Myoelectric control can significantly improve human–robot interaction and intensive research has worked on the attempt of providing the user with intuitive control of multiple Degrees of Freedom (DOFs). However, no work has focused on patients with severe dyskinetic cerebral palsy (CP) who are unable to achieve effective voluntary movements. Research aimed at developing intuitive and flexible control interface strategies has the potential to provide these patients with significantly improved mobility. Indeed, in CP patients, there is no disconnect between the brain and the spinal cord, so that the electromyographic (EMG) signal provides a direct read-out of the movement-related activity in motor cortex. On the other hand, a major obstacle to the use of myoelectric control in patients with CP and arm dystonia is that the EMG signal is corrupted by co-contraction, variability, and noise. To address this problem, a synergy-based myoelectric approach should be tested. Indeed, when extracting synergies from multi-muscle EMG, a set of incoming EMG signals is converted into repeatable descriptors, while discarding irrelevant information, thus making muscle synergies more robust to possible noisy activity. In addition, previous studies showed that, although children with dystonia present aberrant EMG activity compared to control subjects, muscle synergies extracted from the two groups are very similar in terms of number and structure. In a previous work, we developed and successfully tested, on healthy subjects, a semi-supervised method to achieve online, simultaneous, continuous control of 2 DOFs of a robotic arm, using muscle synergies extracted from 8 upper limb muscles while performing reaching movements of the elbow and shoulder joints in the horizontal plane. Here, we tested this synergy-based myoelectric interface on 5 children with secondary dystonia due to CP. Our goal was to evaluate the feasibility and the efficacy of the synergy-based control method, compared to the muscle-pair method typically used in commercial applications, using EMG signals recorded during both unconstrained movements (Dynamic Condition) and isometric contractions (Isometric Condition). For the Dynamic Condition, the control performance was assessed by computing the Root-mean-square Error and the Pearson’s Correlation coefficient between the subject’s and the robot’s angles. For the Isometric Condition, we designed a graphical interface with a cursor that tracked the position of the robot’s end-effector and specific targets to be reached. The performance was evaluated using the time needed to accomplish the task and the number of targets reached. Results show that our method is able to provide online, simultaneous, and accurate control of 2 DOFs of a robotic arm in children with secondary dystonia due to CP. The current study is a first step toward application of synergy-based myocontrol for patients with dyskinetic CP and other disorders of the control of muscles.

Synergy-based myocontrol of multiple degrees of freedom in children with secondary dystonia

LUNARDINI, FRANCESCA;CASELLATO, CLAUDIA;PEDROCCHI, ALESSANDRA LAURA GIULIA
2016-01-01

Abstract

Myoelectric control can significantly improve human–robot interaction and intensive research has worked on the attempt of providing the user with intuitive control of multiple Degrees of Freedom (DOFs). However, no work has focused on patients with severe dyskinetic cerebral palsy (CP) who are unable to achieve effective voluntary movements. Research aimed at developing intuitive and flexible control interface strategies has the potential to provide these patients with significantly improved mobility. Indeed, in CP patients, there is no disconnect between the brain and the spinal cord, so that the electromyographic (EMG) signal provides a direct read-out of the movement-related activity in motor cortex. On the other hand, a major obstacle to the use of myoelectric control in patients with CP and arm dystonia is that the EMG signal is corrupted by co-contraction, variability, and noise. To address this problem, a synergy-based myoelectric approach should be tested. Indeed, when extracting synergies from multi-muscle EMG, a set of incoming EMG signals is converted into repeatable descriptors, while discarding irrelevant information, thus making muscle synergies more robust to possible noisy activity. In addition, previous studies showed that, although children with dystonia present aberrant EMG activity compared to control subjects, muscle synergies extracted from the two groups are very similar in terms of number and structure. In a previous work, we developed and successfully tested, on healthy subjects, a semi-supervised method to achieve online, simultaneous, continuous control of 2 DOFs of a robotic arm, using muscle synergies extracted from 8 upper limb muscles while performing reaching movements of the elbow and shoulder joints in the horizontal plane. Here, we tested this synergy-based myoelectric interface on 5 children with secondary dystonia due to CP. Our goal was to evaluate the feasibility and the efficacy of the synergy-based control method, compared to the muscle-pair method typically used in commercial applications, using EMG signals recorded during both unconstrained movements (Dynamic Condition) and isometric contractions (Isometric Condition). For the Dynamic Condition, the control performance was assessed by computing the Root-mean-square Error and the Pearson’s Correlation coefficient between the subject’s and the robot’s angles. For the Isometric Condition, we designed a graphical interface with a cursor that tracked the position of the robot’s end-effector and specific targets to be reached. The performance was evaluated using the time needed to accomplish the task and the number of targets reached. Results show that our method is able to provide online, simultaneous, and accurate control of 2 DOFs of a robotic arm in children with secondary dystonia due to CP. The current study is a first step toward application of synergy-based myocontrol for patients with dyskinetic CP and other disorders of the control of muscles.
2016
dystonia; muscle synergies; myocontrol; childhood dystonia
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/988464
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