Literature DB >> 29109981

Variable Damping Force Tunnel for Gait Training Using ALEX III.

Paul Stegall1, Damiano Zanotto2, Sunil K Agrawal1.   

Abstract

Haptic feedback affects not only the quality of training but can also influence the physical design of robotic gait trainers by determining how much force needs to be applied to the user and the nature of the force. This paper presents the design of a variable damping force tunnel and explores the effect of the shape and strength of the damping field using ALEX III, a treadmill-based exoskeleton developed at Columbia University. The study consists of 32 healthy subjects who were trained for 40 minutes in the device. The subjects were trained to follow a footpath with a 50% increase in step height, so the foot would have 1.5 times the ground clearance. Subjects were assigned to one of four groups: linear high, linear low, parabolic high, and parabolic low. Linear or parabolic denotes the shape of the damping field, and high or low denotes the rate of change (strength) of the field based on error. It is shown that the new controller is capable of inducing gait adaptations in healthy individuals while walking in the device. All groups showed adaptations in step height, while only the high strength groups showed changes in normalized error area, a measure of how closely the desired path was followed.

Entities:  

Keywords:  Haptics and Haptic Interfaces; Prosthetics and Exoskeletons; Rehabilitation Robotics

Year:  2017        PMID: 29109981      PMCID: PMC5668690          DOI: 10.1109/LRA.2017.2671374

Source DB:  PubMed          Journal:  IEEE Robot Autom Lett


  35 in total

1.  Augmented dynamics and motor exploration as training for stroke.

Authors:  Felix C Huang; James L Patton
Journal:  IEEE Trans Biomed Eng       Date:  2012-04-03       Impact factor: 4.538

2.  Repetitive locomotor training and physiotherapy improve walking and basic activities of daily living after stroke: a single-blind, randomized multicentre trial (DEutsche GAngtrainerStudie, DEGAS).

Authors:  M Pohl; C Werner; M Holzgraefe; G Kroczek; J Mehrholz; I Wingendorf; G Hoölig; R Koch; S Hesse
Journal:  Clin Rehabil       Date:  2007-01       Impact factor: 3.477

3.  Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification.

Authors:  Jeremy L Emken; David J Reinkensmeyer
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2005-03       Impact factor: 3.802

4.  Enhanced gait-related improvements after therapist- versus robotic-assisted locomotor training in subjects with chronic stroke: a randomized controlled study.

Authors:  T George Hornby; Donielle D Campbell; Jennifer H Kahn; Tobey Demott; Jennifer L Moore; Heidi R Roth
Journal:  Stroke       Date:  2008-05-08       Impact factor: 7.914

5.  Directed neural connectivity changes in robot-assisted gait training: a partial Granger causality analysis.

Authors:  Vahab Youssofzadeh; Damiano Zanotto; Paul Stegall; Muhammad Naeem; KongFatt Wong-Lin; Sunil K Agrawal; Girijesh Prasad
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2014

6.  Effects of complementary auditory feedback in robot-assisted lower extremity motor adaptation.

Authors:  Damiano Zanotto; Giulio Rosati; Simone Spagnol; Paul Stegall; Sunil K Agrawal
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2013-03-18       Impact factor: 3.802

7.  Robotic resistance treadmill training improves locomotor function in human spinal cord injury: a pilot study.

Authors:  Ming Wu; Jill M Landry; Brian D Schmit; T George Hornby; Sheng-Che Yen
Journal:  Arch Phys Med Rehabil       Date:  2012-03-27       Impact factor: 3.966

8.  Multicenter randomized clinical trial evaluating the effectiveness of the Lokomat in subacute stroke.

Authors:  Joseph Hidler; Diane Nichols; Marlena Pelliccio; Kathy Brady; Donielle D Campbell; Jennifer H Kahn; T George Hornby
Journal:  Neurorehabil Neural Repair       Date:  2009-01       Impact factor: 3.919

9.  Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: a randomized controlled pilot study.

Authors:  Leonard E Kahn; Michele L Zygman; W Zev Rymer; David J Reinkensmeyer
Journal:  J Neuroeng Rehabil       Date:  2006-06-21       Impact factor: 4.262

10.  Selective control of gait subtasks in robotic gait training: foot clearance support in stroke survivors with a powered exoskeleton.

Authors:  Bram Koopman; Edwin H F van Asseldonk; Herman van der Kooij
Journal:  J Neuroeng Rehabil       Date:  2013-01-21       Impact factor: 4.262

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  3 in total

Review 1.  A Review of Robot-Assisted Lower-Limb Stroke Therapy: Unexplored Paths and Future Directions in Gait Rehabilitation.

Authors:  Bradley Hobbs; Panagiotis Artemiadis
Journal:  Front Neurorobot       Date:  2020-04-15       Impact factor: 2.650

2.  ALICE: Conceptual Development of a Lower Limb Exoskeleton Robot Driven by an On-Board Musculoskeletal Simulator.

Authors:  Manuel Cardona; Cecilia E García Cena; Fernando Serrano; Roque Saltaren
Journal:  Sensors (Basel)       Date:  2020-01-31       Impact factor: 3.576

3.  Variable Impedance Control Based on Target Position and Tracking Error for Rehabilitation Robots During a Reaching Task.

Authors:  Rongrong Tang; Qianqian Yang; Rong Song
Journal:  Front Neurorobot       Date:  2022-03-03       Impact factor: 2.650

  3 in total

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