Preliminary Evaluation of Intelligent Intention Estimation Algorithms for an Actuated Lower-Limb Exoskeleton. (27th February 2013)

• Record Type: Journal Article
• Authors: Chandrapal, Mervin; Chen, XiaoQi; Wang, Wenhui; Stanke, Benjamin; Pape, Nicolas Le
• Is Part Of: International journal of advanced robotic systems. Volume 10:Number 2(2013)
• Publication Date: 2013-02-27
• Subjects: EMG-to-Force -- Exoskeleton -- Artificial Neural Networks -- Differential Evolution; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.5772/56063
• Languages: English
• ISSNs: 1729-8806

Abstract

This paper describes the experimental testing of an actuated lower-limb exoskeleton. The exoskeleton is designed to alleviate the loading at the knee joint by supplying assistive torque. It is hypothesized that the support provided will reduce the muscular effort required to perform activities of daily living and thus facilitate the execution of these movements by those who previously had limited mobility. The exoskeleton is actuated by four pneumatic artificial muscles, each providing 150N of pulling force to assist in the flexion and extension of the knee joint. The exoskeleton system estimates the user's intended motion using muscle activity information recorded from five thigh muscles, together with the knee angle. To experimentally evaluate the performance of the device, the exoskeleton was worn by an able-bodied user, whilst performing the sit-to-stand-to-sit movement. In addition, the three intention estimation algorithms were also tested to determine the influence of the various algorithms on the support provided. The results show a significant reduction in the user's muscle activity (≈ 20%) when assisted by the exoskeleton in a predictable manner.