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- D'Accolti, D., et al.
(författare)
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Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier
- 2023
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1534-4320 .- 1558-0210. ; 31, s. 208-217
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Tidskriftsartikel (refereegranskat)abstract
- The design of prosthetic controllers bymeans of neurophysiologicalsignals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the transient EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of similar to 96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of similar to 89%. Importantly, for each amputee, it produced at least one acceptable combination of wrist- hand movements (i.e., with accuracy > 85%). Regarding the cross-subject classifier, while our algorithm obtainedpromising resultswith non-amputees (accuracyup to similar to 80%), they were not as good with amputees (accuracy up to similar to 35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments.
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| 2. |
- D'Accolti, D., et al.
(författare)
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Online Classification of Transient EMG Patterns for the Control of the Wrist and Hand in a Transradial Prosthesis
- 2023
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Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766 .- 2377-3774. ; 8:2, s. 1045-1052
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Tidskriftsartikel (refereegranskat)abstract
- Decoding human motor intentions by processing electrophysiological signals is a crucial, yet unsolved, challenge for the development of effective upper limb prostheses. Pattern recognition of continuous myoelectric (EMG) signals represents the state-of-art for multi-DoF prosthesis control. However, this approach relies on the unreliable assumption that repeatable muscular contractions produce repeatable patterns of steady-state EMGs. Here, we propose an approach for decoding wrist and hand movements by processing the signals associated with the onset of contraction (transient EMG). Specifically, we extend the concept of a transient EMG controller for the control of both wrist and hand, and tested it online. We assessed it with one transradial amputee and 15 non-amputees via the Target Achievement Control test. Non-amputees successfully completed 95% of the trials with a median completion time of 17 seconds, showing a significant learning trend (p < 0.001). The transradial amputee completed about the 80% of the trials with a median completion time of 26 seconds. Although the performance proved comparable with earlier studies, the long completion times suggest that the current controller is not yet clinically viable. However, taken collectively, our outcomes reinforce earlier hypothesis that the transient EMG could represent a viable alternative to steady-state pattern recognition approaches.
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| 3. |
- Ortiz Catalan, Max Jair, 1982, et al.
(författare)
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A highly integrated bionic hand with neural control and feedback for use in daily life
- 2023
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Ingår i: Science Robotics. - 2470-9476. ; 8:83
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Tidskriftsartikel (refereegranskat)abstract
- Restoration of sensorimotor function after amputation has remained challenging because of the lack of human-machine interfaces that provide reliable control, feedback, and attachment. Here, we present the clinical implementation of a transradial neuromusculoskeletal prosthesis-a bionic hand connected directly to the user's nervous and skeletal systems. In one person with unilateral below-elbow amputation, titanium implants were placed intramedullary in the radius and ulna bones, and electromuscular constructs were created surgically by transferring the severed nerves to free muscle grafts. The native muscles, free muscle grafts, and ulnar nerve were implanted with electrodes. Percutaneous extensions from the titanium implants provided direct skeletal attachment and bidirectional communication between the implanted electrodes and a prosthetic hand. Operation of the bionic hand in daily life resulted in improved prosthetic function, reduced postamputation, and increased quality of life. Sensations elicited via direct neural stimulation were consistently perceived on the phantom hand throughout the study. To date, the patient continues using the prosthesis in daily life. The functionality of conventional artificial limbs is hindered by discomfort and limited and unreliable control. Neuromusculoskeletal interfaces can overcome these hurdles and provide the means for the everyday use of a prosthesis with reliable neural control fixated into the skeleton.
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