Literature DB >> 32149646

Wearer-Prosthesis Interaction for Symmetrical Gait: A Study Enabled by Reinforcement Learning Prosthesis Control.

Yue Wen, Minhan Li, Jennie Si, He Huang.   

Abstract

With advances in robotic prostheses, rese-archers attempt to improve amputee's gait performance (e.g., gait symmetry) beyond restoring normative knee kinematics/kinetics. Yet, little is known about how the prosthesis mechanics/control influence wearer-prosthesis' gait performance, such as gait symmetry, stability, etc. This study aimed to investigate the influence of robotic transfemoral prosthesis mechanics on human wearers' gait symmetry. The investigation was enabled by our previously designed reinforcement learning (RL) supplementary control, which simultaneously tuned 12 control parameters that determined the prosthesis mechanics throughout a gait cycle. The RL control design facilitated safe explorations of prosthesis mechanics with the human in the loop. Subjects were recruited and walked with a robotic transfemoral prosthesis on a treadmill while the RL controller tuned the control parameters. Stance time symmetry, step length symmetry, and bilateral anteroposterior (AP) impulses were measured. The data analysis showed that changes in robotic knee mechanics led to movement variations in both lower limbs and therefore gait temporal-spatial symmetry measures. Consistent across all the subjects, inter-limb AP impulse measurements explained gait symmetry: the stance time symmetry was significantly correlated with the net inter-limb AP impulse, and the step length symmetry was significantly correlated with braking and propulsive impulse symmetry. The results suggest that it is possible to personalize transfemoral prosthesis control for improved temporal-spatial gait symmetry. However, adjusting prosthesis mechanics alone was insufficient to maximize the gait symmetry. Rather, achieving gait symmetry may require coordination between the wearer's motor control of the intact limb and adaptive control of the prosthetic joints. The results also indicated that the RL-based prosthesis tuning system was a potential tool for studying wearer-prosthesis interactions.

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Year:  2020        PMID: 32149646      PMCID: PMC7250159          DOI: 10.1109/TNSRE.2020.2979033

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


  38 in total

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Journal:  Ann Biomed Eng       Date:  2015-09-25       Impact factor: 3.934

Review 3.  Biomechanics and physiological parameters during gait in lower-limb amputees: a systematic review.

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Journal:  Gait Posture       Date:  2011-03-10       Impact factor: 2.840

4.  A New Powered Lower Limb Prosthesis Control Framework Based on Adaptive Dynamic Programming.

Authors:  Yue Wen; Jennie Si; Xiang Gao; Stephanie Huang; He Helen Huang
Journal:  IEEE Trans Neural Netw Learn Syst       Date:  2016-07-11       Impact factor: 10.451

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Authors:  Robert D Gregg; Jonathon W Sensinger
Journal:  IEEE Trans Control Syst Technol       Date:  2014-01       Impact factor: 5.485

6.  Temporal Spatial and Metabolic Measures of Walking in Highly Functional Individuals With Lower Limb Amputations.

Authors:  Hannah L Jarvis; Alex N Bennett; Martin Twiste; Rhodri D Phillip; John Etherington; Richard Baker
Journal:  Arch Phys Med Rehabil       Date:  2016-11-16       Impact factor: 3.966

7.  Human-in-the-loop optimization of exoskeleton assistance during walking.

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Journal:  Science       Date:  2017-06-23       Impact factor: 47.728

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Journal:  IEEE ASME Trans Mechatron       Date:  2009       Impact factor: 5.303

9.  Robotic lower limb prosthesis design through simultaneous computer optimizations of human and prosthesis costs.

Authors:  Matthew L Handford; Manoj Srinivasan
Journal:  Sci Rep       Date:  2016-02-09       Impact factor: 4.379

10.  Effects of extended powered knee prosthesis stance time via visual feedback on gait symmetry of individuals with unilateral amputation: a preliminary study.

Authors:  Andrea Brandt; William Riddick; Jonathan Stallrich; Michael Lewek; He Helen Huang
Journal:  J Neuroeng Rehabil       Date:  2019-09-11       Impact factor: 4.262

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  2 in total

1.  Foot contact forces can be used to personalize a wearable robot during human walking.

Authors:  Michael Jacobson; Prakyath Kantharaju; Hyeongkeun Jeong; Jae-Kwan Ryu; Jung-Jae Park; Hyun-Joon Chung; Myunghee Kim
Journal:  Sci Rep       Date:  2022-06-29       Impact factor: 4.996

2.  Visual guidance can help with the use of a robotic exoskeleton during human walking.

Authors:  Myunghee Kim; Hyeongkeun Jeong; Prakyath Kantharaju; Dongyual Yoo; Michael Jacobson; Dongbin Shin; Changsoo Han; James L Patton
Journal:  Sci Rep       Date:  2022-03-10       Impact factor: 4.379

  2 in total

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