Experimental evaluation of a model-based assistance-as-needed paradigm using an assistive robot

In robotic rehabilitation a promising paradigm is assistance-as-needed. This is because it promotes patient active participation which is essential for neuro-rehabilitation. A model-based assistance-as-needed paradigm has been developed which utilizes a musculoskeletal model representing the subject to calculate their assistance needs. In this paper we experimentally evaluate this model-based paradigm to control an assistive robot and provide a subject with assistance-as-needed at the muscular level. A subject with impairments defined in specific muscle groups performs a number of upper limb tasks, whilst receiving assistance from a robotic exoskeleton. The paradigm is evaluated on its ability to provide assistance only as the subject needs, depending on the tasks being performed and the impairments defined. Results show that the model-based assistance-as-needed paradigm was relatively successful in providing assistance when it was needed.

[1]  Dikai Liu,et al.  Estimating Physical Assistance Need Using a Musculoskeletal Model , 2013, IEEE Transactions on Biomedical Engineering.

[2]  D.J. Reinkensmeyer,et al.  Real-time computer modeling of weakness following stroke optimizes robotic assistance for movement therapy , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[3]  D. Reinkensmeyer,et al.  Review of control strategies for robotic movement training after neurologic injury , 2009, Journal of NeuroEngineering and Rehabilitation.

[4]  Goro Obinata,et al.  Robot-aided rehabilitation task design for inner shoulder muscles , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Dikai Liu,et al.  A task description model for robotic rehabilitation , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Marcus G Pandy,et al.  Lines of action and stabilizing potential of the shoulder musculature , 2009, Journal of anatomy.

[7]  J Ueda,et al.  Individual Muscle Control Using an Exoskeleton Robot for Muscle Function Testing , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  D.J. Reinkensmeyer,et al.  Robotic movement training as an optimization problem: designing a controller that assists only as needed , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[9]  N. Hogan,et al.  Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery. , 2006, Journal of rehabilitation research and development.

[10]  P M Rozing,et al.  Glenohumeral stability in simulated rotator cuff tears. , 2009, Journal of biomechanics.

[11]  Dikai Liu,et al.  Towards using musculoskeletal models for intelligent control of physically assistive robots , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Michael L Boninger,et al.  Personalized neuromusculoskeletal modeling to improve treatment of mobility impairments: a perspective from European research sites , 2012, Journal of NeuroEngineering and Rehabilitation.

[13]  Hermano Igo Krebs,et al.  Rehabilitation Robotics: Performance-Based Progressive Robot-Assisted Therapy , 2003, Auton. Robots.

[14]  Scott L. Delp,et al.  A Model of the Upper Extremity for Simulating Musculoskeletal Surgery and Analyzing Neuromuscular Control , 2005, Annals of Biomedical Engineering.