Bibliografías temáticas / Transhumeral prosthesis control

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Literatura académica sobre el tema "Transhumeral prosthesis control"

Autor: Grafiati
Publicado: 19 de octubre de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Transhumeral prosthesis control"

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de Backer-Bes, Femke, Maaike Lange, Michael Brouwers, and Iris van Wijk. "De Hoogstraat Xperience Prosthesis Transhumeral: An Innovative Test Prosthesis." JPO Journal of Prosthetics and Orthotics 36, no. 3 (2024): 193–97. http://dx.doi.org/10.1097/jpo.0000000000000510.

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ABSTRACT Introduction To choose a suitable prosthesis, clients need to experience both the weight and the control of a prosthesis. A few years ago, De Hoogstraat Rehabilitation Center developed the Xperience Prosthesis for children and adults with a transradial congenital or acquired limb deficiency. Because of the positive effects, we developed a reusable test prosthesis for the transhumeral level. Xperience Prosthesis Transhumeral is an innovative test prosthesis and an essential tool in managing expectations when providing clients with a suitable upper-limb prosthesis. Xperience Prosthesis
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Tereshenko, Vlad, Riccardo Giorgino, Kyle R. Eberlin, et al. "Emerging Value of Osseointegration for Intuitive Prosthetic Control after Transhumeral Amputations: A Systematic Review." Plastic and Reconstructive Surgery - Global Open 12, no. 5 (2024): e5850. http://dx.doi.org/10.1097/gox.0000000000005850.

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Background: Upper extremity limb loss profoundly impacts a patient’s quality of life and well-being and carries a significant societal cost. Although osseointegration allows the attachment of the prosthesis directly to the bone, it is a relatively recent development as an alternative to conventional socket prostheses. The objective of this review was to identify reports on osseointegrated prosthetic embodiment for transhumeral amputations and assess the implant systems used, postoperative outcomes, and complications. Methods: A systematic review following PRISMA and AMSTAR guidelines assessed
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Sattar, Neelum Yousaf, Zareena Kausar, Syed Ali Usama, et al. "fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees." Sensors 22, no. 3 (2022): 726. http://dx.doi.org/10.3390/s22030726.

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Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy
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Molina Arias, Ludwin, Marek Iwaniec, Paulina Pirowska, Magdalena Smoleń, and Piotr Augustyniak. "Head and Voice-Controlled Human-Machine Interface System for Transhumeral Prosthesis." Electronics 12, no. 23 (2023): 4770. http://dx.doi.org/10.3390/electronics12234770.

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The design of artificial limbs is a research topic that has, over time, attracted considerable interest from researchers in various fields of study, such as mechanics, electronics, robotics, and neuroscience. Continuous efforts are being made to build electromechanical systems functionally equivalent to the original limbs and to develop strategies to control them appropriately according to the intentions of the user. The development of Human–Machine Interfaces (HMIs) is a key point in the development of upper limb prostheses, since the actions carried out with the upper limbs lack fixed patter
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Alshammary, Nasser A., Daniel A. Bennett, and Michael Goldfarb. "Synergistic Elbow Control for a Myoelectric Transhumeral Prosthesis." IEEE Transactions on Neural Systems and Rehabilitation Engineering 26, no. 2 (2018): 468–76. http://dx.doi.org/10.1109/tnsre.2017.2781719.

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Ahmed, Muhammad Hannan, Jiazheng Chai, Shingo Shimoda, and Mitsuhiro Hayashibe. "Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion." Sensors 23, no. 9 (2023): 4188. http://dx.doi.org/10.3390/s23094188.

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Transhumeral amputees experience considerable difficulties with controlling a multifunctional prosthesis (powered hand, wrist, and elbow) due to the lack of available muscles to provide electromyographic (EMG) signals. The residual limb motion strategy has become a popular alternative for transhumeral prosthesis control. It provides an intuitive way to estimate the motion of the prosthesis based on the residual shoulder motion, especially for target reaching tasks. Conventionally, a predictive model, typically an artificial neural network (ANN), is directly trained and relied upon to map the s
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OʼShaughnessy, Kristina D., Gregory A. Dumanian, Robert D. Lipschutz, Laura A. Miller, Kathy Stubblefield, and Todd A. Kuiken. "Targeted Reinnervation to Improve Prosthesis Control in Transhumeral Amputees." Journal of Bone & Joint Surgery 90, no. 2 (2008): 393–400. http://dx.doi.org/10.2106/jbjs.g.00268.

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Nsugbe, Ejay, Oluwarotimi Williams Samuel, Mojisola Grace Asogbon, and Guanglin Li. "A Self-Learning and Adaptive Control Scheme for Phantom Prosthesis Control Using Combined Neuromuscular and Brain-Wave Bio-Signals." Engineering Proceedings 2, no. 1 (2020): 59. http://dx.doi.org/10.3390/ecsa-7-08169.

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The control scheme in a myoelectric prosthesis includes a pattern recognition section whose task is to decode an input signal, produce a respective actuation signal and drive the motors in the prosthesis limb towards the completion of the user’s intended gesture motion. The pattern recognition architecture works with a classifier which is typically trained and calibrated offline with a supervised learning framework. This method involves the training of classifiers which form part of the pattern recognition scheme, but also induces additional and often undesired lead time in the prosthesis desi
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Nsugbe, Ejay, Carol Phillips, Mike Fraser, and Jess McIntosh. "Gesture recognition for transhumeral prosthesis control using EMG and NIR." IET Cyber-Systems and Robotics 2, no. 3 (2020): 122–31. http://dx.doi.org/10.1049/iet-csr.2020.0008.

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Hebert, Jacqueline S., K. Ming Chan, and Michael R. Dawson. "Cutaneous sensory outcomes from three transhumeral targeted reinnervation cases." Prosthetics and Orthotics International 40, no. 3 (2016): 303–10. http://dx.doi.org/10.1177/0309364616633919.

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Background: Although targeted muscle reinnervation has been shown to be effective in enhancing prosthetic control for upper limb amputees, restored hand sensations have been variable. An understanding of possible sensory feedback channels is crucial in working toward more effective closed-loop prosthetic control. Objectives: To compare sensory outcomes of different targeted sensory reinnervation approaches. Study design: Case series, cross-sectional, and retrospective. Methods: Three transhumeral amputees that had undergone different sensory reinnervation approaches were recruited. Skin pressu
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Tesis sobre el tema "Transhumeral prosthesis control"

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Mérad, Manelle. "Investigations on upper limb prosthesis control with an active elbow." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066615/document.

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Les progrès de la mécatronique ont permis d’améliorer les prothèses du membre supérieur en augmentant le catalogue des mouvements prothétiques. Cependant, un fossé se creuse entre les capacités technologiques de la prothèse et leur méthode de contrôle. La commande myoélectrique, qui est la méthode la plus répandue, reste complexe, notamment pour les personnes amputées au niveau trans-huméral qui peuvent avoir un coude actif en plus de la main et du poignet motorisés. Une approche intéressante consiste à utiliser la mobilité du membre résiduel, présente chez la plupart des amputés trans-humérau
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Lento, Bianca. "Contrôle biomimétique de prothèse à partir de mouvements naturels : base de données et transformation de référentiel pour une situation réelle." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0183.

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Les contrôles myoélectriques pour les prothèses transhumérales entraînent souvent un taux élevé d’abandon en raison de leurs performances insatisfaisantes. Inspirés des progrès réalisés dans les contrôles exploitant les mouvements résiduels, nous avons affiné une approche alternative utilisant un réseau de neurones artificiels entrainé sur les mouvements naturels de bras pour prédire la configuration des articulations distales en fonction du mouvement des articulations proximales et d’information sur l’objet à saisir. Des études antérieures ont montré que cette stratégie permet aux amputés de
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Capítulos de libros sobre el tema "Transhumeral prosthesis control"

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Brånemark, Rickard. "Advanced Prosthetic Control in Transhumeral Amputees Using Osseointegration and Bidirectional Neuromuscular Interfaces." In Biosystems & Biorobotics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08072-7_6.

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Barron, Olivier, Maxime Raison, and Sofiane Achiche. "Control of transhumeral prostheses based on electromyography pattern recognition: from amputees to deep learning." In Powered Prostheses. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-817450-0.00001-8.

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Ali Syed, Usama, Zareena Kausar, and Neelum Yousaf Sattar. "Control of a Prosthetic Arm Using fNIRS, a Neural-Machine Interface." In Data Acquisition - Recent Advances and Applications in Biomedical Engineering [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93565.

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Development in the field of bio-mechatronics has provided diverse ways to mimic and improve the function of human limbs. Without an elbow joint, the hand remains stiff because all the muscles tension passes through this joint. Advanced myoelectric prosthetic devices are limited due to the lack of appropriate signal sources on residual amputee muscles and insufficient real-time control. Neural-machine interfaces (NMI) are representing a recent approach to develop effective applications. In this research study, an NMI is designed that presents real-time signal processing for command generation.
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Actas de conferencias sobre el tema "Transhumeral prosthesis control"

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Sittiwanchai, Teppakorn, Ippei Nakayama, Shinichi Inoue, and Jun Kobayashi. "Transhumeral prosthesis prototype with 3D printing and sEMG-based elbow joint control method." In 2014 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE, 2014. http://dx.doi.org/10.1109/icamechs.2014.6911655.

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Nguyen, Phuong Duy, and Chi Thanh Pham. "Towards a modular and dexterous transhumeral prosthesis based on bio-signals and active vision." In 2019 IEEE International Symposium on Measurement and Control in Robotics (ISMCR). IEEE, 2019. http://dx.doi.org/10.1109/ismcr47492.2019.8955664.

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Ruhunage, Isuru, Sanjaya Mallikarachchi, Dulith Chinthaka, Janith Sandaruwan, and Thilina Dulantha Lalitharatne. "Hybrid EEG-EMG Signals Based Approach for Control of Hand Motions of a Transhumeral Prosthesis." In 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech). IEEE, 2019. http://dx.doi.org/10.1109/lifetech.2019.8883865.

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Bakshi, Koushik, Rajesh Pramanik, M. Manjunatha, and C. S. Kumar. "Upper Limb Prosthesis Control: A Hybrid EEG-EMG Scheme for Motion Estimation in Transhumeral Subjects." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8512678.

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Jarrasse, Nathanael, Caroline Nicol, Florian Richer, et al. "Voluntary phantom hand and finger movements in transhumerai amputees could be used to naturally control polydigital prostheses." In 2017 International Conference on Rehabilitation Robotics (ICORR). IEEE, 2017. http://dx.doi.org/10.1109/icorr.2017.8009419.

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