98%
921
2 minutes
20
Motor learning mediated by motor training has in the past been explored for rehabilitation. Myoelectric interfaces together with exoskeletons allow patients to receive real-time feedback about their muscle activity. However, the number of degrees of freedom that can be simultaneously controlled is limited, which hinders the training of functional tasks and the effectiveness of the rehabilitation therapy. The objective of this study was to develop a myoelectric interface that would allow multi-degree-of-freedom control of an exoskeleton involving arm, wrist and hand joints, with an eye toward rehabilitation. We tested the effectiveness of a myoelectric decoder trained with data from one upper limb and mirrored to control a multi-degree-of-freedom exoskeleton with the opposite upper limb (i.e., mirror myoelectric interface) in 10 healthy participants. We demonstrated successful simultaneous control of multiple upper-limb joints by all participants. We showed evidence that subjects learned the mirror myoelectric model within the span of a five-session experiment, as reflected by a significant decrease in the time to execute trials and in the number of failed trials. These results are the necessary precursor to evaluating if a decoder trained with EMG from the healthy limb could foster learning of natural EMG patterns and lead to motor rehabilitation in stroke patients.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8962619 | PMC |
http://dx.doi.org/10.3389/fnins.2022.764936 | DOI Listing |
J Neuroeng Rehabil
February 2025
Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
Background: Phantom limb pain (PLP) is a restrictive condition in which patients perceive pain in a limb that is no longer present, greatly reducing their quality of life. Mirror Therapy, wherein patients observe a mirror reflection of their intact limb, has demonstrated efficacy in alleviating PLP. However, its unilateral and seated nature presents limitations.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
November 2024
IEEE 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 PDFFront Bioeng Biotechnol
April 2024
Health Unit, TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain.
The primary constraint of non-invasive brain-machine interfaces (BMIs) in stroke rehabilitation lies in the poor spatial resolution of motor intention related neural activity capture. To address this limitation, hybrid brain-muscle-machine interfaces (hBMIs) have been suggested as superior alternatives. These hybrid interfaces incorporate supplementary input data from muscle signals to enhance the accuracy, smoothness and dexterity of rehabilitation device control.
View Article and Find Full Text PDFBMJ Mil Health
December 2024
Department of Plastic Surgery, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Upper limb prosthetics have a challenging task. A natural upper limb combines strength, coordination and dexterity to accomplish daily activities such as eating, writing, working and social interaction. Artificially replicating these functions requires a prosthetic with composite, synchronous motor function while maintaining sensory feedback and skeletal stability.
View Article and Find Full Text PDF