Background: This research aims to improve the control of assistive devices for individuals with hemiparesis after stroke by providing intuitive and proportional motor control. Stroke is the leading cause of disability in the United States, with 80% of stroke-related disability coming in the form of hemiparesis, presented as weakness or paresis on half of the body. Current assistive exoskeletonscontrolled via electromyography do not allow for fine force regulation.
View Article and Find Full Text PDFNeuroprostheses typically operate under supervised learning, in which a machine-learning algorithm is trained to correlate neural or myoelectric activity with an individual's motor intent. Due to the stochastic nature of neuromyoelectric signals, algorithm performance decays over time. This decay is accelerated when attempting to regress proportional control of multiple joints in parallel, compared with the more typical classification-based pattern recognition control.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
May 2024
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 this study, we quantified the accuracy and precision of labeling hand kinematics using two common training paradigms: 1) mimic training, where participants mimic predetermined motions of a prosthesis, and 2) mirror training, where participants mirror their contralateral intact hand during synchronized bilateral movements.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2023
Stroke is the leading cause of disability worldwide, and nearly 80% of stroke survivors suffer from upper-limb hemiparesis. Myoelectric exoskeletons can restore dexterity and independence to stroke survivors with upper-limb hemiparesis. However, the ability of patients to dexterously control myoelectric exoskeletons is limited by an incomplete understanding of the electromyographic (EMG) hallmarks of hemiparesis, such as muscle weakness and spasticity.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
November 2021
Upper-limb prosthetic control is often challenging and non-intuitive, leading to up to 50% of prostheses users abandoning their prostheses. Convolutional neural networks (CNN) and recurrent long short-term memory (LSTM) networks have shown promise in extracting high-degree-of-freedom motor intent from myoelectric signals, thereby providing more intuitive and dexterous prosthetic control. An important next consideration for these algorithms is if performance remains stable over multiple days.
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