TPress
1.
Hahne, J. M.; Wilke, M. A.; Koppe, M.; Farina, D.; Schilling, A. F.
Longitudinal Case Study of Regression-Based Hand Prosthesis Control in Daily Life Artikel
In: Front. Neurosci., Bd. 14, 2020, ISSN: 1662-4548.
Abstract | Links | Schlagwörter: adult, arm amputation, article, case study, clinical article, controlled study, electric hand, hand prosthesis, human, longitudinal study, male, middle aged, motor control, questionnaire, regression analysis, VariPlus Speed
@article{Hahne2020,
title = {Longitudinal Case Study of Regression-Based Hand Prosthesis Control in Daily Life},
author = {J. M. Hahne and M. A. Wilke and M. Koppe and D. Farina and A. F. Schilling},
url = {https://www.embase.com/search/results?subaction=viewrecord&id=L632229544&from=export},
doi = {10.3389/fnins.2020.00600},
issn = {1662-4548},
year = {2020},
date = {2020-01-01},
journal = {Front. Neurosci.},
volume = {14},
address = {J.M. Hahne, Applied Rehabilitation Technology Lab, Department of Trauma Surgery, Orthopedic Surgery and Hand Surgery, University Medical Center Göttingen, Göttingen, Germany},
abstract = {Hand prostheses are usually controlled by electromyographic (EMG) signals from the remnant muscles of the residual limb. Most prostheses used today are controlled with very simple techniques using only two EMG electrodes that allow to control a single prosthetic function at a time only. Recently, modern prosthesis controllers based on EMG classification, have become clinically available, which allow to directly access more functions, but still in a sequential manner only. We have recently shown in laboratory tests that a regression-based mapping from EMG signals into prosthetic control commands allows for a simultaneous activation of two functions and an independent control of their velocities with high reliability. Here we aimed to study how such regression-based control performs in daily life in a two-month case study. The performance is evaluated in functional tests and with a questionnaire at the beginning and the end of this phase and compared with the participant’s own prosthesis, controlled with a classical approach. Already 1 day after training of the regression model, the participant with transradial amputation outperformed the performance achieved with his own Michelangelo hand in two out of three functional metrics. No retraining of the model was required during the entire study duration. During the use of the system at home, the performance improved further and outperformed the conventional control in all three metrics. This study demonstrates that the high fidelity of linear regression-based prosthesis control is not restricted to a laboratory environment, but can be transferred to daily use.},
keywords = {adult, arm amputation, article, case study, clinical article, controlled study, electric hand, hand prosthesis, human, longitudinal study, male, middle aged, motor control, questionnaire, regression analysis, VariPlus Speed},
pubstate = {published},
tppubtype = {article}
}
Hand prostheses are usually controlled by electromyographic (EMG) signals from the remnant muscles of the residual limb. Most prostheses used today are controlled with very simple techniques using only two EMG electrodes that allow to control a single prosthetic function at a time only. Recently, modern prosthesis controllers based on EMG classification, have become clinically available, which allow to directly access more functions, but still in a sequential manner only. We have recently shown in laboratory tests that a regression-based mapping from EMG signals into prosthetic control commands allows for a simultaneous activation of two functions and an independent control of their velocities with high reliability. Here we aimed to study how such regression-based control performs in daily life in a two-month case study. The performance is evaluated in functional tests and with a questionnaire at the beginning and the end of this phase and compared with the participant’s own prosthesis, controlled with a classical approach. Already 1 day after training of the regression model, the participant with transradial amputation outperformed the performance achieved with his own Michelangelo hand in two out of three functional metrics. No retraining of the model was required during the entire study duration. During the use of the system at home, the performance improved further and outperformed the conventional control in all three metrics. This study demonstrates that the high fidelity of linear regression-based prosthesis control is not restricted to a laboratory environment, but can be transferred to daily use.
2.
Dosen, S.; Markovic, M.; Somer, K.; Graimann, B.; Farina, D.
EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis Artikel
In: J. NeuroEng. Rehabil., Bd. 12, Nr. 1, 2015, ISSN: 1743-0003.
Abstract | Links | Schlagwörter: adult, arm amputation, article, case report, computer interface, controlled study, EMG biofeedback, feedback system, grip strength, human, human computer interaction, Michelangelo hand, middle aged, muscle strength, myoelectric control, myoelectrically controlled prosthesis, online system, prediction, priority journal, Sensor Hand
@article{Dosen2015,
title = {EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis},
author = {S. Dosen and M. Markovic and K. Somer and B. Graimann and D. Farina},
url = {https://www.embase.com/search/results?subaction=viewrecord&id=L605002250&from=export},
doi = {10.1186/s12984-015-0047-z},
issn = {1743-0003},
year = {2015},
date = {2015-06-01},
journal = {J. NeuroEng. Rehabil.},
volume = {12},
number = {1},
publisher = {Springer Science and Business Media LLC},
address = {D. Farina, Department of Neurorehabilitation Engineering, University Medical Center Göttingen (UMG), Georg-August University, Göttingen, Germany},
abstract = {Background: Active hand prostheses controlled using electromyography (EMG) signals have been used for decades to restore the grasping function, lost after an amputation. Although myocontrol is a simple and intuitive interface, it is also imprecise due to the stochastic nature of the EMG recorded using surface electrodes. Furthermore, the sensory feedback from the prosthesis to the user is still missing. In this study, we present a novel concept to close the loop in myoelectric prostheses. In addition to conveying the grasping force (system output), we provided to the user the online information about the system input (EMG biofeedback). Methods: As a proof-of-concept, the EMG biofeedback was transmitted in the current study using a visual interface (ideal condition). Ten able-bodied subjects and two amputees controlled a state-of-the-art myoelectric prosthesis in routine grasping and force steering tasks using EMG and force feedback (novel approach) and force feedback only (classic approach). The outcome measures were the variability of the generated forces and absolute deviation from the target levels in the routine grasping task, and the root mean square tracking error and the number of sudden drops in the force steering task. Results: During the routine grasping, the novel method when used by able-bodied subjects decreased twofold the force dispersion as well as absolute deviations from the target force levels, and also resulted in a more accurate and stable tracking of the reference force profiles during the force steering. Furthermore, the force variability during routine grasping did not increase for the higher target forces with EMG biofeedback. The trend was similar in the two amputees. Conclusions: The study demonstrated that the subjects, including the two experienced users of a myoelectric prosthesis, were able to exploit the online EMG biofeedback to observe and modulate the myoelectric signals, generating thereby more consistent commands. This allowed them to control the force predictively (routine grasping) and with a finer resolution (force steering). The future step will be to implement this promising and simple approach using an electrotactile interface. A prosthesis with a reliable response, following faithfully user intentions, would improve the utility during daily-life use and also facilitate the embodiment of the assistive system.},
keywords = {adult, arm amputation, article, case report, computer interface, controlled study, EMG biofeedback, feedback system, grip strength, human, human computer interaction, Michelangelo hand, middle aged, muscle strength, myoelectric control, myoelectrically controlled prosthesis, online system, prediction, priority journal, Sensor Hand},
pubstate = {published},
tppubtype = {article}
}
Background: Active hand prostheses controlled using electromyography (EMG) signals have been used for decades to restore the grasping function, lost after an amputation. Although myocontrol is a simple and intuitive interface, it is also imprecise due to the stochastic nature of the EMG recorded using surface electrodes. Furthermore, the sensory feedback from the prosthesis to the user is still missing. In this study, we present a novel concept to close the loop in myoelectric prostheses. In addition to conveying the grasping force (system output), we provided to the user the online information about the system input (EMG biofeedback). Methods: As a proof-of-concept, the EMG biofeedback was transmitted in the current study using a visual interface (ideal condition). Ten able-bodied subjects and two amputees controlled a state-of-the-art myoelectric prosthesis in routine grasping and force steering tasks using EMG and force feedback (novel approach) and force feedback only (classic approach). The outcome measures were the variability of the generated forces and absolute deviation from the target levels in the routine grasping task, and the root mean square tracking error and the number of sudden drops in the force steering task. Results: During the routine grasping, the novel method when used by able-bodied subjects decreased twofold the force dispersion as well as absolute deviations from the target force levels, and also resulted in a more accurate and stable tracking of the reference force profiles during the force steering. Furthermore, the force variability during routine grasping did not increase for the higher target forces with EMG biofeedback. The trend was similar in the two amputees. Conclusions: The study demonstrated that the subjects, including the two experienced users of a myoelectric prosthesis, were able to exploit the online EMG biofeedback to observe and modulate the myoelectric signals, generating thereby more consistent commands. This allowed them to control the force predictively (routine grasping) and with a finer resolution (force steering). The future step will be to implement this promising and simple approach using an electrotactile interface. A prosthesis with a reliable response, following faithfully user intentions, would improve the utility during daily-life use and also facilitate the embodiment of the assistive system.
3.
Kistenberg, R. S.
Prosthetic choices for people with leg and arm amputations Artikel
In: Phys. Med. Rehabil. Clin. North Am., Bd. 25, Nr. 1, S. 93–115, 2014, ISSN: 1558-1381.
Abstract | Links | Schlagwörter: anatomy, ankle prosthesis, arm amputation, arm movement, arm prosthesis, biomechanics, bone regeneration, C-leg, Delrin, elbow prosthesis, equipment design, finger amputation, functional status, Genium, hand amputation, health care access, Helix3D, hemipelvectomy, hip prosthesis, human, iLIMB Hand, Kevlar, kinematics, knee prosthesis, leg amputation, leg movement, leg prosthesis, microprocessor, motor control, orthopedic shoe, patient preference, physical activity, Power Knee, priority journal, prosthesis complication, public health service, quality of life, rehabilitation care, review, shoulder prosthesis, surgical technique, surgical technology, suspension, thumb amputation
@article{Kistenberg2014,
title = {Prosthetic choices for people with leg and arm amputations},
author = {R. S. Kistenberg},
url = {https://www.embase.com/search/results?subaction=viewrecord&id=L370343297&from=export},
doi = {10.1016/j.pmr.2013.10.001},
issn = {1558-1381},
year = {2014},
date = {2014-01-01},
journal = {Phys. Med. Rehabil. Clin. North Am.},
volume = {25},
number = {1},
pages = {93–115},
address = {R.S. Kistenberg, Georgia Institute of Technology, School of Applied Physiology, 555 14th Street, Atlanta, GA 30318, United States},
abstract = {New technology and materials have advanced prosthetic designs to enable people who rely on artificial limbs to achieve feats never dreamed before. However, the latest and the greatest technology is not appropriate for everyone. The aim of this article is to present contemporary options that are available for people who rely on artificial limbs to enhance their quality of life for mobility and independence. © 2014 Elsevier Inc.},
keywords = {anatomy, ankle prosthesis, arm amputation, arm movement, arm prosthesis, biomechanics, bone regeneration, C-leg, Delrin, elbow prosthesis, equipment design, finger amputation, functional status, Genium, hand amputation, health care access, Helix3D, hemipelvectomy, hip prosthesis, human, iLIMB Hand, Kevlar, kinematics, knee prosthesis, leg amputation, leg movement, leg prosthesis, microprocessor, motor control, orthopedic shoe, patient preference, physical activity, Power Knee, priority journal, prosthesis complication, public health service, quality of life, rehabilitation care, review, shoulder prosthesis, surgical technique, surgical technology, suspension, thumb amputation},
pubstate = {published},
tppubtype = {article}
}
New technology and materials have advanced prosthetic designs to enable people who rely on artificial limbs to achieve feats never dreamed before. However, the latest and the greatest technology is not appropriate for everyone. The aim of this article is to present contemporary options that are available for people who rely on artificial limbs to enhance their quality of life for mobility and independence. © 2014 Elsevier Inc.
2020
Hahne, J. M.; Wilke, M. A.; Koppe, M.; Farina, D.; Schilling, A. F.
Longitudinal Case Study of Regression-Based Hand Prosthesis Control in Daily Life Artikel
In: Front. Neurosci., Bd. 14, 2020, ISSN: 1662-4548.
Abstract | Links | Schlagwörter: adult, arm amputation, article, case study, clinical article, controlled study, electric hand, hand prosthesis, human, longitudinal study, male, middle aged, motor control, questionnaire, regression analysis, VariPlus Speed
@article{Hahne2020,
title = {Longitudinal Case Study of Regression-Based Hand Prosthesis Control in Daily Life},
author = {J. M. Hahne and M. A. Wilke and M. Koppe and D. Farina and A. F. Schilling},
url = {https://www.embase.com/search/results?subaction=viewrecord&id=L632229544&from=export},
doi = {10.3389/fnins.2020.00600},
issn = {1662-4548},
year = {2020},
date = {2020-01-01},
journal = {Front. Neurosci.},
volume = {14},
address = {J.M. Hahne, Applied Rehabilitation Technology Lab, Department of Trauma Surgery, Orthopedic Surgery and Hand Surgery, University Medical Center Göttingen, Göttingen, Germany},
abstract = {Hand prostheses are usually controlled by electromyographic (EMG) signals from the remnant muscles of the residual limb. Most prostheses used today are controlled with very simple techniques using only two EMG electrodes that allow to control a single prosthetic function at a time only. Recently, modern prosthesis controllers based on EMG classification, have become clinically available, which allow to directly access more functions, but still in a sequential manner only. We have recently shown in laboratory tests that a regression-based mapping from EMG signals into prosthetic control commands allows for a simultaneous activation of two functions and an independent control of their velocities with high reliability. Here we aimed to study how such regression-based control performs in daily life in a two-month case study. The performance is evaluated in functional tests and with a questionnaire at the beginning and the end of this phase and compared with the participant’s own prosthesis, controlled with a classical approach. Already 1 day after training of the regression model, the participant with transradial amputation outperformed the performance achieved with his own Michelangelo hand in two out of three functional metrics. No retraining of the model was required during the entire study duration. During the use of the system at home, the performance improved further and outperformed the conventional control in all three metrics. This study demonstrates that the high fidelity of linear regression-based prosthesis control is not restricted to a laboratory environment, but can be transferred to daily use.},
keywords = {adult, arm amputation, article, case study, clinical article, controlled study, electric hand, hand prosthesis, human, longitudinal study, male, middle aged, motor control, questionnaire, regression analysis, VariPlus Speed},
pubstate = {published},
tppubtype = {article}
}
Hand prostheses are usually controlled by electromyographic (EMG) signals from the remnant muscles of the residual limb. Most prostheses used today are controlled with very simple techniques using only two EMG electrodes that allow to control a single prosthetic function at a time only. Recently, modern prosthesis controllers based on EMG classification, have become clinically available, which allow to directly access more functions, but still in a sequential manner only. We have recently shown in laboratory tests that a regression-based mapping from EMG signals into prosthetic control commands allows for a simultaneous activation of two functions and an independent control of their velocities with high reliability. Here we aimed to study how such regression-based control performs in daily life in a two-month case study. The performance is evaluated in functional tests and with a questionnaire at the beginning and the end of this phase and compared with the participant’s own prosthesis, controlled with a classical approach. Already 1 day after training of the regression model, the participant with transradial amputation outperformed the performance achieved with his own Michelangelo hand in two out of three functional metrics. No retraining of the model was required during the entire study duration. During the use of the system at home, the performance improved further and outperformed the conventional control in all three metrics. This study demonstrates that the high fidelity of linear regression-based prosthesis control is not restricted to a laboratory environment, but can be transferred to daily use.
2015
Dosen, S.; Markovic, M.; Somer, K.; Graimann, B.; Farina, D.
EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis Artikel
In: J. NeuroEng. Rehabil., Bd. 12, Nr. 1, 2015, ISSN: 1743-0003.
Abstract | Links | Schlagwörter: adult, arm amputation, article, case report, computer interface, controlled study, EMG biofeedback, feedback system, grip strength, human, human computer interaction, Michelangelo hand, middle aged, muscle strength, myoelectric control, myoelectrically controlled prosthesis, online system, prediction, priority journal, Sensor Hand
@article{Dosen2015,
title = {EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis},
author = {S. Dosen and M. Markovic and K. Somer and B. Graimann and D. Farina},
url = {https://www.embase.com/search/results?subaction=viewrecord&id=L605002250&from=export},
doi = {10.1186/s12984-015-0047-z},
issn = {1743-0003},
year = {2015},
date = {2015-06-01},
journal = {J. NeuroEng. Rehabil.},
volume = {12},
number = {1},
publisher = {Springer Science and Business Media LLC},
address = {D. Farina, Department of Neurorehabilitation Engineering, University Medical Center Göttingen (UMG), Georg-August University, Göttingen, Germany},
abstract = {Background: Active hand prostheses controlled using electromyography (EMG) signals have been used for decades to restore the grasping function, lost after an amputation. Although myocontrol is a simple and intuitive interface, it is also imprecise due to the stochastic nature of the EMG recorded using surface electrodes. Furthermore, the sensory feedback from the prosthesis to the user is still missing. In this study, we present a novel concept to close the loop in myoelectric prostheses. In addition to conveying the grasping force (system output), we provided to the user the online information about the system input (EMG biofeedback). Methods: As a proof-of-concept, the EMG biofeedback was transmitted in the current study using a visual interface (ideal condition). Ten able-bodied subjects and two amputees controlled a state-of-the-art myoelectric prosthesis in routine grasping and force steering tasks using EMG and force feedback (novel approach) and force feedback only (classic approach). The outcome measures were the variability of the generated forces and absolute deviation from the target levels in the routine grasping task, and the root mean square tracking error and the number of sudden drops in the force steering task. Results: During the routine grasping, the novel method when used by able-bodied subjects decreased twofold the force dispersion as well as absolute deviations from the target force levels, and also resulted in a more accurate and stable tracking of the reference force profiles during the force steering. Furthermore, the force variability during routine grasping did not increase for the higher target forces with EMG biofeedback. The trend was similar in the two amputees. Conclusions: The study demonstrated that the subjects, including the two experienced users of a myoelectric prosthesis, were able to exploit the online EMG biofeedback to observe and modulate the myoelectric signals, generating thereby more consistent commands. This allowed them to control the force predictively (routine grasping) and with a finer resolution (force steering). The future step will be to implement this promising and simple approach using an electrotactile interface. A prosthesis with a reliable response, following faithfully user intentions, would improve the utility during daily-life use and also facilitate the embodiment of the assistive system.},
keywords = {adult, arm amputation, article, case report, computer interface, controlled study, EMG biofeedback, feedback system, grip strength, human, human computer interaction, Michelangelo hand, middle aged, muscle strength, myoelectric control, myoelectrically controlled prosthesis, online system, prediction, priority journal, Sensor Hand},
pubstate = {published},
tppubtype = {article}
}
Background: Active hand prostheses controlled using electromyography (EMG) signals have been used for decades to restore the grasping function, lost after an amputation. Although myocontrol is a simple and intuitive interface, it is also imprecise due to the stochastic nature of the EMG recorded using surface electrodes. Furthermore, the sensory feedback from the prosthesis to the user is still missing. In this study, we present a novel concept to close the loop in myoelectric prostheses. In addition to conveying the grasping force (system output), we provided to the user the online information about the system input (EMG biofeedback). Methods: As a proof-of-concept, the EMG biofeedback was transmitted in the current study using a visual interface (ideal condition). Ten able-bodied subjects and two amputees controlled a state-of-the-art myoelectric prosthesis in routine grasping and force steering tasks using EMG and force feedback (novel approach) and force feedback only (classic approach). The outcome measures were the variability of the generated forces and absolute deviation from the target levels in the routine grasping task, and the root mean square tracking error and the number of sudden drops in the force steering task. Results: During the routine grasping, the novel method when used by able-bodied subjects decreased twofold the force dispersion as well as absolute deviations from the target force levels, and also resulted in a more accurate and stable tracking of the reference force profiles during the force steering. Furthermore, the force variability during routine grasping did not increase for the higher target forces with EMG biofeedback. The trend was similar in the two amputees. Conclusions: The study demonstrated that the subjects, including the two experienced users of a myoelectric prosthesis, were able to exploit the online EMG biofeedback to observe and modulate the myoelectric signals, generating thereby more consistent commands. This allowed them to control the force predictively (routine grasping) and with a finer resolution (force steering). The future step will be to implement this promising and simple approach using an electrotactile interface. A prosthesis with a reliable response, following faithfully user intentions, would improve the utility during daily-life use and also facilitate the embodiment of the assistive system.
2014
Kistenberg, R. S.
Prosthetic choices for people with leg and arm amputations Artikel
In: Phys. Med. Rehabil. Clin. North Am., Bd. 25, Nr. 1, S. 93–115, 2014, ISSN: 1558-1381.
Abstract | Links | Schlagwörter: anatomy, ankle prosthesis, arm amputation, arm movement, arm prosthesis, biomechanics, bone regeneration, C-leg, Delrin, elbow prosthesis, equipment design, finger amputation, functional status, Genium, hand amputation, health care access, Helix3D, hemipelvectomy, hip prosthesis, human, iLIMB Hand, Kevlar, kinematics, knee prosthesis, leg amputation, leg movement, leg prosthesis, microprocessor, motor control, orthopedic shoe, patient preference, physical activity, Power Knee, priority journal, prosthesis complication, public health service, quality of life, rehabilitation care, review, shoulder prosthesis, surgical technique, surgical technology, suspension, thumb amputation
@article{Kistenberg2014,
title = {Prosthetic choices for people with leg and arm amputations},
author = {R. S. Kistenberg},
url = {https://www.embase.com/search/results?subaction=viewrecord&id=L370343297&from=export},
doi = {10.1016/j.pmr.2013.10.001},
issn = {1558-1381},
year = {2014},
date = {2014-01-01},
journal = {Phys. Med. Rehabil. Clin. North Am.},
volume = {25},
number = {1},
pages = {93–115},
address = {R.S. Kistenberg, Georgia Institute of Technology, School of Applied Physiology, 555 14th Street, Atlanta, GA 30318, United States},
abstract = {New technology and materials have advanced prosthetic designs to enable people who rely on artificial limbs to achieve feats never dreamed before. However, the latest and the greatest technology is not appropriate for everyone. The aim of this article is to present contemporary options that are available for people who rely on artificial limbs to enhance their quality of life for mobility and independence. © 2014 Elsevier Inc.},
keywords = {anatomy, ankle prosthesis, arm amputation, arm movement, arm prosthesis, biomechanics, bone regeneration, C-leg, Delrin, elbow prosthesis, equipment design, finger amputation, functional status, Genium, hand amputation, health care access, Helix3D, hemipelvectomy, hip prosthesis, human, iLIMB Hand, Kevlar, kinematics, knee prosthesis, leg amputation, leg movement, leg prosthesis, microprocessor, motor control, orthopedic shoe, patient preference, physical activity, Power Knee, priority journal, prosthesis complication, public health service, quality of life, rehabilitation care, review, shoulder prosthesis, surgical technique, surgical technology, suspension, thumb amputation},
pubstate = {published},
tppubtype = {article}
}
New technology and materials have advanced prosthetic designs to enable people who rely on artificial limbs to achieve feats never dreamed before. However, the latest and the greatest technology is not appropriate for everyone. The aim of this article is to present contemporary options that are available for people who rely on artificial limbs to enhance their quality of life for mobility and independence. © 2014 Elsevier Inc.
2020
Hahne, J. M.; Wilke, M. A.; Koppe, M.; Farina, D.; Schilling, A. F.
Longitudinal Case Study of Regression-Based Hand Prosthesis Control in Daily Life Artikel
In: Front. Neurosci., Bd. 14, 2020, ISSN: 1662-4548.
@article{Hahne2020,
title = {Longitudinal Case Study of Regression-Based Hand Prosthesis Control in Daily Life},
author = {J. M. Hahne and M. A. Wilke and M. Koppe and D. Farina and A. F. Schilling},
url = {https://www.embase.com/search/results?subaction=viewrecord&id=L632229544&from=export},
doi = {10.3389/fnins.2020.00600},
issn = {1662-4548},
year = {2020},
date = {2020-01-01},
journal = {Front. Neurosci.},
volume = {14},
address = {J.M. Hahne, Applied Rehabilitation Technology Lab, Department of Trauma Surgery, Orthopedic Surgery and Hand Surgery, University Medical Center Göttingen, Göttingen, Germany},
abstract = {Hand prostheses are usually controlled by electromyographic (EMG) signals from the remnant muscles of the residual limb. Most prostheses used today are controlled with very simple techniques using only two EMG electrodes that allow to control a single prosthetic function at a time only. Recently, modern prosthesis controllers based on EMG classification, have become clinically available, which allow to directly access more functions, but still in a sequential manner only. We have recently shown in laboratory tests that a regression-based mapping from EMG signals into prosthetic control commands allows for a simultaneous activation of two functions and an independent control of their velocities with high reliability. Here we aimed to study how such regression-based control performs in daily life in a two-month case study. The performance is evaluated in functional tests and with a questionnaire at the beginning and the end of this phase and compared with the participant’s own prosthesis, controlled with a classical approach. Already 1 day after training of the regression model, the participant with transradial amputation outperformed the performance achieved with his own Michelangelo hand in two out of three functional metrics. No retraining of the model was required during the entire study duration. During the use of the system at home, the performance improved further and outperformed the conventional control in all three metrics. This study demonstrates that the high fidelity of linear regression-based prosthesis control is not restricted to a laboratory environment, but can be transferred to daily use.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hand prostheses are usually controlled by electromyographic (EMG) signals from the remnant muscles of the residual limb. Most prostheses used today are controlled with very simple techniques using only two EMG electrodes that allow to control a single prosthetic function at a time only. Recently, modern prosthesis controllers based on EMG classification, have become clinically available, which allow to directly access more functions, but still in a sequential manner only. We have recently shown in laboratory tests that a regression-based mapping from EMG signals into prosthetic control commands allows for a simultaneous activation of two functions and an independent control of their velocities with high reliability. Here we aimed to study how such regression-based control performs in daily life in a two-month case study. The performance is evaluated in functional tests and with a questionnaire at the beginning and the end of this phase and compared with the participant’s own prosthesis, controlled with a classical approach. Already 1 day after training of the regression model, the participant with transradial amputation outperformed the performance achieved with his own Michelangelo hand in two out of three functional metrics. No retraining of the model was required during the entire study duration. During the use of the system at home, the performance improved further and outperformed the conventional control in all three metrics. This study demonstrates that the high fidelity of linear regression-based prosthesis control is not restricted to a laboratory environment, but can be transferred to daily use.
2015
Dosen, S.; Markovic, M.; Somer, K.; Graimann, B.; Farina, D.
EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis Artikel
In: J. NeuroEng. Rehabil., Bd. 12, Nr. 1, 2015, ISSN: 1743-0003.
@article{Dosen2015,
title = {EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis},
author = {S. Dosen and M. Markovic and K. Somer and B. Graimann and D. Farina},
url = {https://www.embase.com/search/results?subaction=viewrecord&id=L605002250&from=export},
doi = {10.1186/s12984-015-0047-z},
issn = {1743-0003},
year = {2015},
date = {2015-06-01},
journal = {J. NeuroEng. Rehabil.},
volume = {12},
number = {1},
publisher = {Springer Science and Business Media LLC},
address = {D. Farina, Department of Neurorehabilitation Engineering, University Medical Center Göttingen (UMG), Georg-August University, Göttingen, Germany},
abstract = {Background: Active hand prostheses controlled using electromyography (EMG) signals have been used for decades to restore the grasping function, lost after an amputation. Although myocontrol is a simple and intuitive interface, it is also imprecise due to the stochastic nature of the EMG recorded using surface electrodes. Furthermore, the sensory feedback from the prosthesis to the user is still missing. In this study, we present a novel concept to close the loop in myoelectric prostheses. In addition to conveying the grasping force (system output), we provided to the user the online information about the system input (EMG biofeedback). Methods: As a proof-of-concept, the EMG biofeedback was transmitted in the current study using a visual interface (ideal condition). Ten able-bodied subjects and two amputees controlled a state-of-the-art myoelectric prosthesis in routine grasping and force steering tasks using EMG and force feedback (novel approach) and force feedback only (classic approach). The outcome measures were the variability of the generated forces and absolute deviation from the target levels in the routine grasping task, and the root mean square tracking error and the number of sudden drops in the force steering task. Results: During the routine grasping, the novel method when used by able-bodied subjects decreased twofold the force dispersion as well as absolute deviations from the target force levels, and also resulted in a more accurate and stable tracking of the reference force profiles during the force steering. Furthermore, the force variability during routine grasping did not increase for the higher target forces with EMG biofeedback. The trend was similar in the two amputees. Conclusions: The study demonstrated that the subjects, including the two experienced users of a myoelectric prosthesis, were able to exploit the online EMG biofeedback to observe and modulate the myoelectric signals, generating thereby more consistent commands. This allowed them to control the force predictively (routine grasping) and with a finer resolution (force steering). The future step will be to implement this promising and simple approach using an electrotactile interface. A prosthesis with a reliable response, following faithfully user intentions, would improve the utility during daily-life use and also facilitate the embodiment of the assistive system.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Active hand prostheses controlled using electromyography (EMG) signals have been used for decades to restore the grasping function, lost after an amputation. Although myocontrol is a simple and intuitive interface, it is also imprecise due to the stochastic nature of the EMG recorded using surface electrodes. Furthermore, the sensory feedback from the prosthesis to the user is still missing. In this study, we present a novel concept to close the loop in myoelectric prostheses. In addition to conveying the grasping force (system output), we provided to the user the online information about the system input (EMG biofeedback). Methods: As a proof-of-concept, the EMG biofeedback was transmitted in the current study using a visual interface (ideal condition). Ten able-bodied subjects and two amputees controlled a state-of-the-art myoelectric prosthesis in routine grasping and force steering tasks using EMG and force feedback (novel approach) and force feedback only (classic approach). The outcome measures were the variability of the generated forces and absolute deviation from the target levels in the routine grasping task, and the root mean square tracking error and the number of sudden drops in the force steering task. Results: During the routine grasping, the novel method when used by able-bodied subjects decreased twofold the force dispersion as well as absolute deviations from the target force levels, and also resulted in a more accurate and stable tracking of the reference force profiles during the force steering. Furthermore, the force variability during routine grasping did not increase for the higher target forces with EMG biofeedback. The trend was similar in the two amputees. Conclusions: The study demonstrated that the subjects, including the two experienced users of a myoelectric prosthesis, were able to exploit the online EMG biofeedback to observe and modulate the myoelectric signals, generating thereby more consistent commands. This allowed them to control the force predictively (routine grasping) and with a finer resolution (force steering). The future step will be to implement this promising and simple approach using an electrotactile interface. A prosthesis with a reliable response, following faithfully user intentions, would improve the utility during daily-life use and also facilitate the embodiment of the assistive system.
2014
Kistenberg, R. S.
Prosthetic choices for people with leg and arm amputations Artikel
In: Phys. Med. Rehabil. Clin. North Am., Bd. 25, Nr. 1, S. 93–115, 2014, ISSN: 1558-1381.
@article{Kistenberg2014,
title = {Prosthetic choices for people with leg and arm amputations},
author = {R. S. Kistenberg},
url = {https://www.embase.com/search/results?subaction=viewrecord&id=L370343297&from=export},
doi = {10.1016/j.pmr.2013.10.001},
issn = {1558-1381},
year = {2014},
date = {2014-01-01},
journal = {Phys. Med. Rehabil. Clin. North Am.},
volume = {25},
number = {1},
pages = {93–115},
address = {R.S. Kistenberg, Georgia Institute of Technology, School of Applied Physiology, 555 14th Street, Atlanta, GA 30318, United States},
abstract = {New technology and materials have advanced prosthetic designs to enable people who rely on artificial limbs to achieve feats never dreamed before. However, the latest and the greatest technology is not appropriate for everyone. The aim of this article is to present contemporary options that are available for people who rely on artificial limbs to enhance their quality of life for mobility and independence. © 2014 Elsevier Inc.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
New technology and materials have advanced prosthetic designs to enable people who rely on artificial limbs to achieve feats never dreamed before. However, the latest and the greatest technology is not appropriate for everyone. The aim of this article is to present contemporary options that are available for people who rely on artificial limbs to enhance their quality of life for mobility and independence. © 2014 Elsevier Inc.