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1.
Sci Rep ; 14(1): 23248, 2024 10 06.
Article de Anglais | MEDLINE | ID: mdl-39370497

RÉSUMÉ

Wrist function is a top priority for transradial amputees. However, the combined functional, biomechanical, and cognitive impact of using a powered prosthetic wrist is unclear. Here, we quantify task performance, compensatory movements, and cognitive load while three transradial amputees performed a modified Clothespin Relocation Task using two myoelectric prostheses with and without the wrists. The two myoelectric prostheses include a commercial prosthesis with a built-in powered wrist, and a newly developed inexpensive prosthetic wrist for research purposes, called the "Utah wrist", that can be adapted to work with various sockets and prostheses. For these three participants, task failure rate decreased significantly from 66% ± 12% without the wrist to 39% ± 9% with the Utah wrist. Compensatory forward leaning movements also decreased significantly, from 24.2° ± 2.5 without the wrist to 12.6° ± 1.0 with the Utah wrist, and from 23.6° ± 7.6 to 15.3° ± 7.2 with the commercial prosthesis with an integrated wrist. Compensatory leftward bending movements also significantly decreased, from 20.8° ± 8.6 to 12.3° ± 5.3, for the commercial with an integrated wrist. Importantly, simultaneous myoelectric control of either prosthetic wrist had no significant impact on cognitive load, as assessed by the NASA Task Load Index survey and a secondary detection response task. This work suggests that functional prosthetic wrists can improve dexterity and reduce compensation without significantly increasing cognitive effort. These results, and the introduction of a new inexpensive prosthetic wrist for research purposes, can aid future research and development and guide the prescription of upper-limb prostheses.


Sujet(s)
Membres artificiels , Cognition , Électromyographie , Poignet , Humains , Électromyographie/méthodes , Mâle , Poignet/physiologie , Cognition/physiologie , Adulte , Femelle , Amputés , Conception de prothèse , Mouvement/physiologie , Adulte d'âge moyen , Phénomènes biomécaniques
2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2021: 6608-6612, 2021 11.
Article de Anglais | MEDLINE | ID: mdl-34892623

RÉSUMÉ

Commercial prosthetic hands are frequently abandoned due to unintuitive control methods and a lack of sensory feedback from the prosthesis. Advanced neuromyoelectric prostheses can restore intuitive control and sensory feedback to prosthesis users and potentially reduce abandonment. However, not all advanced prosthetic systems are deployable for home use on portable systems with limited computational power. In this work, we use a commercially available portable neural interface processor (the Ripple Neuro Nomad), and a multi-degree-of-freedom bionic arm (the DEKA LUKE Arm) to create a closed-loop neuromyoelectric prosthesis. The system restores intuitive, independent, continuous control over the arm's six-degrees-of-freedom and provides sensory feedback for up to 288 neural and six vibrotactile channels. Additionally, the large storage capacity of the system enables high-resolution logging of EMG, hand positions, prosthesis sensors, and stimulation parameters. We developed two GUIs enabling wireless, real-time adjustments to motor control and feedback parameters: one with nearly full control over motor control and feedback parameters for investigators, and one with restricted capabilities enabling end-user safety. We verified the system's closed-loop function through a fragile egg task with vibrotactile sensory feedback. We tested the neural stimulation with an amplifier capable of eliciting transcutaneous percepts. This neuromyoelectric prosthetic system will be used for an extended take-home trial that could provide strong clinical justification for advanced, closed-loop prostheses.Clinical Relevance- This work establishes an advanced, intuitive, sensorized prosthesis that can be used in home and clinical settings.


Sujet(s)
Membres artificiels , Bionique , Bras , Électromyographie , Conception de prothèse
3.
J Neuroeng Rehabil ; 18(1): 45, 2021 02 25.
Article de Anglais | MEDLINE | ID: mdl-33632237

RÉSUMÉ

BACKGROUND: Advanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not fully utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control using multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm's output. Individual groups often focus on a single decoder, so very few studies compare different decoders using otherwise similar experimental conditions. METHODS: We completed a two-phase, head-to-head comparison of 12 continuous decoders using activities of daily living. In phase one, we compared two training types and a smoothing filter with three algorithms (modified Kalman filter, multi-layer perceptron, and convolutional neural network) in a clothespin relocation task. We compared training types that included only individual digit and wrist movements vs. combination movements (e.g., simultaneous grasp and wrist flexion). We also compared raw vs. nonlinearly smoothed algorithm outputs. In phase two, we compared the three algorithms in fragile egg, zipping, pouring, and folding tasks using the combination training and smoothing found beneficial in phase one. In both phases, we collected objective, performance-based (e.g., success rate), and subjective, user-focused (e.g., preference) measures. RESULTS: Phase one showed that combination training improved prosthesis control accuracy and speed, and that the nonlinear smoothing improved accuracy but generally reduced speed. Phase one importantly showed simultaneous movements were used in the task, and that the modified Kalman filter and multi-layer perceptron predicted more simultaneous movements than the convolutional neural network. In phase two, user-focused metrics favored the convolutional neural network and modified Kalman filter, whereas performance-based metrics were generally similar among all algorithms. CONCLUSIONS: These results confirm that state-of-the-art algorithms, whether linear or nonlinear in nature, functionally benefit from training on more complex data and from output smoothing. These studies will be used to select a decoder for a long-term take-home trial with implanted neuromyoelectric devices. Overall, clinical considerations may favor the mKF as it is similar in performance, faster to train, and computationally less expensive than neural networks.


Sujet(s)
Activités de la vie quotidienne , Membres artificiels , Apprentissage machine , Traitement du signal assisté par ordinateur , Bras/physiologie , Bionique/méthodes , Électromyographie , Humains , Mâle , Mouvement/physiologie , Qualité de vie , Jeune adulte
4.
Front Robot AI ; 7: 559034, 2020.
Article de Anglais | MEDLINE | ID: mdl-33501323

RÉSUMÉ

This paper describes a portable, prosthetic control system and the first at-home use of a multi-degree-of-freedom, proportionally controlled bionic arm. The system uses a modified Kalman filter to provide 6 degree-of-freedom, real-time, proportional control. We describe (a) how the system trains motor control algorithms for use with an advanced bionic arm, and (b) the system's ability to record an unprecedented and comprehensive dataset of EMG, hand positions and force sensor values. Intact participants and a transradial amputee used the system to perform activities-of-daily-living, including bi-manual tasks, in the lab and at home. This technology enables at-home dexterous bionic arm use, and provides a high-temporal resolution description of daily use-essential information to determine clinical relevance and improve future research for advanced bionic arms.

5.
IEEE Trans Neural Syst Rehabil Eng ; 27(10): 2070-2076, 2019 10.
Article de Anglais | MEDLINE | ID: mdl-31536008

RÉSUMÉ

Bypass sockets allow researchers to perform tests of prosthetic systems from the prosthetic user's perspective. We designed a modular upper-limb bypass socket with 3D-printed components that can be easily modified for use with a variety of terminal devices. Our bypass socket preserves access to forearm musculature and the hand, which are necessary for surface electromyography and to provide substituted sensory feedback. Our bypass socket allows a sufficient range of motion to complete tasks in the frontal working area, as measured on non-amputee participants. We examined the performance of non-amputee participants using the bypass socket on the original and modified Box and Block Tests. Participants moved 11.3 ± 2.7 and 11.7 ± 2.4 blocks in the original and modified Box and Block Tests (mean ± SD), respectively, within the range of reported scores using amputee participants. Range of motion for users wearing the bypass socket meets or exceeds most reported range of motion requirements for activities of daily living. The bypass socket was originally designed with a freely rotating wrist; we found that adding elastic resistance to user wrist rotation while wearing the bypass socket had no significant effect on motor decode performance. We have open-sourced the design files and an assembly manual for the bypass socket. We anticipate that the bypass socket will be a useful tool to evaluate and develop sensorized myoelectric prosthesis technology.


Sujet(s)
Membres artificiels , Électromyographie/méthodes , Radius , Amputés , Simulation numérique , Rétroaction sensorielle , Femelle , Volontaires sains , Humains , Mâle , Muscles squelettiques/physiologie , Impression tridimensionnelle , Conception de prothèse , Poignet/physiologie
6.
Nat Med ; 24(12): 1830-1836, 2018 12.
Article de Anglais | MEDLINE | ID: mdl-30297910

RÉSUMÉ

Peripheral nerve injuries represent a significant problem in public health, constituting 2-5% of all trauma cases1. For severe nerve injuries, even advanced forms of clinical intervention often lead to incomplete and unsatisfactory motor and/or sensory function2. Numerous studies report the potential of pharmacological approaches (for example, growth factors, immunosuppressants) to accelerate and enhance nerve regeneration in rodent models3-10. Unfortunately, few have had a positive impact in clinical practice. Direct intraoperative electrical stimulation of injured nerve tissue proximal to the site of repair has been demonstrated to enhance and accelerate functional recovery11,12, suggesting a novel nonpharmacological, bioelectric form of therapy that could complement existing surgical approaches. A significant limitation of this technique is that existing protocols are constrained to intraoperative use and limited therapeutic benefits13. Herein we introduce (i) a platform for wireless, programmable electrical peripheral nerve stimulation, built with a collection of circuit elements and substrates that are entirely bioresorbable and biocompatible, and (ii) the first reported demonstration of enhanced neuroregeneration and functional recovery in rodent models as a result of multiple episodes of electrical stimulation of injured nervous tissue.


Sujet(s)
Stimulation électrique/méthodes , Régénération nerveuse/physiologie , Lésions des nerfs périphériques/thérapie , Cicatrisation de plaie/physiologie , Implant résorbable/normes , Stimulation électrique/instrumentation , Humains , Lésions des nerfs périphériques/physiopathologie , Récupération fonctionnelle , Technologie sans fil
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