BROWSE

Related Researcher

Author's Photo

Kang, Sang Hoon
Robotics and Rehab. Engineering Lab (R2EL)
Research Interests
  • Rehabilitation Robotics & Mechatronic Tools, Biomechanics for Rehabilitation, Human Limb Impedance Estimation, Assistive and Healthcare robotics, Robust Motion/Force Control

ITEM VIEW & DOWNLOAD

EMG-Based Real-Time Linear-Nonlinear Cascade Regression Decoding of Shoulder, Elbow and Wrist Movements in Able-Bodied Persons and Stroke Survivors

Cited 0 times inthomson ciCited 0 times inthomson ci
Title
EMG-Based Real-Time Linear-Nonlinear Cascade Regression Decoding of Shoulder, Elbow and Wrist Movements in Able-Bodied Persons and Stroke Survivors
Author
Liu, JieRen, YupengXu, DaliKang, Sang HoonZhang, Li-Qun
Issue Date
2020-05
Publisher
Institute of Electrical and Electronics Engineers
Citation
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, v.67, no.5, pp.1272 - 1281
Abstract
Objective: This study aimed to decode shoulder, elbow and wrist dynamic movements continuously and simultaneously based on multi-channel surface electromyography signals, useful for electromyography controlled exoskeleton robots for upper-limb rehabilitation. Methods: Ten able-bodied subjects and ten stroke subjects were instructed to voluntarily move the shoulder, elbow and wrist joints back and forth in a horizontal plane with an exoskeleton robot. The shoulder, elbow and wrist movements and surface electromyography signals from six muscles crossing the joints were recorded. A set of three parallel linear-nonlinear cascade decoders was developed to continuously estimate the selected shoulder, elbow and wrist movements based on a generalized linear model using the anterior deltoid, posterior deltoid, biceps brachii, long head triceps brachii, flexor carpi radialis, and extensor carpi radialis muscle electromyography signals as the model inputs. Results: The decoder performed well for both healthy and stroke populations. As movement smoothness decreased, decoding performance decreased for the stroke population. Conclusion: The proposed method is capable of simultaneously and continuously estimating multi-joint movements of the human arm in real-time by characterizing the nonlinear mappings between muscle activity and kinematic signals based on linear regression. Significance: This may prove useful in developing myoelectric controlled exoskeletons for motor rehabilitation of neurological disorders.
URI
https://scholarworks.unist.ac.kr/handle/201301/27282
URL
https://ieeexplore.ieee.org/document/8795589
DOI
10.1109/tbme.2019.2935182
ISSN
0018-9294
Appears in Collections:
MNE_Journal Papers
Files in This Item:
There are no files associated with this item.

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record

qrcode

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU