Validity and Impact of Methods for Collecting Training Data for Myoelectric Prosthetic Control Algorithms

Intuitive regression control of prostheses relies on training algorithms to correlate biological recordings to motor intent. The quality of the training dataset is critical to run-time regression performance, but accurately labeling intended hand kinematics after hand amputation is challenging. In t...

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Veröffentlicht in:	IEEE transactions on neural systems and rehabilitation engineering 2024, Vol.32, p.1974-1983
Hauptverfasser:	Tully, Troy N., Thomson, Caleb J., Clark, Gregory A., George, Jacob A.
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Algorithms Amputation Amputees - rehabilitation Artificial Limbs Biomechanical Phenomena Biomechanics brain-computer interfaces Control algorithms Datasets Electromyography Electromyography - methods EMG Female Hand - physiology Healthy Volunteers Humans Kinematics Labeling Labelling Machine learning Male Mirrors Motion capture Movement - physiology myoelectric prostheses Myoelectricity Neural networks Neural Networks, Computer Neural prostheses neuroprostheses Nonlinear Dynamics Prostheses Prosthesis Design Prosthetic control prosthetic hand Prosthetics Regression Reproducibility of Results Training Training data Young Adult
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