Motor modules in robot-aided walking

Background It is hypothesized that locomotion is achieved by means of rhythm generating networks (central pattern generators) and muscle activation generating networks. This modular organization can be partly identified from the analysis of the muscular activity by means of factorization algorithms....

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Veröffentlicht in:Journal of neuroengineering and rehabilitation Jg. 9; H. 1; S. 76
Hauptverfasser: Gizzi, Leonardo, Nielsen, Jørgen Feldbæk, Felici, Francesco, Moreno, Juan C, Pons, José L, Farina, Dario
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 08.10.2012
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Adult
Algorithms
Analysis
Biomechanical Phenomena
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Body weight
Body Weight - physiology
Electromyography
Female
Gait Disorders, Neurologic - rehabilitation
Humans
Locomotion
Lower Extremity - innervation
Lower Extremity - physiology
Male
Motor control
Motor modules
Muscle, Skeletal - innervation
Muscle, Skeletal - physiology
Nerve Net - physiology
Neurology
Neurosciences
Physical Education and Training
Rehabilitation Medicine
Robotic gait trainer
Robotics - instrumentation
Robotics - methods
Robots
Signal Processing, Computer-Assisted
Walking - physiology
Motor modules
Robotic gait trainer
Motor control
ISSN:1743-0003, 1743-0003
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Zusammenfassung:Background It is hypothesized that locomotion is achieved by means of rhythm generating networks (central pattern generators) and muscle activation generating networks. This modular organization can be partly identified from the analysis of the muscular activity by means of factorization algorithms. The activity of rhythm generating networks is described by activation signals whilst the muscle intervention generating network is represented by motor modules (muscle synergies). In this study, we extend the analysis of modular organization of walking to the case of robot-aided locomotion, at varying speed and body weight support level. Methods Non Negative Matrix Factorization was applied on surface electromyographic signals of 8 lower limb muscles of healthy subjects walking in gait robotic trainer at different walking velocities (1 to 3km/h) and levels of body weight support (0 to 30%). Results The muscular activity of volunteers could be described by low dimensionality (4 modules), as for overground walking. Moreover, the activation signals during robot-aided walking were bursts of activation timed at specific phases of the gait cycle, underlying an impulsive controller, as also observed in overground walking. This modular organization was consistent across the investigated speeds, body weight support level, and subjects. Conclusions These results indicate that walking in a Lokomat robotic trainer is achieved by similar motor modules and activation signals as overground walking and thus supports the use of robotic training for re-establishing natural walking patterns.
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ISSN:1743-0003
1743-0003
DOI:10.1186/1743-0003-9-76