Literature DB >> 25079449

Engineering platform and experimental protocol for design and evaluation of a neurally-controlled powered transfemoral prosthesis.

Fan Zhang1, Ming Liu1, Stephen Harper2, Michael Lee3, He Huang4.   

Abstract

To enable intuitive operation of powered artificial legs, an interface between user and prosthesis that can recognize the user's movement intent is desired. A novel neural-machine interface (NMI) based on neuromuscular-mechanical fusion developed in our previous study has demonstrated a great potential to accurately identify the intended movement of transfemoral amputees. However, this interface has not yet been integrated with a powered prosthetic leg for true neural control. This study aimed to report (1) a flexible platform to implement and optimize neural control of powered lower limb prosthesis and (2) an experimental setup and protocol to evaluate neural prosthesis control on patients with lower limb amputations. First a platform based on a PC and a visual programming environment were developed to implement the prosthesis control algorithms, including NMI training algorithm, NMI online testing algorithm, and intrinsic control algorithm. To demonstrate the function of this platform, in this study the NMI based on neuromuscular-mechanical fusion was hierarchically integrated with intrinsic control of a prototypical transfemoral prosthesis. One patient with a unilateral transfemoral amputation was recruited to evaluate our implemented neural controller when performing activities, such as standing, level-ground walking, ramp ascent, and ramp descent continuously in the laboratory. A novel experimental setup and protocol were developed in order to test the new prosthesis control safely and efficiently. The presented proof-of-concept platform and experimental setup and protocol could aid the future development and application of neurally-controlled powered artificial legs.

Entities:  

Mesh:

Year:  2014        PMID: 25079449      PMCID: PMC4318637          DOI: 10.3791/51059

Source DB:  PubMed          Journal:  J Vis Exp        ISSN: 1940-087X            Impact factor:   1.355


  9 in total

1.  Volitional control of a prosthetic knee using surface electromyography.

Authors:  Kevin H Ha; Huseyin Atakan Varol; Michael Goldfarb
Journal:  IEEE Trans Biomed Eng       Date:  2010-08-30       Impact factor: 4.538

2.  Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits.

Authors:  Samuel Au; Max Berniker; Hugh Herr
Journal:  Neural Netw       Date:  2008-04-26

3.  Agonist-antagonist active knee prosthesis: a preliminary study in level-ground walking.

Authors:  Ernesto C Martinez-Villalpando; Hugh Herr
Journal:  J Rehabil Res Dev       Date:  2009

4.  Design and Control of a Powered Transfemoral Prosthesis.

Authors:  Frank Sup; Amit Bohara; Michael Goldfarb
Journal:  Int J Rob Res       Date:  2008-02-01       Impact factor: 4.703

5.  Real-time myoelectric control of knee and ankle motions for transfemoral amputees.

Authors:  Levi J Hargrove; Ann M Simon; Robert D Lipschutz; Suzanne B Finucane; Todd A Kuiken
Journal:  JAMA       Date:  2011-04-20       Impact factor: 56.272

6.  Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion.

Authors:  He Huang; Fan Zhang; Levi J Hargrove; Zhi Dou; Daniel R Rogers; Kevin B Englehart
Journal:  IEEE Trans Biomed Eng       Date:  2011-07-14       Impact factor: 4.538

7.  Real-time implementation of an intent recognition system for artificial legs.

Authors:  Fan Zhang; Zhi Dou; Michael Nunnery; He Huang
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2011

8.  Source selection for real-time user intent recognition toward volitional control of artificial legs.

Authors: 
Journal:  IEEE J Biomed Health Inform       Date:  2013-09       Impact factor: 5.772

9.  A strategy for identifying locomotion modes using surface electromyography.

Authors:  He Huang; Todd A Kuiken; Robert D Lipschutz
Journal:  IEEE Trans Biomed Eng       Date:  2009-01       Impact factor: 4.538

  9 in total
  5 in total

1.  Investigation of Timing to Switch Control Mode in Powered Knee Prostheses during Task Transitions.

Authors:  Fan Zhang; Ming Liu; He Huang
Journal:  PLoS One       Date:  2015-07-21       Impact factor: 3.240

2.  Detection of Gait Modes Using an Artificial Neural Network during Walking with a Powered Ankle-Foot Orthosis.

Authors:  Mazharul Islam; Elizabeth T Hsiao-Wecksler
Journal:  J Biophys       Date:  2016-12-13

Review 3.  Relying on more sense for enhancing lower limb prostheses control: a review.

Authors:  Michael Tschiedel; Michael Friedrich Russold; Eugenijus Kaniusas
Journal:  J Neuroeng Rehabil       Date:  2020-07-17       Impact factor: 4.262

4.  An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition.

Authors:  Ming Liu; Fan Zhang; He Helen Huang
Journal:  Sensors (Basel)       Date:  2017-09-04       Impact factor: 3.576

Review 5.  Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions.

Authors:  Aaron Fleming; Nicole Stafford; Stephanie Huang; Xiaogang Hu; Daniel P Ferris; He Helen Huang
Journal:  J Neural Eng       Date:  2021-07-27       Impact factor: 5.379

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.