The Experts below are selected from a list of 18 Experts worldwide ranked by ideXlab platform
Aldo A Faisal - One of the best experts on this subject based on the ideXlab platform.
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gaussian process autoregression for simultaneous proportional multi modal prosthetic control with natural Hand kinematics
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017Co-Authors: Michele Xiloyannis, Constantinos Gavriel, Andreas A C Thomik, Aldo A FaisalAbstract:Matching the dexterity, versatility, and robustness of the human Hand is still an unachieved goal in bionics, robotics, and neural engineering. A major limitation for Hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic Hands. Most of the commercially available prosthetic Hands use muscle-related signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole Hand. Here, in contrast, we aim to offer users flexible control of individual Joints of their artificial Hand. We propose a novel framework for decoding neural information that enables a user to independently control 11 Joints of the Hand in a continuous manner—much like we control our natural Hands. Toward this end, we instructed six able-bodied subjects to perform everyday object manipulation tasks combining both dynamic, free movements (e.g., grasping) and isometric force tasks (e.g., squeezing). We recorded the electromyographic and mechanomyographic activities of five extrinsic muscles of the Hand in the forearm, while simultaneously monitoring 11 Joints of Hand and fingers using a sensorized data glove that tracked the Joints of the Hand. Instead of learning just a direct mapping from current muscle activity to intended Hand movement, we formulated a novel autoregressive approach that combines the context of previous Hand movements with instantaneous muscle activity to predict future Hand movements. Specifically, we evaluated a linear vector autoregressive moving average model with exogenous inputs and a novel Gaussian process ( $\mathcal {GP}$ ) autoregressive framework to learn the continuous mapping from Hand joint dynamics and muscle activity to decode intended Hand movement. Our $\mathcal {GP}$ approach achieves high levels of performance (RMSE of 8°/s and $\rho =0.79$ ). Crucially, we use a small set of sensors that allows us to control a larger set of independently actuated degrees of freedom of a Hand. This novel undersensored control is enabled through the combination of nonlinear autoregressive continuous mapping between muscle activity and joint angles. The system evaluates the muscle signals in the context of previous natural Hand movements. This enables us to resolve ambiguities in situations, where muscle signals alone cannot determine the correct action as we evaluate the muscle signals in their context of natural Hand movements. $\mathcal {GP}$ autoregression is a particularly powerful approach which makes not only a prediction based on the context but also represents the associated uncertainty of its predictions, thus enabling the novel notion of risk-based control in neuroprosthetics. Our results suggest that $\mathcal {GP}$ autoregressive approaches with exogenous inputs lend themselves for natural, intuitive, and continuous control in neurotechnology, with the particular focus on prosthetic restoration of natural limb function, where high dexterity is required for complex movements.
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dynamic forward prediction for prosthetic Hand control by integration of emg mmg and kinematic signals
International IEEE EMBS Conference on Neural Engineering, 2015Co-Authors: Michele Xiloyannis, Constantinos Gavriel, Andreas A C Thomik, Aldo A FaisalAbstract:We propose a new framework for extracting information from extrinsic muscles in the forearm that will allow a continuous, natural and intuitive control of a neuroprosthetic devices and robotic Hands. This is achieved through a continuous mapping between muscle activity and joint angles rather than prior discretisation of Hand gestures. We instructed 6 able-bodied subjects, to perform everyday object manipulation tasks. We recorded the Electromyographic (EMG) and Mechanomyographic (MMG) activities of 5 extrinsic muscles of the Hand in their forearm, while simultaneously monitoring 11 Joints of Hand and fingers using a sensorised glove. We used these signals to train a Gaussian Process (GP) and a Vector AutoRegressive Moving Average model with Exogenous inputs (VARMAX) to learn the mapping from current muscle activity and current joint state to predict future Hand configurations. We investigated the performances of both models across tasks, subjects and different Joints for varying time-lags, finding that both models have good generalisation properties and high correlation even for time-lags in the order of hundreds of milliseconds. Our results suggest that regression is a very appealing tool for natural, intuitive and continuous control of robotic devices, with particular focus on prosthetic replacements where high dexterity is required for complex movements.
Michele Xiloyannis - One of the best experts on this subject based on the ideXlab platform.
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gaussian process autoregression for simultaneous proportional multi modal prosthetic control with natural Hand kinematics
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017Co-Authors: Michele Xiloyannis, Constantinos Gavriel, Andreas A C Thomik, Aldo A FaisalAbstract:Matching the dexterity, versatility, and robustness of the human Hand is still an unachieved goal in bionics, robotics, and neural engineering. A major limitation for Hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic Hands. Most of the commercially available prosthetic Hands use muscle-related signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole Hand. Here, in contrast, we aim to offer users flexible control of individual Joints of their artificial Hand. We propose a novel framework for decoding neural information that enables a user to independently control 11 Joints of the Hand in a continuous manner—much like we control our natural Hands. Toward this end, we instructed six able-bodied subjects to perform everyday object manipulation tasks combining both dynamic, free movements (e.g., grasping) and isometric force tasks (e.g., squeezing). We recorded the electromyographic and mechanomyographic activities of five extrinsic muscles of the Hand in the forearm, while simultaneously monitoring 11 Joints of Hand and fingers using a sensorized data glove that tracked the Joints of the Hand. Instead of learning just a direct mapping from current muscle activity to intended Hand movement, we formulated a novel autoregressive approach that combines the context of previous Hand movements with instantaneous muscle activity to predict future Hand movements. Specifically, we evaluated a linear vector autoregressive moving average model with exogenous inputs and a novel Gaussian process ( $\mathcal {GP}$ ) autoregressive framework to learn the continuous mapping from Hand joint dynamics and muscle activity to decode intended Hand movement. Our $\mathcal {GP}$ approach achieves high levels of performance (RMSE of 8°/s and $\rho =0.79$ ). Crucially, we use a small set of sensors that allows us to control a larger set of independently actuated degrees of freedom of a Hand. This novel undersensored control is enabled through the combination of nonlinear autoregressive continuous mapping between muscle activity and joint angles. The system evaluates the muscle signals in the context of previous natural Hand movements. This enables us to resolve ambiguities in situations, where muscle signals alone cannot determine the correct action as we evaluate the muscle signals in their context of natural Hand movements. $\mathcal {GP}$ autoregression is a particularly powerful approach which makes not only a prediction based on the context but also represents the associated uncertainty of its predictions, thus enabling the novel notion of risk-based control in neuroprosthetics. Our results suggest that $\mathcal {GP}$ autoregressive approaches with exogenous inputs lend themselves for natural, intuitive, and continuous control in neurotechnology, with the particular focus on prosthetic restoration of natural limb function, where high dexterity is required for complex movements.
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dynamic forward prediction for prosthetic Hand control by integration of emg mmg and kinematic signals
International IEEE EMBS Conference on Neural Engineering, 2015Co-Authors: Michele Xiloyannis, Constantinos Gavriel, Andreas A C Thomik, Aldo A FaisalAbstract:We propose a new framework for extracting information from extrinsic muscles in the forearm that will allow a continuous, natural and intuitive control of a neuroprosthetic devices and robotic Hands. This is achieved through a continuous mapping between muscle activity and joint angles rather than prior discretisation of Hand gestures. We instructed 6 able-bodied subjects, to perform everyday object manipulation tasks. We recorded the Electromyographic (EMG) and Mechanomyographic (MMG) activities of 5 extrinsic muscles of the Hand in their forearm, while simultaneously monitoring 11 Joints of Hand and fingers using a sensorised glove. We used these signals to train a Gaussian Process (GP) and a Vector AutoRegressive Moving Average model with Exogenous inputs (VARMAX) to learn the mapping from current muscle activity and current joint state to predict future Hand configurations. We investigated the performances of both models across tasks, subjects and different Joints for varying time-lags, finding that both models have good generalisation properties and high correlation even for time-lags in the order of hundreds of milliseconds. Our results suggest that regression is a very appealing tool for natural, intuitive and continuous control of robotic devices, with particular focus on prosthetic replacements where high dexterity is required for complex movements.
Constantinos Gavriel - One of the best experts on this subject based on the ideXlab platform.
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gaussian process autoregression for simultaneous proportional multi modal prosthetic control with natural Hand kinematics
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017Co-Authors: Michele Xiloyannis, Constantinos Gavriel, Andreas A C Thomik, Aldo A FaisalAbstract:Matching the dexterity, versatility, and robustness of the human Hand is still an unachieved goal in bionics, robotics, and neural engineering. A major limitation for Hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic Hands. Most of the commercially available prosthetic Hands use muscle-related signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole Hand. Here, in contrast, we aim to offer users flexible control of individual Joints of their artificial Hand. We propose a novel framework for decoding neural information that enables a user to independently control 11 Joints of the Hand in a continuous manner—much like we control our natural Hands. Toward this end, we instructed six able-bodied subjects to perform everyday object manipulation tasks combining both dynamic, free movements (e.g., grasping) and isometric force tasks (e.g., squeezing). We recorded the electromyographic and mechanomyographic activities of five extrinsic muscles of the Hand in the forearm, while simultaneously monitoring 11 Joints of Hand and fingers using a sensorized data glove that tracked the Joints of the Hand. Instead of learning just a direct mapping from current muscle activity to intended Hand movement, we formulated a novel autoregressive approach that combines the context of previous Hand movements with instantaneous muscle activity to predict future Hand movements. Specifically, we evaluated a linear vector autoregressive moving average model with exogenous inputs and a novel Gaussian process ( $\mathcal {GP}$ ) autoregressive framework to learn the continuous mapping from Hand joint dynamics and muscle activity to decode intended Hand movement. Our $\mathcal {GP}$ approach achieves high levels of performance (RMSE of 8°/s and $\rho =0.79$ ). Crucially, we use a small set of sensors that allows us to control a larger set of independently actuated degrees of freedom of a Hand. This novel undersensored control is enabled through the combination of nonlinear autoregressive continuous mapping between muscle activity and joint angles. The system evaluates the muscle signals in the context of previous natural Hand movements. This enables us to resolve ambiguities in situations, where muscle signals alone cannot determine the correct action as we evaluate the muscle signals in their context of natural Hand movements. $\mathcal {GP}$ autoregression is a particularly powerful approach which makes not only a prediction based on the context but also represents the associated uncertainty of its predictions, thus enabling the novel notion of risk-based control in neuroprosthetics. Our results suggest that $\mathcal {GP}$ autoregressive approaches with exogenous inputs lend themselves for natural, intuitive, and continuous control in neurotechnology, with the particular focus on prosthetic restoration of natural limb function, where high dexterity is required for complex movements.
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dynamic forward prediction for prosthetic Hand control by integration of emg mmg and kinematic signals
International IEEE EMBS Conference on Neural Engineering, 2015Co-Authors: Michele Xiloyannis, Constantinos Gavriel, Andreas A C Thomik, Aldo A FaisalAbstract:We propose a new framework for extracting information from extrinsic muscles in the forearm that will allow a continuous, natural and intuitive control of a neuroprosthetic devices and robotic Hands. This is achieved through a continuous mapping between muscle activity and joint angles rather than prior discretisation of Hand gestures. We instructed 6 able-bodied subjects, to perform everyday object manipulation tasks. We recorded the Electromyographic (EMG) and Mechanomyographic (MMG) activities of 5 extrinsic muscles of the Hand in their forearm, while simultaneously monitoring 11 Joints of Hand and fingers using a sensorised glove. We used these signals to train a Gaussian Process (GP) and a Vector AutoRegressive Moving Average model with Exogenous inputs (VARMAX) to learn the mapping from current muscle activity and current joint state to predict future Hand configurations. We investigated the performances of both models across tasks, subjects and different Joints for varying time-lags, finding that both models have good generalisation properties and high correlation even for time-lags in the order of hundreds of milliseconds. Our results suggest that regression is a very appealing tool for natural, intuitive and continuous control of robotic devices, with particular focus on prosthetic replacements where high dexterity is required for complex movements.
Andreas A C Thomik - One of the best experts on this subject based on the ideXlab platform.
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gaussian process autoregression for simultaneous proportional multi modal prosthetic control with natural Hand kinematics
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017Co-Authors: Michele Xiloyannis, Constantinos Gavriel, Andreas A C Thomik, Aldo A FaisalAbstract:Matching the dexterity, versatility, and robustness of the human Hand is still an unachieved goal in bionics, robotics, and neural engineering. A major limitation for Hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic Hands. Most of the commercially available prosthetic Hands use muscle-related signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole Hand. Here, in contrast, we aim to offer users flexible control of individual Joints of their artificial Hand. We propose a novel framework for decoding neural information that enables a user to independently control 11 Joints of the Hand in a continuous manner—much like we control our natural Hands. Toward this end, we instructed six able-bodied subjects to perform everyday object manipulation tasks combining both dynamic, free movements (e.g., grasping) and isometric force tasks (e.g., squeezing). We recorded the electromyographic and mechanomyographic activities of five extrinsic muscles of the Hand in the forearm, while simultaneously monitoring 11 Joints of Hand and fingers using a sensorized data glove that tracked the Joints of the Hand. Instead of learning just a direct mapping from current muscle activity to intended Hand movement, we formulated a novel autoregressive approach that combines the context of previous Hand movements with instantaneous muscle activity to predict future Hand movements. Specifically, we evaluated a linear vector autoregressive moving average model with exogenous inputs and a novel Gaussian process ( $\mathcal {GP}$ ) autoregressive framework to learn the continuous mapping from Hand joint dynamics and muscle activity to decode intended Hand movement. Our $\mathcal {GP}$ approach achieves high levels of performance (RMSE of 8°/s and $\rho =0.79$ ). Crucially, we use a small set of sensors that allows us to control a larger set of independently actuated degrees of freedom of a Hand. This novel undersensored control is enabled through the combination of nonlinear autoregressive continuous mapping between muscle activity and joint angles. The system evaluates the muscle signals in the context of previous natural Hand movements. This enables us to resolve ambiguities in situations, where muscle signals alone cannot determine the correct action as we evaluate the muscle signals in their context of natural Hand movements. $\mathcal {GP}$ autoregression is a particularly powerful approach which makes not only a prediction based on the context but also represents the associated uncertainty of its predictions, thus enabling the novel notion of risk-based control in neuroprosthetics. Our results suggest that $\mathcal {GP}$ autoregressive approaches with exogenous inputs lend themselves for natural, intuitive, and continuous control in neurotechnology, with the particular focus on prosthetic restoration of natural limb function, where high dexterity is required for complex movements.
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dynamic forward prediction for prosthetic Hand control by integration of emg mmg and kinematic signals
International IEEE EMBS Conference on Neural Engineering, 2015Co-Authors: Michele Xiloyannis, Constantinos Gavriel, Andreas A C Thomik, Aldo A FaisalAbstract:We propose a new framework for extracting information from extrinsic muscles in the forearm that will allow a continuous, natural and intuitive control of a neuroprosthetic devices and robotic Hands. This is achieved through a continuous mapping between muscle activity and joint angles rather than prior discretisation of Hand gestures. We instructed 6 able-bodied subjects, to perform everyday object manipulation tasks. We recorded the Electromyographic (EMG) and Mechanomyographic (MMG) activities of 5 extrinsic muscles of the Hand in their forearm, while simultaneously monitoring 11 Joints of Hand and fingers using a sensorised glove. We used these signals to train a Gaussian Process (GP) and a Vector AutoRegressive Moving Average model with Exogenous inputs (VARMAX) to learn the mapping from current muscle activity and current joint state to predict future Hand configurations. We investigated the performances of both models across tasks, subjects and different Joints for varying time-lags, finding that both models have good generalisation properties and high correlation even for time-lags in the order of hundreds of milliseconds. Our results suggest that regression is a very appealing tool for natural, intuitive and continuous control of robotic devices, with particular focus on prosthetic replacements where high dexterity is required for complex movements.
Amogh Laxman Jambagi - One of the best experts on this subject based on the ideXlab platform.
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EARLY PRESENTATION of LESS COMMON ASSOCIATION- SYSTEMIC SCLEROSIS WITH APLA
Level Up Business Center, 2017Co-Authors: Kalinga Bommanakatte Eranaik, Uday Subhas Bande, Basavaraj Devendrappa Baligar, Varun Bhaktarahalli Renukappa, Amogh Laxman JambagiAbstract:PRESENTATION of CASE A 25-year-old female presented to outpatient department with complaints of pain in small Joints of Hand and on exposure to cold water since 2 months. Patient was nonsmoker and nonalcoholic. Patient was previously treated with analgesics with minimum improvement in symptoms. She noticed small ulcers in Hand and foot since 1 month. Examination revealed painful sclerodactyly involving all fingers solitary ulcer was noted in foot and there was restricted mouth opening, rest of systemic examination was within normal limit
