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Klauer C, Schauer T, Reichenfelser W, Karner J, Zwicker S, Gandolla M, Ambrosini E, Ferrante S, Hack M, Jedlitschka A, Duschau-Wicke A, Gföhler M, Pedrocchi A. Feedback control of arm movements using Neuro-Muscular Electrical Stimulation (NMES) combined with a lockable, passive exoskeleton for gravity compensation. Front Neurosci 2014; 8:262. [PMID: 25228853 PMCID: PMC4151235 DOI: 10.3389/fnins.2014.00262] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2014] [Accepted: 08/04/2014] [Indexed: 11/25/2022] Open
Abstract
Within the European project MUNDUS, an assistive framework was developed for the support of arm and hand functions during daily life activities in severely impaired people. This contribution aims at designing a feedback control system for Neuro-Muscular Electrical Stimulation (NMES) to enable reaching functions in people with no residual voluntary control of the arm and shoulder due to high level spinal cord injury. NMES is applied to the deltoids and the biceps muscles and integrated with a three degrees of freedom (DoFs) passive exoskeleton, which partially compensates gravitational forces and allows to lock each DOF. The user is able to choose the target hand position and to trigger actions using an eyetracker system. The target position is selected by using the eyetracker and determined by a marker-based tracking system using Microsoft Kinect. A central controller, i.e., a finite state machine, issues a sequence of basic movement commands to the real-time arm controller. The NMES control algorithm sequentially controls each joint angle while locking the other DoFs. Daily activities, such as drinking, brushing hair, pushing an alarm button, etc., can be supported by the system. The robust and easily tunable control approach was evaluated with five healthy subjects during a drinking task. Subjects were asked to remain passive and to allow NMES to induce the movements. In all of them, the controller was able to perform the task, and a mean hand positioning error of less than five centimeters was achieved. The average total time duration for moving the hand from a rest position to a drinking cup, for moving the cup to the mouth and back, and for finally returning the arm to the rest position was 71 s.
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Affiliation(s)
- Christian Klauer
- Control Systems Group, Technische Universität Berlin Berlin, Germany
| | - Thomas Schauer
- Control Systems Group, Technische Universität Berlin Berlin, Germany
| | - Werner Reichenfelser
- Research Group for Machine Design and Rehabilitation, Vienna University of Technology Vienna, Austria
| | - Jakob Karner
- Research Group for Machine Design and Rehabilitation, Vienna University of Technology Vienna, Austria
| | | | - Marta Gandolla
- NeuroEngineering and Medical Robotics Laboratory, NearLab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano Milan, Italy
| | - Emilia Ambrosini
- NeuroEngineering and Medical Robotics Laboratory, NearLab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano Milan, Italy
| | - Simona Ferrante
- NeuroEngineering and Medical Robotics Laboratory, NearLab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano Milan, Italy
| | - Marco Hack
- Fraunhofer Institute for Experimental Software Engineering Kaiserslautern, Germany
| | - Andreas Jedlitschka
- Fraunhofer Institute for Experimental Software Engineering Kaiserslautern, Germany
| | | | - Margit Gföhler
- Research Group for Machine Design and Rehabilitation, Vienna University of Technology Vienna, Austria
| | - Alessandra Pedrocchi
- NeuroEngineering and Medical Robotics Laboratory, NearLab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano Milan, Italy
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