The Experts below are selected from a list of 885 Experts worldwide ranked by ideXlab platform
Linda Resnik - One of the best experts on this subject based on the ideXlab platform.
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Attrition and retention in Upper Limb Prosthetics research: experience of the VA home study of the DEKA arm.
Disability and rehabilitation. Assistive technology, 2017Co-Authors: Linda Resnik, Shana Lieberman KlingerAbstract:AbstractPurpose: (1) Describe study attrition; (2) identify reasons for attrition, and (3) discuss implications for prosthetic prescription and design of future device studies.Design and methodological procedures used: Completion phase (during in-laboratory training, after training, or home use) was identified for 42 participants. Qualitative data were analyzed to identify attrition reasons. Reasons were classified as related to the DEKA arm, or not.Results: Study attrition was 57%, with 43% completing the full study. Attrition during the in-laboratory portion was 21%. Reasons for attrition were related to the DEKA arm entirely or in-part for 42%, 25%, respectively. Most common reasons were scheduling/personal (54%); device weight (29%); and dissatisfaction with device (25%). About 21% withdrew because of concerns about compliance with study protocol.Conclusions: This study had a high attrition rate with evidence of selective attrition due to device characteristics. Strategies to minimize attrition and th...
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Advanced Upper Limb Prosthetic Devices: Implications for Upper Limb Prosthetic Rehabilitation
Archives of physical medicine and rehabilitation, 2012Co-Authors: Linda Resnik, Marissa R. Meucci, Shana Lieberman-klinger, Christopher Fantini, Debra L. Kelty, Roxanne Disla, Nicole SassonAbstract:The number of catastrophic injuries caused by improvised explosive devices in the Afghanistan and Iraq Wars has increased public, legislative, and research attention to Upper Limb amputation. The Department of Veterans Affairs (VA) has partnered with the Defense Advanced Research Projects Agency and DEKA Integrated Solutions to optimize the function of an advanced prosthetic arm system that will enable greater independence and function. In this special communication, we examine current practices in prosthetic rehabilitation including trends in adoption and use of prosthetic devices, financial considerations, and the role of rehabilitation team members in light of our experiences with a prototype advanced Upper Limb prosthesis during a VA study to optimize the device. We discuss key challenges in the adoption of advanced prosthetic technology and make recommendations for service provision and use of advanced Upper Limb Prosthetics. Rates of prosthetic rejection are high among Upper Limb amputees. However, these rates may be reduced with sufficient training by a highly specialized, multidisciplinary team of clinicians, and a focus on patient education and empowerment throughout the rehabilitation process. There are significant challenges emerging that are unique to implementing the use of advanced Upper Limb prosthetic technology, and a lack of evidence to establish clinical guidelines regarding prosthetic prescription and treatment. Finally, we make recommendations for future research to aid in the identification of best practices and development of policy decisions regarding insurance coverage of prosthetic rehabilitation.
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Development and testing of new Upper-Limb prosthetic devices: research designs for usability testing.
Journal of rehabilitation research and development, 2011Co-Authors: Linda ResnikAbstract:The purposes of this article are to describe usability testing and introduce designs and methods of usability testing research as it relates to Upper-Limb Prosthetics. This article defines usability, describes usability research, discusses research approaches to and designs for usability testing, and highlights a variety of methodological considerations, including sampling, sample size requirements, and usability metrics. Usability testing is compared with other types of study designs used in prosthetic research.
Nathanaël Jarrassé - One of the best experts on this subject based on the ideXlab platform.
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Phantom-mobility-based prosthesis control in transhumeral amputees without surgical reinnervation: a preliminary study.
Frontiers in Bioengineering and Biotechnology, 2018Co-Authors: Nathanaël Jarrassé, Etienne De Montalivet, Caroline Nicol, Noël Martinet, Jean Paysant, Amélie Touillet, Florent Richer, Jozina De GraafAbstract:Transhumeral amputees face substantial difficulties in efficiently controlling their prosthetic Limb, leading to a high rate of rejection of these devices. Actual myoelectric control approaches make their use slow, sequential and unnatural, especially for these patients with a high level of amputation who need a prosthesis with numerous active degrees of freedom (powered elbow, wrist, and hand). While surgical muscle-reinnervation is becoming a generic solution for amputees to increase their control capabilities over a prosthesis, research is still being conducted on the possibility of using the surface myoelectric patterns specifically associated to voluntary Phantom Limb Mobilization (PLM), appearing naturally in most Upper-Limb amputees without requiring specific surgery. The objective of this study was to evaluate the possibility for transhumeral amputees to use a PLM-based control approach to perform more realistic functional grasping tasks. Two transhumeral amputated participants were asked to repetitively grasp one out of three different objects with an unworn eight-active-DoF prosthetic arm and release it in a dedicated drawer. The prosthesis control was based on phantom Limb mobilization and myoelectric pattern recognition techniques, using only two repetitions of each PLM to train the classification architecture. The results show that the task could be successfully achieved with rather optimal strategies and joint trajectories, even if the completion time was increased in comparison with the performances obtained by a control group using a simple GUI control, and the control strategies required numerous corrections. While numerous limitations related to robustness of pattern recognition techniques and to the perturbations generated by actual wearing of the prosthesis remain to be solved, these preliminary results encourage further exploration and deeper understanding of the phenomenon of natural residual myoelectric activity related to PLM, since it could possibly be a viable option in some transhumeral amputees to extend their control abilities of functional Upper Limb Prosthetics with multiple active joints without undergoing muscular reinnervation surgery.
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A simple movement based control approach to ease the control of a myoelectric elbow Prosthetics in transhumeral amputees
2018Co-Authors: Nathanaël Jarrassé, Etienne De Montalivet, Florian Richer, Noël Martinet, D. Muller, M Merad, Amélie Touillet, Jean PaysantAbstract:Transhumeral amputees face substantial difficulties in efficiently controlling their prosthetic Limb, leading to a high rate of rejection of these devices. Actual myoelectric control approaches make their use slow, sequential and unnatural, especially for these patients with a high level of amputation who need a prosthesis with numerous active degrees of freedom (powered elbow, wrist, and hand). While surgical muscle-reinnervation is becoming a generic solution for amputees to increase their control capabilities over a prosthesis, research is still being conducted on the possibility of using the surface myoelectric patterns specifically associated to voluntary Phantom Limb Mobilization (PLM), appearing naturally in most Upper-Limb amputees without requiring specific surgery. The objective of this study was to evaluate the possibility for transhumeral amputees to use a PLM-based control approach to perform more realistic functional grasping tasks. Two transhumeral amputated participants were asked to repetitively grasp one out of three different objects with an unworn eight-active-DoF prosthetic arm and release it in a dedicated drawer. The prosthesis control was based on phantom Limb mobilization and myoelectric pattern recognition techniques, using only two repetitions of each PLM to train the classification architecture. The results show that the task could be successfully achieved with rather optimal strategies and joint trajectories, even if the completion time was increased in comparison with the performances obtained by a control group using a simple GUI control, and the control strategies required numerous corrections. While numerous limitations related to robustness of pattern recognition techniques and to the perturbations generated by actual wearing of the prosthesis remain to be solved, these preliminary results encourage further exploration and deeper understanding of the phenomenon of natural residual myoelectric activity related to PLM, since it could possibly be a viable option in some transhumeral amputees to extend their control abilities of functional Upper Limb Prosthetics with multiple active joints without undergoing muscular reinnervation surgery.
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Movement-Based Control for Upper-Limb Prosthetics: Is the Regression Technique the Key to a Robust and Accurate Control?
Frontiers Media S.A., 2018Co-Authors: Mathilde Legrand, Manelle Merad, Etienne De Montalivet, Agnès Roby-brami, Nathanaël JarrasséAbstract:Due to the limitations of myoelectric control (such as dependence on muscular fatigue and on electrodes shift, difficulty in decoding complex patterns or in dealing with simultaneous movements), there is a renewal of interest in the movement-based control approaches for Prosthetics. The latter use residual Limb movements rather than muscular activity as command inputs, in order to develop more natural and intuitive control techniques. Among those, several research works rely on the interjoint coordinations that naturally exist in human Upper Limb movements. These relationships are modeled to control the distal joints (e.g., elbow) based on the motions of proximal ones (e.g., shoulder). The regression techniques, used to model the coordinations, are various [Artificial Neural Networks, Principal Components Analysis (PCA), etc.] and yet, analysis of their performance and impact on the prosthesis control is missing in the literature. Is there one technique really more efficient than the others to model interjoint coordinations? To answer this question, we conducted an experimental campaign to compare the performance of three common regression techniques in the control of the elbow joint on a transhumeral prosthesis. Ten non-disabled subjects performed a reaching task, while wearing an elbow prosthesis which was driven by several interjoint coordination models obtained through different regression techniques. The models of the shoulder-elbow kinematic relationship were built from the recordings of fifteen different non-disabled subjects that performed a similar reaching task with their healthy arm. Among Radial Basis Function Networks (RBFN), Locally Weighted Regression (LWR), and PCA, RBFN was found to be the most robust, based on the analysis of several criteria including the quality of generated movements but also the compensatory strategies exhibited by users. Yet, RBFN does not significantly outperform LWR and PCA. The regression technique seems not to be the most significant factor for improvement of interjoint coordinations-based control. By characterizing the impact of the modeling techniques through closed-loop experiments with human users instead of purely offline simulations, this work could also help in improving movement-based control approaches and in bringing them closer to a real use by patients
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Classification of Phantom Finger, Hand, Wrist and Elbow Voluntary Gestures in Transhumeral Amputees with sEMG
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017Co-Authors: Nathanaël Jarrassé, Caroline Nicol, A. Touillet, Florian Richer, Noël Martinet, Jean Paysant, Jozina B. De GraafAbstract:Decoding finger and hand movements from sEMG electrodes placed on the forearm of transradial amputees has been commonly studied by many research groups. A few recent studies have shown an interesting phenomenon: simple correlations between distal phantom finger, hand and wrist voluntary movements and muscle activity in the residual Upper arm in transhumeral amputees, i.e., of muscle groups that, prior to amputation, had no physical effect on the concerned hand and wrist joints. In this study, we are going further into the exploration of this phenomenon by setting up an evaluation study of phantom finger, hand, wrist and elbow (if present) movement classification based on the analysis of surface electromyographic (sEMG) signals measured by multiple electrodes placed on the residual Upper arm of five transhumeral amputees with a controllable phantom Limb who did not undergo any reinnervation surgery. We showed that with a state-of-the-art classification architecture, it is possible to correctly classify phantom Limb activity (up to 14 movements) with a rather important average success (over 80% if considering basic sets of six hand, wrist and elbow movements) and to use this pattern recognition output to give online control of a device (here a graphical interface) to these transhumeral amputees. Beyond changing the way the phantom Limb condition is apprehended by both patients and clinicians, such results could pave the road towards a new control approach for transhumeral amputated patients with a voluntary controllable phantom Limb. This could ease and extend their control abilities of functional Upper Limb Prosthetics with multiple active joints without undergoing muscular reinnervation surgery.
Dario Farina - One of the best experts on this subject based on the ideXlab platform.
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live demonstration system based on electronic skin and cutaneous electrostimulation for sensory feedback in Prosthetics
Biomedical Circuits and Systems Conference, 2018Co-Authors: Mohamad Alameh, M Saleh, Ali Ibrahim, Maurizio Valle, Flavio Ansovini, Hoda Fares, Marta Franceschi, Lucia Seminara, Strahinja Dosen, Dario FarinaAbstract:To restore the sense of touch in Upper Limb Prosthetics, a prosthetic device can be equipped with tactile sensors providing data to be transmitted to the user using either invasive or non-invasive interfaces. This demo will be based on our sensing - noninvasive stimulation feedback system [1]. It will show two important aspects of our technology: 1) High sensitivity: light touch detection will be enabled by the high sensitivity of electronic skin (e-skin) prototypes for fingertips, 2) Measuring complex interactions: different contact shapes and multiple contact points will be detected by the commercial e- skin prototype suitable for palm.
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myocontrol is closed loop control incidental feedback is sufficient for scaling the prosthesis force in routine grasping
Journal of Neuroengineering and Rehabilitation, 2018Co-Authors: Marko Markovic, Dario Farina, Strahinja Dosen, Meike A Schweisfurth, Leonard F EngelsAbstract:Sensory feedback is critical for grasping in able-bodied subjects. Consequently, closing the loop in Upper-Limb Prosthetics by providing artificial sensory feedback to the amputee is expected to improve the prosthesis utility. Nevertheless, even though amputees rate the prospect of sensory feedback high, its benefits in daily life are still very much debated. We argue that in order to measure the potential functional benefit of artificial sensory feedback, the baseline open-loop performance needs to be established. The myoelectric control of naive able-bodied subjects was evaluated during modulation of electromyographic signals (EMG task), and grasping with a prosthesis (Prosthesis task). The subjects needed to activate the wrist flexor muscles and close the prosthesis to reach a randomly selected target level (routine grasping). To assess the baseline performance, the tasks were performed with a different extent of implicit feedback (proprioception, prosthesis motion and sound). Finally, the prosthesis task was repeated with explicit visual force feedback. The subjects’ ability to scale the prosthesis command/force was assessed by testing for a statistically significant increase in the median of the generated commands/forces between neighboring levels. The quality of control was evaluated by computing the median absolute error (MAE) with respect to the target. The subjects could successfully scale their motor commands and generated prosthesis forces across target levels in all tasks, even with the least amount of implicit feedback (only muscle proprioception, EMG task). In addition, the deviation of the generated commands/forces from the target levels decreased with additional feedback. However, the increase in implicit feedback, from proprioception to prosthesis motion and sound, seemed to have a more substantial effect than the final introduction of explicit feedback. Explicit feedback improved the performance mainly at the higher target-force levels. The study establishes the baseline performance of myoelectric control and prosthesis grasping force. The results demonstrate that even without additional feedback, naive subjects can effectively modulate force with good accuracy with respect to that achieved when increasing the amount of feedback information.
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Myocontrol is closed-loop control: incidental feedback is sufficient for scaling the prosthesis force in routine grasping
Journal of NeuroEngineering and Rehabilitation, 2018Co-Authors: Marko Markovic, Dario Farina, Meike A Schweisfurth, Leonard F Engels, Strahinja DosenAbstract:Background Sensory feedback is critical for grasping in able-bodied subjects. Consequently, closing the loop in Upper-Limb Prosthetics by providing artificial sensory feedback to the amputee is expected to improve the prosthesis utility. Nevertheless, even though amputees rate the prospect of sensory feedback high, its benefits in daily life are still very much debated. We argue that in order to measure the potential functional benefit of artificial sensory feedback, the baseline open-loop performance needs to be established. Methods The myoelectric control of naïve able-bodied subjects was evaluated during modulation of electromyographic signals ( EMG task ), and grasping with a prosthesis ( Prosthesis task ). The subjects needed to activate the wrist flexor muscles and close the prosthesis to reach a randomly selected target level (routine grasping). To assess the baseline performance, the tasks were performed with a different extent of implicit feedback (proprioception, prosthesis motion and sound). Finally, the prosthesis task was repeated with explicit visual force feedback. The subjects’ ability to scale the prosthesis command/force was assessed by testing for a statistically significant increase in the median of the generated commands/forces between neighboring levels. The quality of control was evaluated by computing the median absolute error (MAE) with respect to the target. Results The subjects could successfully scale their motor commands and generated prosthesis forces across target levels in all tasks, even with the least amount of implicit feedback (only muscle proprioception, EMG task). In addition, the deviation of the generated commands/forces from the target levels decreased with additional feedback. However, the increase in implicit feedback, from proprioception to prosthesis motion and sound, seemed to have a more substantial effect than the final introduction of explicit feedback. Explicit feedback improved the performance mainly at the higher target-force levels. Conclusions The study establishes the baseline performance of myoelectric control and prosthesis grasping force. The results demonstrate that even without additional feedback, naïve subjects can effectively modulate force with good accuracy with respect to that achieved when increasing the amount of feedback information.
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3d printed Upper Limb Prosthetics
Expert Review of Medical Devices, 2018Co-Authors: Ivan Vujaklija, Dario FarinaAbstract:Introduction: In the last 15 years, the market for prosthetic arms and hands has shifted toward systems with greater degrees of actuation. There has also been a progressive use of emerging technolo...
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Clinical Evaluation of a Socket-Ready Naturally Controlled Multichannel Upper Limb Prosthetic System
Biosystems & Biorobotics, 2016Co-Authors: Ivan Vujaklija, Dario Farina, Sebastian Amsuess, Aidan D. Roche, Oskar C. AszmannAbstract:Research conducted over the last decades indicates a necessity of having larger number of EMG sensors in order to extract sufficient information needed for natural control of Upper Limb Prosthetics. Various studies have addressed this issue, though clinical transition and evaluation of such systems on a larger pool of patients is still missing. We propose a specifically designed system which allows users to perform clinically relevant tests in an unobstructed way while handling dexterous prosthesis. Eight electrodes were embedded into customized sockets along with the controllers driving an algorithm recently tested in laboratory conditions that allows simultaneous manipulation of four out of seven prosthetic functions. The fully self-contained system was evaluated on seven amputees conducting the Southampton Hand Assessment Procedure. The scores achieved were compared to those obtained using their own commercial devices. The study shows the necessary steps to validate novel control algorithms in a clinically meaningful context.
Andres Ubeda - One of the best experts on this subject based on the ideXlab platform.
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evaluation of optimal vibrotactile feedback for force controlled Upper Limb myoelectric prostheses
Sensors, 2019Co-Authors: Andrea Gonzalezrodriguez, Jose L Ramon, Vicente Morell, Gabriel J Garcia, Jorge Pomares, Carlos A Jara, Andres UbedaAbstract:The main goal of this study is to evaluate how to optimally select the best vibrotactile pattern to be used in a closed loop control of Upper Limb myoelectric prostheses as a feedback of the exerted force. To that end, we assessed both the selection of actuation patterns and the effects of the selection of frequency and amplitude parameters to discriminate between different feedback levels. A single vibrotactile actuator has been used to deliver the vibrations to subjects participating in the experiments. The results show no difference between pattern shapes in terms of feedback perception. Similarly, changes in amplitude level do not reflect significant improvement compared to changes in frequency. However, decreasing the number of feedback levels increases the accuracy of feedback perception and subject-specific variations are high for particular participants, showing that a fine-tuning of the parameters is necessary in a real-time application to Upper Limb Prosthetics. In future works, the effects of training, location, and number of actuators will be assessed. This optimized selection will be tested in a real-time proportional myocontrol of a prosthetic hand.
Strahinja Dosen - One of the best experts on this subject based on the ideXlab platform.
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live demonstration system based on electronic skin and cutaneous electrostimulation for sensory feedback in Prosthetics
Biomedical Circuits and Systems Conference, 2018Co-Authors: Mohamad Alameh, M Saleh, Ali Ibrahim, Maurizio Valle, Flavio Ansovini, Hoda Fares, Marta Franceschi, Lucia Seminara, Strahinja Dosen, Dario FarinaAbstract:To restore the sense of touch in Upper Limb Prosthetics, a prosthetic device can be equipped with tactile sensors providing data to be transmitted to the user using either invasive or non-invasive interfaces. This demo will be based on our sensing - noninvasive stimulation feedback system [1]. It will show two important aspects of our technology: 1) High sensitivity: light touch detection will be enabled by the high sensitivity of electronic skin (e-skin) prototypes for fingertips, 2) Measuring complex interactions: different contact shapes and multiple contact points will be detected by the commercial e- skin prototype suitable for palm.
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myocontrol is closed loop control incidental feedback is sufficient for scaling the prosthesis force in routine grasping
Journal of Neuroengineering and Rehabilitation, 2018Co-Authors: Marko Markovic, Dario Farina, Strahinja Dosen, Meike A Schweisfurth, Leonard F EngelsAbstract:Sensory feedback is critical for grasping in able-bodied subjects. Consequently, closing the loop in Upper-Limb Prosthetics by providing artificial sensory feedback to the amputee is expected to improve the prosthesis utility. Nevertheless, even though amputees rate the prospect of sensory feedback high, its benefits in daily life are still very much debated. We argue that in order to measure the potential functional benefit of artificial sensory feedback, the baseline open-loop performance needs to be established. The myoelectric control of naive able-bodied subjects was evaluated during modulation of electromyographic signals (EMG task), and grasping with a prosthesis (Prosthesis task). The subjects needed to activate the wrist flexor muscles and close the prosthesis to reach a randomly selected target level (routine grasping). To assess the baseline performance, the tasks were performed with a different extent of implicit feedback (proprioception, prosthesis motion and sound). Finally, the prosthesis task was repeated with explicit visual force feedback. The subjects’ ability to scale the prosthesis command/force was assessed by testing for a statistically significant increase in the median of the generated commands/forces between neighboring levels. The quality of control was evaluated by computing the median absolute error (MAE) with respect to the target. The subjects could successfully scale their motor commands and generated prosthesis forces across target levels in all tasks, even with the least amount of implicit feedback (only muscle proprioception, EMG task). In addition, the deviation of the generated commands/forces from the target levels decreased with additional feedback. However, the increase in implicit feedback, from proprioception to prosthesis motion and sound, seemed to have a more substantial effect than the final introduction of explicit feedback. Explicit feedback improved the performance mainly at the higher target-force levels. The study establishes the baseline performance of myoelectric control and prosthesis grasping force. The results demonstrate that even without additional feedback, naive subjects can effectively modulate force with good accuracy with respect to that achieved when increasing the amount of feedback information.
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Myocontrol is closed-loop control: incidental feedback is sufficient for scaling the prosthesis force in routine grasping
Journal of NeuroEngineering and Rehabilitation, 2018Co-Authors: Marko Markovic, Dario Farina, Meike A Schweisfurth, Leonard F Engels, Strahinja DosenAbstract:Background Sensory feedback is critical for grasping in able-bodied subjects. Consequently, closing the loop in Upper-Limb Prosthetics by providing artificial sensory feedback to the amputee is expected to improve the prosthesis utility. Nevertheless, even though amputees rate the prospect of sensory feedback high, its benefits in daily life are still very much debated. We argue that in order to measure the potential functional benefit of artificial sensory feedback, the baseline open-loop performance needs to be established. Methods The myoelectric control of naïve able-bodied subjects was evaluated during modulation of electromyographic signals ( EMG task ), and grasping with a prosthesis ( Prosthesis task ). The subjects needed to activate the wrist flexor muscles and close the prosthesis to reach a randomly selected target level (routine grasping). To assess the baseline performance, the tasks were performed with a different extent of implicit feedback (proprioception, prosthesis motion and sound). Finally, the prosthesis task was repeated with explicit visual force feedback. The subjects’ ability to scale the prosthesis command/force was assessed by testing for a statistically significant increase in the median of the generated commands/forces between neighboring levels. The quality of control was evaluated by computing the median absolute error (MAE) with respect to the target. Results The subjects could successfully scale their motor commands and generated prosthesis forces across target levels in all tasks, even with the least amount of implicit feedback (only muscle proprioception, EMG task). In addition, the deviation of the generated commands/forces from the target levels decreased with additional feedback. However, the increase in implicit feedback, from proprioception to prosthesis motion and sound, seemed to have a more substantial effect than the final introduction of explicit feedback. Explicit feedback improved the performance mainly at the higher target-force levels. Conclusions The study establishes the baseline performance of myoelectric control and prosthesis grasping force. The results demonstrate that even without additional feedback, naïve subjects can effectively modulate force with good accuracy with respect to that achieved when increasing the amount of feedback information.
