Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling

Abstract Background Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. Methods We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. Results We demonstrated patients’ control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. Conclusions Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Guillaume Durandau [verfasserIn] Dario Farina [verfasserIn] Guillermo Asín-Prieto [verfasserIn] Iris Dimbwadyo-Terrer [verfasserIn] Sergio Lerma-Lara [verfasserIn] Jose L. Pons [verfasserIn] Juan C. Moreno [verfasserIn] Massimo Sartori [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Electromyography EMG-driven modeling Neuromechanical modeling Neuromuscular injury Robotic exoskeleton

Übergeordnetes Werk:	In: Journal of NeuroEngineering and Rehabilitation - BMC, 2004, 16(2019), 1, Seite 18
Übergeordnetes Werk:	volume:16 ; year:2019 ; number:1 ; pages:18

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12984-019-0559-z

Katalog-ID:	DOAJ078493102

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520			\|a Abstract Background Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. Methods We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. Results We demonstrated patients’ control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. Conclusions Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots.
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allfields	10.1186/s12984-019-0559-z doi (DE-627)DOAJ078493102 (DE-599)DOAJ8681db6d940a479cbd51dc02e1d7abed DE-627 ger DE-627 rakwb eng RC321-571 Guillaume Durandau verfasserin aut Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. Methods We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. Results We demonstrated patients’ control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. Conclusions Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots. Electromyography EMG-driven modeling Neuromechanical modeling Neuromuscular injury Robotic exoskeleton Neurosciences. Biological psychiatry. Neuropsychiatry Dario Farina verfasserin aut Guillermo Asín-Prieto verfasserin aut Iris Dimbwadyo-Terrer verfasserin aut Sergio Lerma-Lara verfasserin aut Jose L. Pons verfasserin aut Juan C. Moreno verfasserin aut Massimo Sartori verfasserin aut In Journal of NeuroEngineering and Rehabilitation BMC, 2004 16(2019), 1, Seite 18 (DE-627)461907933 (DE-600)2164377-5 17430003 nnns volume:16 year:2019 number:1 pages:18 https://doi.org/10.1186/s12984-019-0559-z kostenfrei https://doaj.org/article/8681db6d940a479cbd51dc02e1d7abed kostenfrei http://link.springer.com/article/10.1186/s12984-019-0559-z kostenfrei https://doaj.org/toc/1743-0003 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2019 1 18
spelling	10.1186/s12984-019-0559-z doi (DE-627)DOAJ078493102 (DE-599)DOAJ8681db6d940a479cbd51dc02e1d7abed DE-627 ger DE-627 rakwb eng RC321-571 Guillaume Durandau verfasserin aut Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. Methods We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. Results We demonstrated patients’ control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. Conclusions Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots. Electromyography EMG-driven modeling Neuromechanical modeling Neuromuscular injury Robotic exoskeleton Neurosciences. Biological psychiatry. Neuropsychiatry Dario Farina verfasserin aut Guillermo Asín-Prieto verfasserin aut Iris Dimbwadyo-Terrer verfasserin aut Sergio Lerma-Lara verfasserin aut Jose L. Pons verfasserin aut Juan C. Moreno verfasserin aut Massimo Sartori verfasserin aut In Journal of NeuroEngineering and Rehabilitation BMC, 2004 16(2019), 1, Seite 18 (DE-627)461907933 (DE-600)2164377-5 17430003 nnns volume:16 year:2019 number:1 pages:18 https://doi.org/10.1186/s12984-019-0559-z kostenfrei https://doaj.org/article/8681db6d940a479cbd51dc02e1d7abed kostenfrei http://link.springer.com/article/10.1186/s12984-019-0559-z kostenfrei https://doaj.org/toc/1743-0003 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2019 1 18
allfields_unstemmed	10.1186/s12984-019-0559-z doi (DE-627)DOAJ078493102 (DE-599)DOAJ8681db6d940a479cbd51dc02e1d7abed DE-627 ger DE-627 rakwb eng RC321-571 Guillaume Durandau verfasserin aut Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. Methods We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. Results We demonstrated patients’ control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. Conclusions Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots. Electromyography EMG-driven modeling Neuromechanical modeling Neuromuscular injury Robotic exoskeleton Neurosciences. Biological psychiatry. Neuropsychiatry Dario Farina verfasserin aut Guillermo Asín-Prieto verfasserin aut Iris Dimbwadyo-Terrer verfasserin aut Sergio Lerma-Lara verfasserin aut Jose L. Pons verfasserin aut Juan C. Moreno verfasserin aut Massimo Sartori verfasserin aut In Journal of NeuroEngineering and Rehabilitation BMC, 2004 16(2019), 1, Seite 18 (DE-627)461907933 (DE-600)2164377-5 17430003 nnns volume:16 year:2019 number:1 pages:18 https://doi.org/10.1186/s12984-019-0559-z kostenfrei https://doaj.org/article/8681db6d940a479cbd51dc02e1d7abed kostenfrei http://link.springer.com/article/10.1186/s12984-019-0559-z kostenfrei https://doaj.org/toc/1743-0003 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2019 1 18
allfieldsGer	10.1186/s12984-019-0559-z doi (DE-627)DOAJ078493102 (DE-599)DOAJ8681db6d940a479cbd51dc02e1d7abed DE-627 ger DE-627 rakwb eng RC321-571 Guillaume Durandau verfasserin aut Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. Methods We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. Results We demonstrated patients’ control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. Conclusions Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots. Electromyography EMG-driven modeling Neuromechanical modeling Neuromuscular injury Robotic exoskeleton Neurosciences. Biological psychiatry. Neuropsychiatry Dario Farina verfasserin aut Guillermo Asín-Prieto verfasserin aut Iris Dimbwadyo-Terrer verfasserin aut Sergio Lerma-Lara verfasserin aut Jose L. Pons verfasserin aut Juan C. Moreno verfasserin aut Massimo Sartori verfasserin aut In Journal of NeuroEngineering and Rehabilitation BMC, 2004 16(2019), 1, Seite 18 (DE-627)461907933 (DE-600)2164377-5 17430003 nnns volume:16 year:2019 number:1 pages:18 https://doi.org/10.1186/s12984-019-0559-z kostenfrei https://doaj.org/article/8681db6d940a479cbd51dc02e1d7abed kostenfrei http://link.springer.com/article/10.1186/s12984-019-0559-z kostenfrei https://doaj.org/toc/1743-0003 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2019 1 18
allfieldsSound	10.1186/s12984-019-0559-z doi (DE-627)DOAJ078493102 (DE-599)DOAJ8681db6d940a479cbd51dc02e1d7abed DE-627 ger DE-627 rakwb eng RC321-571 Guillaume Durandau verfasserin aut Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. Methods We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. Results We demonstrated patients’ control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. Conclusions Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots. Electromyography EMG-driven modeling Neuromechanical modeling Neuromuscular injury Robotic exoskeleton Neurosciences. Biological psychiatry. Neuropsychiatry Dario Farina verfasserin aut Guillermo Asín-Prieto verfasserin aut Iris Dimbwadyo-Terrer verfasserin aut Sergio Lerma-Lara verfasserin aut Jose L. Pons verfasserin aut Juan C. Moreno verfasserin aut Massimo Sartori verfasserin aut In Journal of NeuroEngineering and Rehabilitation BMC, 2004 16(2019), 1, Seite 18 (DE-627)461907933 (DE-600)2164377-5 17430003 nnns volume:16 year:2019 number:1 pages:18 https://doi.org/10.1186/s12984-019-0559-z kostenfrei https://doaj.org/article/8681db6d940a479cbd51dc02e1d7abed kostenfrei http://link.springer.com/article/10.1186/s12984-019-0559-z kostenfrei https://doaj.org/toc/1743-0003 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2019 1 18
language	English
source	In Journal of NeuroEngineering and Rehabilitation 16(2019), 1, Seite 18 volume:16 year:2019 number:1 pages:18
sourceStr	In Journal of NeuroEngineering and Rehabilitation 16(2019), 1, Seite 18 volume:16 year:2019 number:1 pages:18
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Electromyography EMG-driven modeling Neuromechanical modeling Neuromuscular injury Robotic exoskeleton Neurosciences. Biological psychiatry. Neuropsychiatry
isfreeaccess_bool	true
container_title	Journal of NeuroEngineering and Rehabilitation
authorswithroles_txt_mv	Guillaume Durandau @@aut@@ Dario Farina @@aut@@ Guillermo Asín-Prieto @@aut@@ Iris Dimbwadyo-Terrer @@aut@@ Sergio Lerma-Lara @@aut@@ Jose L. Pons @@aut@@ Juan C. Moreno @@aut@@ Massimo Sartori @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	461907933
id	DOAJ078493102
language_de	englisch
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Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. Conclusions Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. 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callnumber-first	R - Medicine
author	Guillaume Durandau
spellingShingle	Guillaume Durandau misc RC321-571 misc Electromyography misc EMG-driven modeling misc Neuromechanical modeling misc Neuromuscular injury misc Robotic exoskeleton misc Neurosciences. Biological psychiatry. Neuropsychiatry Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling
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topic_title	RC321-571 Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling Electromyography EMG-driven modeling Neuromechanical modeling Neuromuscular injury Robotic exoskeleton
topic	misc RC321-571 misc Electromyography misc EMG-driven modeling misc Neuromechanical modeling misc Neuromuscular injury misc Robotic exoskeleton misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc Electromyography misc EMG-driven modeling misc Neuromechanical modeling misc Neuromuscular injury misc Robotic exoskeleton misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling
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title_full	Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling
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author_browse	Guillaume Durandau Dario Farina Guillermo Asín-Prieto Iris Dimbwadyo-Terrer Sergio Lerma-Lara Jose L. Pons Juan C. Moreno Massimo Sartori
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title_sort	voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling
callnumber	RC321-571
title_auth	Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling
abstract	Abstract Background Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. Methods We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. Results We demonstrated patients’ control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. Conclusions Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots.
abstractGer	Abstract Background Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. Methods We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. Results We demonstrated patients’ control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. Conclusions Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots.
abstract_unstemmed	Abstract Background Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. Methods We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. Results We demonstrated patients’ control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. Conclusions Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots.
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title_short	Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling
url	https://doi.org/10.1186/s12984-019-0559-z https://doaj.org/article/8681db6d940a479cbd51dc02e1d7abed http://link.springer.com/article/10.1186/s12984-019-0559-z https://doaj.org/toc/1743-0003
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