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      Multi-Modal Sensing Techniques for Interfacing Hand Prostheses: A Review

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          High-performance neuroprosthetic control by an individual with tetraplegia.

          Paralysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain-machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface. We implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain-machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. This study is registered with ClinicalTrials.gov, NCT01364480. The participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91·6% (SD 4·4) versus median chance level 6·2% (95% CI 2·0-15·3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s [6]) and path efficiency (increased from 0·30 [0·04] to 0·38 [0·02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported. With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living. Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute. Copyright © 2013 Elsevier Ltd. All rights reserved.
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            A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems.

            Considerable scientific and technological efforts have been devoted to develop neuroprostheses and hybrid bionic systems that link the human nervous system with electronic or robotic prostheses, with the main aim of restoring motor and sensory functions in disabled patients. A number of neuroprostheses use interfaces with peripheral nerves or muscles for neuromuscular stimulation and signal recording. Herein, we provide a critical overview of the peripheral interfaces available and trace their use from research to clinical application in controlling artificial and robotic prostheses. The first section reviews the different types of non-invasive and invasive electrodes, which include surface and muscular electrodes that can record EMG signals from and stimulate the underlying or implanted muscles. Extraneural electrodes, such as cuff and epineurial electrodes, provide simultaneous interface with many axons in the nerve, whereas intrafascicular, penetrating, and regenerative electrodes may contact small groups of axons within a nerve fascicle. Biological, technological, and material science issues are also reviewed relative to the problems of electrode design and tissue injury. The last section reviews different strategies for the use of information recorded from peripheral interfaces and the current state of control neuroprostheses and hybrid bionic systems.
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              Myoelectric control systems—A survey

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                Author and article information

                Journal
                IEEE Sensors Journal
                IEEE Sensors J.
                Institute of Electrical and Electronics Engineers (IEEE)
                1530-437X
                1558-1748
                2379-9153
                November 2015
                November 2015
                : 15
                : 11
                : 6065-6076
                Article
                10.1109/JSEN.2015.2450211
                c4127021-eba9-4066-9f5b-7576a9518670
                © 2015
                History

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