A Comparative Analysis of Speed Profile Models for Ankle Pointing Movements: Evidence that Lower and Upper Extremity Discrete Movements are Controlled by a Single Invariant Strategy
Little is known about whether our knowledge of how the central nervous system controls the upper extremities (UE), can generalize, and to what extent to the lower limbs. Our continuous efforts to design the ideal adaptive robotic therapy for the lower limbs of stroke patients and children with cereb...
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| Published in | Frontiers in human neuroscience Vol. 8; p. 962 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Frontiers Research Foundation
27.11.2014
Frontiers Media S.A |
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| Online Access | Get full text |
| ISSN | 1662-5161 1662-5161 |
| DOI | 10.3389/fnhum.2014.00962 |
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| Abstract | Little is known about whether our knowledge of how the central nervous system controls the upper extremities (UE), can generalize, and to what extent to the lower limbs. Our continuous efforts to design the ideal adaptive robotic therapy for the lower limbs of stroke patients and children with cerebral palsy highlighted the importance of analyzing and modeling the kinematics of the lower limbs, in general, and those of the ankle joints, in particular. We recruited 15 young healthy adults that performed in total 1,386 visually evoked, visually guided, and target-directed discrete pointing movements with their ankle in dorsal-plantar and inversion-eversion directions. Using a non-linear, least-squares error-minimization procedure, we estimated the parameters for 19 models, which were initially designed to capture the dynamics of upper limb movements of various complexity. We validated our models based on their ability to reconstruct the experimental data. Our results suggest a remarkable similarity between the top-performing models that described the speed profiles of ankle pointing movements and the ones previously found for the UE both during arm reaching and wrist pointing movements. Among the top performers were the support-bounded lognormal and the beta models that have a neurophysiological basis and have been successfully used in upper extremity studies with normal subjects and patients. Our findings suggest that the same model can be applied to different "human" hardware, perhaps revealing a key invariant in human motor control. These findings have a great potential to enhance our rehabilitation efforts in any population with lower extremity deficits by, for example, assessing the level of motor impairment and improvement as well as informing the design of control algorithms for therapeutic ankle robots. |
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| AbstractList | Little is known about whether our knowledge of how the central nervous system controls the upper extremities (UE), can generalize, and to what extent to the lower limbs. Our continuous efforts to design the ideal adaptive robotic therapy for the lower limbs of stroke patients and children with cerebral palsy highlighted the importance of analyzing and modeling the kinematics of the lower limbs, in general, and those of the ankle joints, in particular. We recruited 15 young healthy adults that performed in total 1,386 visually evoked, visually guided, and target-directed discrete pointing movements with their ankle in dorsal-plantar and inversion-eversion directions. Using a non-linear, least-squares error-minimization procedure, we estimated the parameters for 19 models, which were initially designed to capture the dynamics of upper limb movements of various complexity. We validated our models based on their ability to reconstruct the experimental data. Our results suggest a remarkable similarity between the top-performing models that described the speed profiles of ankle pointing movements and the ones previously found for the UE both during arm reaching and wrist pointing movements. Among the top performers were the support-bounded lognormal and the beta models that have a neurophysiological basis and have been successfully used in upper extremity studies with normal subjects and patients. Our findings suggest that the same model can be applied to different "human" hardware, perhaps revealing a key invariant in human motor control. These findings have a great potential to enhance our rehabilitation efforts in any population with lower extremity deficits by, for example, assessing the level of motor impairment and improvement as well as informing the design of control algorithms for therapeutic ankle robots. Little is known about whether our knowledge of how the central nervous system controls the upper extremities, can generalize, and to what extent to the lower limbs. Our continuous efforts to design the ideal adaptive robotic therapy for the lower limbs of stroke patients and children with cerebral palsy highlighted the importance of analyzing and modeling the kinematics of the lower limbs, in general, and those of the ankle joints, in particular. We recruited 15 young healthy adults that performed in total 1,386 visually-evoked, visually-guided and target-directed discrete pointing movements with their ankle in dorsal–plantar and inversion–eversion directions. Using a nonlinear, least-squares error-minimization procedure, we estimated the parameters for 19 models which were initially designed to capture the dynamics of upper limb movements of various complexity. We validated our models based on their ability to reconstruct the experimental data. Our results suggest a remarkable similarity between the top performing models that described the speed profiles of ankle pointing movements and the ones previously found for the upper extremities both during arm reaching and wrist pointing movements. Among the top performers were the support-bounded lognormal and the beta models that have a neurophysiological basis and have been successfully used in upper extremity studies with normal subjects and patients. Our findings suggest that the same model can be applied to different human hardware, perhaps revealing a key invariant in human motor control. These findings have a great potential to enhance our rehabilitation efforts in any population with lower extremity deficits by, for example, assessing the level of motor impairment and improvement as well as informing the design of control algorithms for therapeutic ankle robots. Little is known about whether our knowledge of how the central nervous system controls the upper extremities (UE), can generalize, and to what extent to the lower limbs. Our continuous efforts to design the ideal adaptive robotic therapy for the lower limbs of stroke patients and children with cerebral palsy highlighted the importance of analyzing and modeling the kinematics of the lower limbs, in general, and those of the ankle joints, in particular. We recruited 15 young healthy adults that performed in total 1,386 visually evoked, visually guided, and target-directed discrete pointing movements with their ankle in dorsal-plantar and inversion-eversion directions. Using a non-linear, least-squares error-minimization procedure, we estimated the parameters for 19 models, which were initially designed to capture the dynamics of upper limb movements of various complexity. We validated our models based on their ability to reconstruct the experimental data. Our results suggest a remarkable similarity between the top-performing models that described the speed profiles of ankle pointing movements and the ones previously found for the UE both during arm reaching and wrist pointing movements. Among the top performers were the support-bounded lognormal and the beta models that have a neurophysiological basis and have been successfully used in upper extremity studies with normal subjects and patients. Our findings suggest that the same model can be applied to different "human" hardware, perhaps revealing a key invariant in human motor control. These findings have a great potential to enhance our rehabilitation efforts in any population with lower extremity deficits by, for example, assessing the level of motor impairment and improvement as well as informing the design of control algorithms for therapeutic ankle robots.Little is known about whether our knowledge of how the central nervous system controls the upper extremities (UE), can generalize, and to what extent to the lower limbs. Our continuous efforts to design the ideal adaptive robotic therapy for the lower limbs of stroke patients and children with cerebral palsy highlighted the importance of analyzing and modeling the kinematics of the lower limbs, in general, and those of the ankle joints, in particular. We recruited 15 young healthy adults that performed in total 1,386 visually evoked, visually guided, and target-directed discrete pointing movements with their ankle in dorsal-plantar and inversion-eversion directions. Using a non-linear, least-squares error-minimization procedure, we estimated the parameters for 19 models, which were initially designed to capture the dynamics of upper limb movements of various complexity. We validated our models based on their ability to reconstruct the experimental data. Our results suggest a remarkable similarity between the top-performing models that described the speed profiles of ankle pointing movements and the ones previously found for the UE both during arm reaching and wrist pointing movements. Among the top performers were the support-bounded lognormal and the beta models that have a neurophysiological basis and have been successfully used in upper extremity studies with normal subjects and patients. Our findings suggest that the same model can be applied to different "human" hardware, perhaps revealing a key invariant in human motor control. These findings have a great potential to enhance our rehabilitation efforts in any population with lower extremity deficits by, for example, assessing the level of motor impairment and improvement as well as informing the design of control algorithms for therapeutic ankle robots. |
| Author | Michmizos, Konstantinos P. Krebs, Hermano Igo Vaisman, Lev |
| AuthorAffiliation | 5 Department of Neurology, Division of Rehabilitation, School of Medicine, University of Maryland , College Park, MD , USA 1 Martinos Center for Biomedical Imaging, Massachusetts Institute of Technology, Massachusetts General Hospital, Harvard Medical School , Charlestown, MA , USA 2 McGovern Institute for Brain Research, Massachusetts Institute of Technology , Cambridge, MA , USA 3 Department of Anatomy and Neurobiology, School of Medicine, Boston University , Boston, MA , USA 4 Department of Mechanical Engineering, Massachusetts Institute of Technology , Cambridge, MA , USA 6 Department of Physical Medicine and Rehabilitation, Fujita Health University , Nagoya , Japan 7 Institute of Neuroscience, University of Newcastle , Newcastle upon Tyne , UK |
| AuthorAffiliation_xml | – name: 1 Martinos Center for Biomedical Imaging, Massachusetts Institute of Technology, Massachusetts General Hospital, Harvard Medical School , Charlestown, MA , USA – name: 3 Department of Anatomy and Neurobiology, School of Medicine, Boston University , Boston, MA , USA – name: 6 Department of Physical Medicine and Rehabilitation, Fujita Health University , Nagoya , Japan – name: 7 Institute of Neuroscience, University of Newcastle , Newcastle upon Tyne , UK – name: 5 Department of Neurology, Division of Rehabilitation, School of Medicine, University of Maryland , College Park, MD , USA – name: 2 McGovern Institute for Brain Research, Massachusetts Institute of Technology , Cambridge, MA , USA – name: 4 Department of Mechanical Engineering, Massachusetts Institute of Technology , Cambridge, MA , USA |
| Author_xml | – sequence: 1 givenname: Konstantinos P. surname: Michmizos fullname: Michmizos, Konstantinos P. – sequence: 2 givenname: Lev surname: Vaisman fullname: Vaisman, Lev – sequence: 3 givenname: Hermano Igo surname: Krebs fullname: Krebs, Hermano Igo |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25505881$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1109_LRA_2018_2885165 crossref_primary_10_1007_s00221_015_4454_y crossref_primary_10_1186_s12984_021_00829_z crossref_primary_10_3390_robotics8040096 crossref_primary_10_1109_TNSRE_2015_2410773 crossref_primary_10_1186_s12984_023_01293_7 crossref_primary_10_1038_s41598_022_05079_0 |
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| Keywords | cerebral palsy rehabilitation robotics stroke neurorehabilitation of motor function sensorimotor control ankle movements |
| Language | English |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Reviewed by: Jascha Ruesseler, University of Bamberg, Germany; Domenico Formica, Università Campus Bio-Medico di Roma, Italy Edited by: Lorenzo Masia, Nanyang Technological University, Singapore This article was submitted to the journal Frontiers in Human Neuroscience. |
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| Snippet | Little is known about whether our knowledge of how the central nervous system controls the upper extremities (UE), can generalize, and to what extent to the... Little is known about whether our knowledge of how the central nervous system controls the upper extremities, can generalize, and to what extent to the lower... |
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| SubjectTerms | Ankle ankle movements Arm Central nervous system Cerebral Palsy Comparative analysis Fitness equipment Gait Kinematics Limbs Motor task performance neurorehabilitation of motor function Neuroscience Neurosciences Paralysis Posture Rehabilitation rehabilitation robotics Robotics Robots Sensorimotor control Stroke Walking Working groups Wrist |
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| Title | A Comparative Analysis of Speed Profile Models for Ankle Pointing Movements: Evidence that Lower and Upper Extremity Discrete Movements are Controlled by a Single Invariant Strategy |
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