Motor modules in robot-aided walking
1 Pain Clinic, Center for Anesthesiology, Emergency and Intensive Care Medicine, University Hospital Göttingen, Göttingen, Germany
2 Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University, Von-Siebold-Str, 4,37075, Göttingen, Germany
3 Department Human Movement and Sport Sciences, University of Roma Foro Italico, Piazza Lauro De Bosis 6, Rome, 00196, Italy
4 Regionshospitalet Hammel Neurocenter, Aarhus University, Voldbyvej 15, 8450, Hammel, Denmark
5 Bioengineering Group, Spanish National Research Council, CSIC, Carretera Campo Real, Madrid, Spain
Journal of NeuroEngineering and Rehabilitation 2012, 9:76 doi:10.1186/1743-0003-9-76Published: 8 October 2012
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.
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%).
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.
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.