of disability and sick day absences. As lower back pain can result in decreased life quality as well as lower industrial productivity, multiple research groups and companies are looking into possible solutions. One of such solutions could be exoskeletons, that engage and disengage the actuators depending on the movements performed by the user. Otherwise we risk hindering the users movements and increasing his metabolic costs. We implemented an exoskeleton control using finite state machine combined with a Gaussian mixture model movement classifier. By conducting a test battery with a subject wearing the exoskeleton we were able to engage the exoskeleton actuators when appropriate and keep them disengaged to allow a full and unhindered range of motion. The results show our exoskeleton control correctly engages and disengages actuators based on the movements being performed by the user.

This work was supported by the European Union’s Horizon 2020 through the SPEXOR project (contract no. 687662).