The Experts below are selected from a list of 4329 Experts worldwide ranked by ideXlab platform
Matteo Cognolato - One of the best experts on this subject based on the ideXlab platform.
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gaze visual myoelectric and inertial data of grasps for intelligent prosthetics
Scientific Data, 2020Co-Authors: Matteo Cognolato, Arjan Gijsberts, Valentina Gregori, Gianluca Saetta, Katia Giacomino, Annegabrielle Mittaz Hager, Andrea Gigli, Diego FaccioAbstract:A Hand Amputation is a highly disabling event, having severe physical and psychological repercussions on a person’s life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric Hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among them the fusion of surface electromyography with other sources of contextual information. We present a dataset to investigate the inclusion of eye tracking and first person video to provide more stable intent recognition for prosthetic control. This multimodal dataset contains surface electromyography and accelerometry of the forearm, and gaze, first person video, and inertial measurements of the head recorded from 15 transradial amputees and 30 able-bodied subjects performing grasping tasks. Besides the intended application for upper-limb prosthetics, we also foresee uses for this dataset to study eye-Hand coordination in the context of psychophysics, neuroscience, and assistive robotics. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.11672442
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gaze visual myoelectric and inertial data of grasps for intelligent prosthetics
medRxiv, 2019Co-Authors: Matteo Cognolato, Arjan Gijsberts, Valentina Gregori, Gianluca Saetta, Katia Giacomino, Annegabrielle Mittaz Hager, Andrea Gigli, Diego FaccioAbstract:Hand Amputation is a highly disabling event, having severe physical and psychological repercussions on a person9s life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric Hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among which the fusion of surface electromyography with other sources of contextual information. We present a dataset to investigate the inclusion of eye tracking and first person video to provide more stable intent recognition for prosthetic control. This multimodal dataset contains surface electromyography and accelerometry of the forearm, and gaze, first person video, and inertial measurements of the head recorded from 15 transradial amputees and 30 able-bodied subjects performing grasping tasks. Besides the intended application for upper-limb prosthetics, we also foresee uses for this dataset to study eye-Hand coordination in the context of psychophysics, neuroscience, and assistive robotics.
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Hand gesture classification in transradial amputees using the myo armband classifier this work was partially supported by the swiss national science foundation sinergia project 410160837 meganepro
IEEE International Conference on Biomedical Robotics and Biomechatronics, 2018Co-Authors: Matteo Cognolato, Diego Faccio, Manfredo Atzori, Cesare Tiengo, F Bassette, Roger Gassert, Henning MullerAbstract:Dexterous Hand prostheses controlled via surface electromyography represent the most advanced non invasive functional restorative solution for Hand amputees. However, control difficulties, comfort problems and high costs are still the main limitations of such commercial devices. The high cost can represent a barrier that is difficult to overcome, especially for pediatric populations and in developing countries. Low-cost technology was successfully used in the Hand prosthetics field in recent years. In previous work, a low-cost gesture recognition armband called Myo showed promising results for Hand gesture classification tasks in intact subjects. Most of these applications were based on machine learning techniques applied to the Myo raw data. However, the classifier provided with the Myo is able to identify five Hand gestures, providing capabilities as a myoelectric control system. No studies have quantitatively investigated its performance in subjects with Hand Amputation, yet. The aim of this study is to quantitatively evaluate the performance of the Myo Hand gesture classifier in Hand amputees. Three subjects with Hand Amputation were asked to attempt performing the five pre-set Hand gestures. Each gesture was repeated three times with the arm in three different postures. The subjects did not perform any training and did not receive any feedback. Overall classification accuracy for the four Hand gestures based on electromyographic data ranged between 50% and 97%. A clear relation between the length of the residual limb and the classification accuracy was observed. The results show that the Myo built-in classifier can provide good performance when tested on Hand amputees, supporting its applicability as a low-cost myoelectric control system.
Ivo Strauss - One of the best experts on this subject based on the ideXlab platform.
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intraneural sensory feedback restores grip force control and motor coordination while using a prosthetic Hand
Journal of Neural Engineering, 2019Co-Authors: Francesco Clemente, Marco Controzzi, Giacomo Valle, Ivo Strauss, Giuseppe Granata, Francesco Iberite, Thomas Stieglitz, Paolo M RossiniAbstract:Objective Tactile afferents in the human Hand provide fundamental information about Hand-environment interactions, which is used by the brain to adapt the motor output to the physical properties of the object being manipulated. A Hand Amputation disrupts both afferent and efferent pathways from/to the Hand, completely invalidating the individual's motor repertoire. Although motor functions may be partially recovered by using a myoelectric prosthesis, providing functionally effective sensory feedback to users of prosthetics is a largely unsolved challenge. While past studies using invasive stimulation suggested that sensory feedback may help in Handling fragile objects, none explored the underpinning, relearned, motor coordination during grasping. In this study, we aimed at showing for the first time that intraneural sensory feedback of the grip force (GF) improves the sensorimotor control of a transradial amputee controlling a myoelectric prosthesis. Approach We performed a longitudinal study testing a single subject (clinical trial registration number NCT02848846). A stacking cups test (CUP) performed over two weeks aimed at measuring the subject's ability to finely regulate the GF applied with the prosthesis. A pick and lift test (PLT), performed at the end of the study, measured the level of motor coordination, and whether the subject transferred the motor skills learned in the CUP to an alien task. Main results The results show that intraneural sensory feedback increases the subject's ability in regulating the GF and allows for improved performance over time. Additionally, the PLT demonstrated that the subject was able to generalize and transfer her manipulation skills to an unknown task and to improve her motor coordination. Significance Our findings suggest that intraneural sensory feedback holds the potential of restoring functionally effective tactile feedback. This opens up new possibilities to improve the quality of life of amputees using a neural prosthesis.
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six month assessment of a Hand prosthesis with intraneural tactile feedback
Annals of Neurology, 2019Co-Authors: Francesco Maria Petrini, Giacomo Valle, Ivo Strauss, Giuseppe Granata, Riccardo Di Iorio, Edoardo Danna, Paul Cvancara, Matthias MuellerAbstract:OBJECTIVE Hand Amputation is a highly disabling event, which significantly affects quality of life. An effective Hand replacement can be achieved if the user, in addition to motor functions, is provided with the sensations that are naturally perceived while grasping and moving. Intraneural peripheral electrodes have shown promising results toward the restoration of the sense of touch. However, the long-term usability and clinical relevance of intraneural sensory feedback have not yet been clearly demonstrated. METHODS To this aim, we performed a 6-month clinical study with 3 transradial amputees who received implants of transverse intrafascicular multichannel electrodes (TIMEs) in their median and ulnar nerves. After calibration, electrical stimulation was delivered through the TIMEs connected to artificial sensors in the digits of a prosthesis to generate sensory feedback, which was then used by the subjects while performing different grasping tasks. RESULTS All subjects, notwithstanding their important clinical differences, reported stimulation-induced sensations from the phantom Hand for the whole duration of the trial. They also successfully integrated the sensory feedback into their motor control strategies while performing experimental tests simulating tasks of real life (with and without the support of vision). Finally, they reported a decrement of their phantom limb pain and a general improvement in mood state. INTERPRETATION The promising results achieved with all subjects show the feasibility of the use of intraneural stimulation in clinical settings. ANN NEUROL 2019;85:137-154.
Giacomo Valle - One of the best experts on this subject based on the ideXlab platform.
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intraneural sensory feedback restores grip force control and motor coordination while using a prosthetic Hand
Journal of Neural Engineering, 2019Co-Authors: Francesco Clemente, Marco Controzzi, Giacomo Valle, Ivo Strauss, Giuseppe Granata, Francesco Iberite, Thomas Stieglitz, Paolo M RossiniAbstract:Objective Tactile afferents in the human Hand provide fundamental information about Hand-environment interactions, which is used by the brain to adapt the motor output to the physical properties of the object being manipulated. A Hand Amputation disrupts both afferent and efferent pathways from/to the Hand, completely invalidating the individual's motor repertoire. Although motor functions may be partially recovered by using a myoelectric prosthesis, providing functionally effective sensory feedback to users of prosthetics is a largely unsolved challenge. While past studies using invasive stimulation suggested that sensory feedback may help in Handling fragile objects, none explored the underpinning, relearned, motor coordination during grasping. In this study, we aimed at showing for the first time that intraneural sensory feedback of the grip force (GF) improves the sensorimotor control of a transradial amputee controlling a myoelectric prosthesis. Approach We performed a longitudinal study testing a single subject (clinical trial registration number NCT02848846). A stacking cups test (CUP) performed over two weeks aimed at measuring the subject's ability to finely regulate the GF applied with the prosthesis. A pick and lift test (PLT), performed at the end of the study, measured the level of motor coordination, and whether the subject transferred the motor skills learned in the CUP to an alien task. Main results The results show that intraneural sensory feedback increases the subject's ability in regulating the GF and allows for improved performance over time. Additionally, the PLT demonstrated that the subject was able to generalize and transfer her manipulation skills to an unknown task and to improve her motor coordination. Significance Our findings suggest that intraneural sensory feedback holds the potential of restoring functionally effective tactile feedback. This opens up new possibilities to improve the quality of life of amputees using a neural prosthesis.
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six month assessment of a Hand prosthesis with intraneural tactile feedback
Annals of Neurology, 2019Co-Authors: Francesco Maria Petrini, Giacomo Valle, Ivo Strauss, Giuseppe Granata, Riccardo Di Iorio, Edoardo Danna, Paul Cvancara, Matthias MuellerAbstract:OBJECTIVE Hand Amputation is a highly disabling event, which significantly affects quality of life. An effective Hand replacement can be achieved if the user, in addition to motor functions, is provided with the sensations that are naturally perceived while grasping and moving. Intraneural peripheral electrodes have shown promising results toward the restoration of the sense of touch. However, the long-term usability and clinical relevance of intraneural sensory feedback have not yet been clearly demonstrated. METHODS To this aim, we performed a 6-month clinical study with 3 transradial amputees who received implants of transverse intrafascicular multichannel electrodes (TIMEs) in their median and ulnar nerves. After calibration, electrical stimulation was delivered through the TIMEs connected to artificial sensors in the digits of a prosthesis to generate sensory feedback, which was then used by the subjects while performing different grasping tasks. RESULTS All subjects, notwithstanding their important clinical differences, reported stimulation-induced sensations from the phantom Hand for the whole duration of the trial. They also successfully integrated the sensory feedback into their motor control strategies while performing experimental tests simulating tasks of real life (with and without the support of vision). Finally, they reported a decrement of their phantom limb pain and a general improvement in mood state. INTERPRETATION The promising results achieved with all subjects show the feasibility of the use of intraneural stimulation in clinical settings. ANN NEUROL 2019;85:137-154.
Diego Faccio - One of the best experts on this subject based on the ideXlab platform.
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gaze visual myoelectric and inertial data of grasps for intelligent prosthetics
Scientific Data, 2020Co-Authors: Matteo Cognolato, Arjan Gijsberts, Valentina Gregori, Gianluca Saetta, Katia Giacomino, Annegabrielle Mittaz Hager, Andrea Gigli, Diego FaccioAbstract:A Hand Amputation is a highly disabling event, having severe physical and psychological repercussions on a person’s life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric Hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among them the fusion of surface electromyography with other sources of contextual information. We present a dataset to investigate the inclusion of eye tracking and first person video to provide more stable intent recognition for prosthetic control. This multimodal dataset contains surface electromyography and accelerometry of the forearm, and gaze, first person video, and inertial measurements of the head recorded from 15 transradial amputees and 30 able-bodied subjects performing grasping tasks. Besides the intended application for upper-limb prosthetics, we also foresee uses for this dataset to study eye-Hand coordination in the context of psychophysics, neuroscience, and assistive robotics. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.11672442
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gaze visual myoelectric and inertial data of grasps for intelligent prosthetics
medRxiv, 2019Co-Authors: Matteo Cognolato, Arjan Gijsberts, Valentina Gregori, Gianluca Saetta, Katia Giacomino, Annegabrielle Mittaz Hager, Andrea Gigli, Diego FaccioAbstract:Hand Amputation is a highly disabling event, having severe physical and psychological repercussions on a person9s life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric Hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among which the fusion of surface electromyography with other sources of contextual information. We present a dataset to investigate the inclusion of eye tracking and first person video to provide more stable intent recognition for prosthetic control. This multimodal dataset contains surface electromyography and accelerometry of the forearm, and gaze, first person video, and inertial measurements of the head recorded from 15 transradial amputees and 30 able-bodied subjects performing grasping tasks. Besides the intended application for upper-limb prosthetics, we also foresee uses for this dataset to study eye-Hand coordination in the context of psychophysics, neuroscience, and assistive robotics.
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Hand gesture classification in transradial amputees using the myo armband classifier this work was partially supported by the swiss national science foundation sinergia project 410160837 meganepro
IEEE International Conference on Biomedical Robotics and Biomechatronics, 2018Co-Authors: Matteo Cognolato, Diego Faccio, Manfredo Atzori, Cesare Tiengo, F Bassette, Roger Gassert, Henning MullerAbstract:Dexterous Hand prostheses controlled via surface electromyography represent the most advanced non invasive functional restorative solution for Hand amputees. However, control difficulties, comfort problems and high costs are still the main limitations of such commercial devices. The high cost can represent a barrier that is difficult to overcome, especially for pediatric populations and in developing countries. Low-cost technology was successfully used in the Hand prosthetics field in recent years. In previous work, a low-cost gesture recognition armband called Myo showed promising results for Hand gesture classification tasks in intact subjects. Most of these applications were based on machine learning techniques applied to the Myo raw data. However, the classifier provided with the Myo is able to identify five Hand gestures, providing capabilities as a myoelectric control system. No studies have quantitatively investigated its performance in subjects with Hand Amputation, yet. The aim of this study is to quantitatively evaluate the performance of the Myo Hand gesture classifier in Hand amputees. Three subjects with Hand Amputation were asked to attempt performing the five pre-set Hand gestures. Each gesture was repeated three times with the arm in three different postures. The subjects did not perform any training and did not receive any feedback. Overall classification accuracy for the four Hand gestures based on electromyographic data ranged between 50% and 97%. A clear relation between the length of the residual limb and the classification accuracy was observed. The results show that the Myo built-in classifier can provide good performance when tested on Hand amputees, supporting its applicability as a low-cost myoelectric control system.
Christian Cipriani - One of the best experts on this subject based on the ideXlab platform.
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treatment of the partial Hand Amputation an engineering perspective
IEEE Reviews in Biomedical Engineering, 2016Co-Authors: I Imbinto, Carlo Peccia, Marco Controzzi, Andrea Giovanni Cutti, Angelo Davalli, Rinaldo Sacchetti, Christian CiprianiAbstract:Partial Hand Amputation is perhaps the most frequent Amputation level, worldwide. Although its annual incidence in western countries is roughly 1:18 000 inhabitants, the treatments of partial Hand Amputations have modestly progressed so far. We have identified three main limitation factors to this progress: 1) the wide range of anatomical and functional presentations, which makes difficult to find standardized and scalable solutions; 2) the technological complexity in replacing the motor and sensory function of a lost digit in the size of a digit; and 3) the fact that a partial Hand can be functionally successful mostly when it restores a lost opposition movement, i.e., the ability to oppose the thumb against the fingers while providing enough grasping force and aperture width. This review presents an overview of the existing surgical and technological solutions for treating partial Hand Amputations, and is specifically targeted to (biomedical) engineers. We critically highlighted the advantages, limitations, and open challenges. Remarkably, current fitting procedures rely on manual approaches by skilled prosthetic technicians rather than on modern engineering methods. Hence, the objective of this study is to comprehensively but concisely overview the field in order to inspire engineers to develop new systems and procedures able to address current open issues.
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Sensory feedback in upper limb prosthetics.
Expert review of medical devices, 2013Co-Authors: Marco D'alonzo, Christian CiprianiAbstract:One of the challenges facing prosthetic designers and engineers is to restore the missing sensory function inherit to Hand Amputation. Several different techniques can be employed to provide amputees with sensory feedback: sensory substitution methods where the recorded stimulus is not only transferred to the amputee, but also translated to a different modality (modality-matched feedback), which transfers the stimulus without translation and direct neural stimulation, which interacts directly with peripheral afferent nerves. This paper presents an overview of the principal works and devices employed to provide upper limb amputees with sensory feedback. The focus is on sensory substitution and modality matched feedback; the principal features, advantages and disadvantages of the different methods are presented.
