1,721,178 research outputs found
Ultrasound Imaging for Hand Prosthesis Control: a Comparative Study of Features and Classification Methods
No full textControlling a robotic rehabilitation artefact such as a hand prosthesis is yet a rather open problem. Particularly, the choice of a human-machine interface (HMI) to enable natural control is still debatable. The traditional choice, i.e. surface electromyography (sEMG), suffers from a number of problems (electrode displacement, sweat, fatigue) which cannot be easily solved. One of its main drawbacks is the inherent low spatial resolution, at least in the standard settings. To overcome this hindrance, several novel HMIs have been proposed to substitute or augment sEMG; among them, pressure and tactile sensing, and ultrasound imaging (US). In this paper we propose an advancement towards the usage of US as a HMI for hand prosthetics; namely, we compare traditional US image features with Histograms of Oriented Gradients used as input for three classifiers, and show that a high number of hand configurations and grasping force levels can be classified way above chance level by choosing the right combination of features and classifier. In an experiment involving three intact human subjects, a classification accuracy of 80% was obtained; when classifying three different levels of grip force for four grasps, the performance reduces to 60%. These results confirm the usability of US imaging as a HMI for hand prosthetics, and pave the way to its practical usage as a means of natural prosthetic control
Mechatronic designs for a robotic hand to explore human body experience and sensory-motor skills: a Delphi study
Full text linkTo bridge the gap between users' expectations and technological solutions, a better understanding of human body experience and sensory-motor skills is mandatory. This could pave the way towards a novel generation of robotic hands, which can be successfully employed in everyday life e.g. in prosthetics and assistive robotics. Available robotic hands are still far from matching the requirements of the corresponding experimental and real-world applications, e.g. fast motions might be achieved at the expense of accuracy. Knowledge of the users' sensory-motor skills can guide technical developments, e.g. prosthetic design processes. This paper presents design solutions developed in a Delphi study. Explorative questionnaires are prepared to acquire and elaborate expert opinions to improve the design of previously developed robotic anthropomorphic hands. By gathering and fusing expert opinions, novel robotic hand and wrist concepts specifically optimized regarding body experience and sensory-motor skill research are developed. In three rounds, experts with experience in robotic hand design and/or control analyze, develop, and rank solutions for mechanisms, actuators, and control, which result in overall design concepts. The technical concepts and implications resulting from the study are discussed considering psychological and biomechanical aspects
Improving Control of Dexterous Hand Prostheses Using Adaptive Learning
Full text linkAt the time of this writing, the main means of control for polyarticulated self-powered hand prostheses is surface electromyography (sEMG). In the clinical setting, data collected from two electrodes are used to guide the hand movements selecting among a finite number of postures. Machine learning has been applied in the past to the sEMG signal (not in the clinical setting) with interesting results, which provide more insight on how these data could be used to improve prosthetic functionality. Researchers have mainly concentrated so far on increasing the accuracy of sEMG classification and/or regression, but, in general, a finer control implies a longer training period. A desirable characteristic would be to shorten the time needed by a patient to learn how to use the prosthesis. To this aim, we propose here a general method to reuse past experience, in the form of models synthesized from previous subjects, to boost the adaptivity of the prosthesis. Extensive tests on databases recorded from healthy subjects in controlled and non-controlled conditions reveal that the method significantly improves the results over the baseline nonadaptive case. This promising approach might be employed to pretrain a prosthesis before shipping it to a patient, leading to a shorter training phase.LIDIA
Multichannel electrotactile feedback for simultaneous and proportional myoelectric control
No full textObjective. Closing the loop in myoelectric prostheses by providing artificial somatosensory feedback to the user is an important need for prosthetic users. Previous studies investigated feedback strategies in combination with the control of one degree of freedom of simple grippers. Modern hands, however, are sophisticated multifunction systems. In this study, we assessed multichannel electrotactile feedback integrated with an advanced method for the simultaneous and proportional control of individual fingers of a dexterous hand. Approach. The feedback used spatial and frequency coding to provide information on the finger positions (normalized flexion angles). A comprehensive set of conditions have been investigated in 28 able-bodied subjects, including feedback modalities (visual, electrotactile and no feedback), control tasks (fingers and grasps), systems (virtual and real hand), control methods (ideal and realistic) and range of motion (low and high). The task for the subjects was to operate the hand using closed-loop myoelectric control and generate the desired movement (e.g., selected finger or grasp at a specific level of closure). Main results. The subjects could perceive the multichannel and multivariable electrotactile feedback and effectively exploit it to improve the control performance with respect to open-loop grasping. The improvement however depended on the reliability of the feedforward control, with less consistent control exhibiting performance trends that were more complex across the conditions. Significance. The results are promising for the potential application of advanced feedback to close the control loop in sophisticated prosthetic systems
Towards improving myocontrol of prosthetic hands: a study on automated instability detection
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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