Reflex control of robotic gait using human walking data.

Control of human walking is not thoroughly understood, which has implications in developing suitable strategies for the retraining of a functional gait following neurological injuries such as spinal cord injury (SCI). Bipedal robots allow us to investigate simple elements of the complex nervous syst...

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Autores principales: Catherine A Macleod, Lin Meng, Bernard A Conway, Bernd Porr
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Publicado: Public Library of Science (PLoS) 2014
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Acceso en línea:https://doaj.org/article/e92f2c31f83648b5b237d215198269a5
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spelling oai:doaj.org-article:e92f2c31f83648b5b237d215198269a52021-11-25T05:55:15ZReflex control of robotic gait using human walking data.1932-620310.1371/journal.pone.0109959https://doaj.org/article/e92f2c31f83648b5b237d215198269a52014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0109959https://doaj.org/toc/1932-6203Control of human walking is not thoroughly understood, which has implications in developing suitable strategies for the retraining of a functional gait following neurological injuries such as spinal cord injury (SCI). Bipedal robots allow us to investigate simple elements of the complex nervous system to quantify their contribution to motor control. RunBot is a bipedal robot which operates through reflexes without using central pattern generators or trajectory planning algorithms. Ground contact information from the feet is used to activate motors in the legs, generating a gait cycle visually similar to that of humans. Rather than developing a more complicated biologically realistic neural system to control the robot's stepping, we have instead further simplified our model by measuring the correlation between heel contact and leg muscle activity (EMG) in human subjects during walking and from this data created filter functions transferring the sensory data into motor actions. Adaptive filtering was used to identify the unknown transfer functions which translate the contact information into muscle activation signals. Our results show a causal relationship between ground contact information from the heel and EMG, which allows us to create a minimal, linear, analogue control system for controlling walking. The derived transfer functions were applied to RunBot II as a proof of concept. The gait cycle produced was stable and controlled, which is a positive indication that the transfer functions have potential for use in the control of assistive devices for the retraining of an efficient and effective gait with potential applications in SCI rehabilitation.Catherine A MacleodLin MengBernard A ConwayBernd PorrPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 10, p e109959 (2014)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Catherine A Macleod
Lin Meng
Bernard A Conway
Bernd Porr
Reflex control of robotic gait using human walking data.
description Control of human walking is not thoroughly understood, which has implications in developing suitable strategies for the retraining of a functional gait following neurological injuries such as spinal cord injury (SCI). Bipedal robots allow us to investigate simple elements of the complex nervous system to quantify their contribution to motor control. RunBot is a bipedal robot which operates through reflexes without using central pattern generators or trajectory planning algorithms. Ground contact information from the feet is used to activate motors in the legs, generating a gait cycle visually similar to that of humans. Rather than developing a more complicated biologically realistic neural system to control the robot's stepping, we have instead further simplified our model by measuring the correlation between heel contact and leg muscle activity (EMG) in human subjects during walking and from this data created filter functions transferring the sensory data into motor actions. Adaptive filtering was used to identify the unknown transfer functions which translate the contact information into muscle activation signals. Our results show a causal relationship between ground contact information from the heel and EMG, which allows us to create a minimal, linear, analogue control system for controlling walking. The derived transfer functions were applied to RunBot II as a proof of concept. The gait cycle produced was stable and controlled, which is a positive indication that the transfer functions have potential for use in the control of assistive devices for the retraining of an efficient and effective gait with potential applications in SCI rehabilitation.
format article
author Catherine A Macleod
Lin Meng
Bernard A Conway
Bernd Porr
author_facet Catherine A Macleod
Lin Meng
Bernard A Conway
Bernd Porr
author_sort Catherine A Macleod
title Reflex control of robotic gait using human walking data.
title_short Reflex control of robotic gait using human walking data.
title_full Reflex control of robotic gait using human walking data.
title_fullStr Reflex control of robotic gait using human walking data.
title_full_unstemmed Reflex control of robotic gait using human walking data.
title_sort reflex control of robotic gait using human walking data.
publisher Public Library of Science (PLoS)
publishDate 2014
url https://doaj.org/article/e92f2c31f83648b5b237d215198269a5
work_keys_str_mv AT catherineamacleod reflexcontrolofroboticgaitusinghumanwalkingdata
AT linmeng reflexcontrolofroboticgaitusinghumanwalkingdata
AT bernardaconway reflexcontrolofroboticgaitusinghumanwalkingdata
AT berndporr reflexcontrolofroboticgaitusinghumanwalkingdata
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