Literature DB >> 20363684

Individual muscle control using an exoskeleton robot for muscle function testing.

Jun Ueda1, Ding Ming, Vijaya Krishnamoorthy, Minoru Shinohara, Tsukasa Ogasawara.   

Abstract

Healthy individuals modulate muscle activation patterns according to their intended movement and external environment. Persons with neurological disorders (e.g., stroke and spinal cord injury), however, have problems in movement control due primarily to their inability to modulate their muscle activation pattern in an appropriate manner. A functionality test at the level of individual muscles that investigates the activity of a muscle of interest on various motor tasks may enable muscle-level force grading. To date there is no extant work that focuses on the application of exoskeleton robots to induce specific muscle activation in a systematic manner. This paper proposes a new method, named "individual muscle-force control" using a wearable robot (an exoskeleton robot, or a power-assisting device) to obtain a wider variety of muscle activity data than standard motor tasks, e.g., pushing a handle by hand. A computational algorithm systematically computes control commands to a wearable robot so that a desired muscle activation pattern for target muscle forces is induced. It also computes an adequate amount and direction of a force that a subject needs to exert against a handle by his/her hand. This individual muscle control method enables users (e.g., therapists) to efficiently conduct neuromuscular function tests on target muscles by arbitrarily inducing muscle activation patterns. This paper presents a basic concept, mathematical formulation, and solution of the individual muscle-force control and its implementation to a muscle control system with an exoskeleton-type robot for upper extremity. Simulation and experimental results in healthy individuals justify the use of an exoskeleton robot for future muscle function testing in terms of the variety of muscle activity data.

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Year:  2010        PMID: 20363684     DOI: 10.1109/TNSRE.2010.2047116

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  8 in total

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2.  Contraction Sensing with Smart Braid McKibben Muscles.

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Review 4.  Rehabilitation of gait after stroke: a review towards a top-down approach.

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Journal:  J Neuroeng Rehabil       Date:  2011-12-13       Impact factor: 4.262

Review 5.  Rehabilitative and assistive wearable mechatronic upper-limb devices: A review.

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6.  Integrated System for Monitoring Muscular States during Elbow Flexor Resistance Training in Bedridden Patients.

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Review 7.  Upper limb soft robotic wearable devices: a systematic review.

Authors:  Elena Bardi; Marta Gandolla; Francesco Braghin; Ferruccio Resta; Alessandra L G Pedrocchi; Emilia Ambrosini
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  8 in total

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