Myoelectric forearm prostheses: state of the art from a user-centered perspective.

By B. Peerdeman, Daphne W. Boere, Heidi J. B. Witteveen, Rianne Huis in 't Veld, H. Hermens, S. Stramigioli, H. Rietman, Peter H. Veltink, S. Misra. Journal of rehabilitation research and development, 2011

470 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience

View Paper: https://www.semanticscholar.org/paper/9eca7cad983c97a1b51be8e5b5862c6ea4d90381

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In Biomedical Engineering

In Cellular and Molecular Neuroscience

Citations in the corpus

Literature Review on Needs of Upper Limb Prosthesis Users (2016, background): “…prosthesis (Fraser, 1998); the explanation of why several amputees prefer not to use the prosthesis (Fraser, 1993; Kejlaa, 1993; Biddiss and Chau, 2007; Peerdeman et al., 2011); the identification of the problems related to job before and after amputation (Heger et al., 1985; Wright et al., 1995).”
Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands (2016, background): “However, average results are usually below 80–90% (Peerdeman et al., 2011).”
Challenges in Task Incremental Learning for Assistive Robotics (2020, background): “The ability of lifelong/continual learning is, however, essential in particular to assistive robotics for elderlies or patients with disabilities [1], [2].”
EMG feature evaluation for improving myoelectric pattern recognition robustness (2013, methodology): “The success of this system to achieve a high classification performance depends almost entirely on the selection of EMG features (Oskoei & Hu, 2007, 2008).”
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning (2018, background): “Even though more recent studies suggest that the latency should optimally be kept between 100-250 ms [31], [32], the performance of the classiﬁer should take priority over speed [31], [33].”