| 1. |
- Farina, D., et al.
(författare)
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Toward higher-performance bionic limbs for wider clinical use
- 2021
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Ingår i: Nature Biomedical Engineering. - : Springer Science and Business Media LLC. - 2157-846X. ; 7:4, s. 473-85
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Tidskriftsartikel (refereegranskat)abstract
- Most prosthetic limbs can autonomously move with dexterity, yet they are not perceived by the user as belonging to their own body. Robotic limbs can convey information about the environment with higher precision than biological limbs, but their actual performance is substantially limited by current technologies for the interfacing of the robotic devices with the body and for transferring motor and sensory information bidirectionally between the prosthesis and the user. In this Perspective, we argue that direct skeletal attachment of bionic devices via osseointegration, the amplification of neural signals by targeted muscle innervation, improved prosthesis control via implanted muscle sensors and advanced algorithms, and the provision of sensory feedback by means of electrodes implanted in peripheral nerves, should all be leveraged towards the creation of a new generation of high-performance bionic limbs. These technologies have been clinically tested in humans, and alongside mechanical redesigns and adequate rehabilitation training should facilitate the wider clinical use of bionic limbs. This Perspective argues that technologies for the neural interfacing of robotic devices with the body that have been clinically tested in humans should be leveraged toward the creation of a new generation of high-performance bionic limbs.
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| 2. |
- Muceli, Silvia, 1981, et al.
(författare)
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A biologically-inspired robust control system for myoelectric control
- 2017
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Ingår i: Biosystems & Biorobotics. - Cham : Springer International Publishing. ; , s. 975-979
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- We review recent studies that aimed at designing an intuitive and robust myoelectric control system for transradial amputees. The methods developed assume that the forearm muscles are controlled in a synergistic manner and capture this synergistic structure hidden in the electromyographic signal patterns by factorization algorithms. We have shown that this system is capable of providing robust control over multiple degrees of freedom relying on 6 electrodes only and that it is robust to electrode shift. However, a pure factorization approach may result in some unwanted movements when the user is willing to activate only one function, which is mitigated by combining a synergistic controller with pattern recognition.
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