Literature DB >> 17009491

A novel mechatronic body weight support system.

Martin Frey1, Gery Colombo, Martino Vaglio, Rainer Bucher, Matthias Jörg, Robert Riener.   

Abstract

A novel mechatronic body weight support (BWS) system has been developed to provide precise body weight unloading for patients with neurological or other impairments during treadmill training. The system is composed of a passive elastic spring element to take over the main unloading force and an active closed-loop controlled electric drive to generate the exact desired force. Both force generating units, the passive spring and the active electric drive, act on the patient via a polyester rope connected to a harness worn by the patient. The length of the rope can be adjusted with an electric winch to adapt the system to different patient sizes. The system is fully computer controlled. At unloading loads of up to 60 kg and walking speeds of up to 3.2 km/h, the mean unloading error and the maximum unloading error of the presented BWS system was less than 1 and 3 kg, respectively. The performance was compared with those of two purely passive BWS systems currently being used by most other rehabilitation groups. This comprised counterweight systems and static BWS systems with fixed rope lengths. Counterweight systems reached mean and maximum unloading errors of up to 5.34 and 16.22 kg, respectively. The values for the static BWS were 11.02 kg and 27.67 kg, respectively. The novel mechatronic BWS system presented in this study adjusts desired unloading changes of up to 20 kg within less than 100 ms. Thus, not only constant BWS, but also gait cycle dependent or time variant oscillations of the desired force can be realized with high accuracy. Precise and constant unloading force is believed to be an important prerequisite for BWS gait therapy, where it is important to generate physiologically correct segmental dynamics and ground reaction forces. Thus, the novel BWS system presented in this paper is an important contribution to maximize the therapeutic outcome of human gait rehabilitation.

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Year:  2006        PMID: 17009491     DOI: 10.1109/TNSRE.2006.881556

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


  15 in total

1.  Versatile robotic interface to evaluate, enable and train locomotion and balance after neuromotor disorders.

Authors:  Nadia Dominici; Urs Keller; Heike Vallery; Lucia Friedli; Rubia van den Brand; Michelle L Starkey; Pavel Musienko; Robert Riener; Grégoire Courtine
Journal:  Nat Med       Date:  2012-07       Impact factor: 53.440

2.  Vertical perturbations of human gait: organisation and adaptation of leg muscle responses.

Authors:  V Bachmann; R Müller; H J A van Hedel; V Dietz
Journal:  Exp Brain Res       Date:  2007-11-23       Impact factor: 1.972

3.  Orthotic Body-Weight Support Through Underactuated Potential Energy Shaping with Contact Constraints.

Authors:  Ge Lv; Robert D Gregg
Journal:  Proc IEEE Conf Decis Control       Date:  2015-12

4.  Underactuated Potential Energy Shaping with Contact Constraints: Application to a Powered Knee-Ankle Orthosis.

Authors:  Ge Lv; Robert D Gregg
Journal:  IEEE Trans Control Syst Technol       Date:  2017-01-17       Impact factor: 5.485

5.  Sensitivity of joint moments to changes in walking speed and body-weight-support are interdependent and vary across joints.

Authors:  Saryn R Goldberg; Steven J Stanhope
Journal:  J Biomech       Date:  2013-01-30       Impact factor: 2.712

6.  Feasibility and effects of patient-cooperative robot-aided gait training applied in a 4-week pilot trial.

Authors:  Alex Schück; Rob Labruyère; Heike Vallery; Robert Riener; Alexander Duschau-Wicke
Journal:  J Neuroeng Rehabil       Date:  2012-05-31       Impact factor: 4.262

Review 7.  Robot-supported assessment of balance in standing and walking.

Authors:  Camila Shirota; Edwin van Asseldonk; Zlatko Matjačić; Heike Vallery; Pierre Barralon; Serena Maggioni; Jaap H Buurke; Jan F Veneman
Journal:  J Neuroeng Rehabil       Date:  2017-08-14       Impact factor: 4.262

8.  Haptic Error Modulation Outperforms Visual Error Amplification When Learning a Modified Gait Pattern.

Authors:  Laura Marchal-Crespo; Panagiotis Tsangaridis; David Obwegeser; Serena Maggioni; Robert Riener
Journal:  Front Neurosci       Date:  2019-02-19       Impact factor: 4.677

Review 9.  Review of control strategies for robotic movement training after neurologic injury.

Authors:  Laura Marchal-Crespo; David J Reinkensmeyer
Journal:  J Neuroeng Rehabil       Date:  2009-06-16       Impact factor: 4.262

10.  Abnormal joint torque patterns exhibited by chronic stroke subjects while walking with a prescribed physiological gait pattern.

Authors:  Nathan D Neckel; Natalie Blonien; Diane Nichols; Joseph Hidler
Journal:  J Neuroeng Rehabil       Date:  2008-09-01       Impact factor: 4.262

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