HapticV1, PascalFrancis, Corpus, bibRecord, 000465

Training multi-parameter gaits to reduce the knee adduction moment with data-driven models and haptic feedback

Identifieur interne : 000465 ( PascalFrancis/Corpus ); précédent : 000464; suivant : 000466

Training multi-parameter gaits to reduce the knee adduction moment with data-driven models and haptic feedback

Auteurs : Pete B. Shull ; Kristen L. Lurie ; Mark R. Cutkosky ; Thor F. Besier

Source :

Journal of biomechanics [ 0021-9290 ] ; 2011.

RBID : Pascal:11-0357352

Descripteurs français

Pascal (Inist)
- Arthrose, Cinématique, Allure, Genou, Moment, Biofeedback, Marche à pied, Rééducation, Homme, Réadaptation, Exercice physique, Génie biomédical, Biomécanique, Adduction.

English descriptors

KwdEn :
- Biofeedback, Biomechanics, Biomedical engineering, Gait, Human, Kinematics, Knee, Moment, Osteoarthritis, Physical exercise, Reeducation, Rehabilitation(human), Walking.

Abstract

The purpose of this study was to evaluate gait retraining for reducing the knee adduction moment. Our primary objective was to determine whether subject-specific altered gaits aimed at reducing the knee adduction moment by 30% or more could be identified and adopted in a single session through haptic (touch) feedback training on multiple kinematic gait parameters. Nine healthy subjects performed gait retraining, in which data-driven models specific to each subject were determined through experimental trials and were used to train novel gaits involving a combination of kinematic changes to the tibia angle, foot progression and trunk sway angles. Wearable haptic devices were used on the back, knee and foot for real-time feedback. All subjects were able to adopt altered gaits requiring simultaneous changes to multiple kinematic parameters and reduced their knee adduction moments by 29-48%. Analysis of single parameter gait training showed that moving the knee medially by increasing tibia angle, increasing trunk sway and toeing in all reduced the first peak of the knee adduction moment with tibia angle changes having the most dramatic effect. These results suggest that individualized data-driven gait retraining may be a viable option for reducing the knee adduction moment as a treatment method for early-stage knee osteoarthritis patients with sufficient sensation, endurance and motor learning capabilities.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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C07	`04`	`X`	`SPA`	`@0 Locomoción @5 40`
N21				`@1 241`

Format Inist (serveur)

NO :	PASCAL 11-0357352 INIST
ET :	Training multi-parameter gaits to reduce the knee adduction moment with data-driven models and haptic feedback
AU :	SHULL (Pete B.); LURIE (Kristen L.); CUTKOSKY (Mark R.); BESIER (Thor F.)
AF :	Department of Mechanical Engineering, Stanford University, Center for Design Research/Stanford, CA 94305-2232/Etats-Unis (1 aut., 3 aut.); Department of Electrical Engineering, Stanford University/Stanford, CA/Etats-Unis (2 aut.); Department of Orthopaedic Surgery, Stanford University Medical Center/Stanford, CA/Etats-Unis (4 aut.)
DT :	Publication en série; Courte communication, note brève; Niveau analytique
SO :	Journal of biomechanics; ISSN 0021-9290; Royaume-Uni; Da. 2011; Vol. 44; No. 8; Pp. 1605-1609; Bibl. 3/4 p.
LA :	Anglais
EA :	The purpose of this study was to evaluate gait retraining for reducing the knee adduction moment. Our primary objective was to determine whether subject-specific altered gaits aimed at reducing the knee adduction moment by 30% or more could be identified and adopted in a single session through haptic (touch) feedback training on multiple kinematic gait parameters. Nine healthy subjects performed gait retraining, in which data-driven models specific to each subject were determined through experimental trials and were used to train novel gaits involving a combination of kinematic changes to the tibia angle, foot progression and trunk sway angles. Wearable haptic devices were used on the back, knee and foot for real-time feedback. All subjects were able to adopt altered gaits requiring simultaneous changes to multiple kinematic parameters and reduced their knee adduction moments by 29-48%. Analysis of single parameter gait training showed that moving the knee medially by increasing tibia angle, increasing trunk sway and toeing in all reduced the first peak of the knee adduction moment with tibia angle changes having the most dramatic effect. These results suggest that individualized data-driven gait retraining may be a viable option for reducing the knee adduction moment as a treatment method for early-stage knee osteoarthritis patients with sufficient sensation, endurance and motor learning capabilities.
CC :	002B26O; 002B15E
FD :	Arthrose; Cinématique; Allure; Genou; Moment; Biofeedback; Marche à pied; Rééducation; Homme; Réadaptation; Exercice physique; Génie biomédical; Biomécanique; Adduction
FG :	Pathologie du système ostéoarticulaire; Arthropathie; Maladie dégénérative; Locomotion
ED :	Osteoarthritis; Kinematics; Gait; Knee; Moment; Biofeedback; Walking; Reeducation; Human; Rehabilitation(human); Physical exercise; Biomedical engineering; Biomechanics
EG :	Diseases of the osteoarticular system; Arthropathy; Degenerative disease; Locomotion
SD :	Artrosis; Cinemática; Marcha; Rodilla; Momento; Biofeedback; Caminata; Reeducación; Hombre; Readaptación; Ejercicio físico; Ingeniería biomédica; Biomecánica
LO :	INIST-14022.354000191592770260
ID :	11-0357352

Links to Exploration step

Pascal:11-0357352

Le document en format XML

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<front><div type="abstract" xml:lang="en">The purpose of this study was to evaluate gait retraining for reducing the knee adduction moment. Our primary objective was to determine whether subject-specific altered gaits aimed at reducing the knee adduction moment by 30% or more could be identified and adopted in a single session through haptic (touch) feedback training on multiple kinematic gait parameters. Nine healthy subjects performed gait retraining, in which data-driven models specific to each subject were determined through experimental trials and were used to train novel gaits involving a combination of kinematic changes to the tibia angle, foot progression and trunk sway angles. Wearable haptic devices were used on the back, knee and foot for real-time feedback. All subjects were able to adopt altered gaits requiring simultaneous changes to multiple kinematic parameters and reduced their knee adduction moments by 29-48%. Analysis of single parameter gait training showed that moving the knee medially by increasing tibia angle, increasing trunk sway and toeing in all reduced the first peak of the knee adduction moment with tibia angle changes having the most dramatic effect. These results suggest that individualized data-driven gait retraining may be a viable option for reducing the knee adduction moment as a treatment method for early-stage knee osteoarthritis patients with sufficient sensation, endurance and motor learning capabilities.</div>
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<server><NO>PASCAL 11-0357352 INIST</NO>
<ET>Training multi-parameter gaits to reduce the knee adduction moment with data-driven models and haptic feedback</ET>
<AU>SHULL (Pete B.); LURIE (Kristen L.); CUTKOSKY (Mark R.); BESIER (Thor F.)</AU>
<AF>Department of Mechanical Engineering, Stanford University, Center for Design Research/Stanford, CA 94305-2232/Etats-Unis (1 aut., 3 aut.); Department of Electrical Engineering, Stanford University/Stanford, CA/Etats-Unis (2 aut.); Department of Orthopaedic Surgery, Stanford University Medical Center/Stanford, CA/Etats-Unis (4 aut.)</AF>
<DT>Publication en série; Courte communication, note brève; Niveau analytique</DT>
<SO>Journal of biomechanics; ISSN 0021-9290; Royaume-Uni; Da. 2011; Vol. 44; No. 8; Pp. 1605-1609; Bibl. 3/4 p.</SO>
<LA>Anglais</LA>
<EA>The purpose of this study was to evaluate gait retraining for reducing the knee adduction moment. Our primary objective was to determine whether subject-specific altered gaits aimed at reducing the knee adduction moment by 30% or more could be identified and adopted in a single session through haptic (touch) feedback training on multiple kinematic gait parameters. Nine healthy subjects performed gait retraining, in which data-driven models specific to each subject were determined through experimental trials and were used to train novel gaits involving a combination of kinematic changes to the tibia angle, foot progression and trunk sway angles. Wearable haptic devices were used on the back, knee and foot for real-time feedback. All subjects were able to adopt altered gaits requiring simultaneous changes to multiple kinematic parameters and reduced their knee adduction moments by 29-48%. Analysis of single parameter gait training showed that moving the knee medially by increasing tibia angle, increasing trunk sway and toeing in all reduced the first peak of the knee adduction moment with tibia angle changes having the most dramatic effect. These results suggest that individualized data-driven gait retraining may be a viable option for reducing the knee adduction moment as a treatment method for early-stage knee osteoarthritis patients with sufficient sensation, endurance and motor learning capabilities.</EA>
<CC>002B26O; 002B15E</CC>
<FD>Arthrose; Cinématique; Allure; Genou; Moment; Biofeedback; Marche à pied; Rééducation; Homme; Réadaptation; Exercice physique; Génie biomédical; Biomécanique; Adduction</FD>
<FG>Pathologie du système ostéoarticulaire; Arthropathie; Maladie dégénérative; Locomotion</FG>
<ED>Osteoarthritis; Kinematics; Gait; Knee; Moment; Biofeedback; Walking; Reeducation; Human; Rehabilitation(human); Physical exercise; Biomedical engineering; Biomechanics</ED>
<EG>Diseases of the osteoarticular system; Arthropathy; Degenerative disease; Locomotion</EG>
<SD>Artrosis; Cinemática; Marcha; Rodilla; Momento; Biofeedback; Caminata; Reeducación; Hombre; Readaptación; Ejercicio físico; Ingeniería biomédica; Biomecánica</SD>
<LO>INIST-14022.354000191592770260</LO>
<ID>11-0357352</ID>
</server>
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Training multi-parameter gaits to reduce the knee adduction moment with data-driven models and haptic feedback

Training multi-parameter gaits to reduce the knee adduction moment with data-driven models and haptic feedback

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