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Intention recognition method for sit-to-stand and stand-to-sit from electromyogram signals for overground lower-limb rehabilitation robots

Title
Intention recognition method for sit-to-stand and stand-to-sit from electromyogram signals for overground lower-limb rehabilitation robots
Authors
Chung, Sang HunLee, Jong MinKim, Seung JongHwang, Yo HaAn, Jin Ung
DGIST Authors
An, Jin Ung
Issue Date
2015
Citation
IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2015, 2015-August, 418-421
Type
Conference
Article Type
Conference Paper
ISBN
9780000000000
Abstract
This paper presents a framework for classifying sit-to-stand and stand-to-sit from just two channel EMG signals taken from the left leg. Our proposed framework uses linear discriminant analysis (LDA) as the classifier and a multi-window feature extraction approach termed Consecutive Time-Windowed Feature Extraction (CTFE). We present the prelimnary results from 2 healthy subjects as a proof of concept. With the two tested subjects, we got predictive accuracies above 90%. The results show promise for a framework capable of recognizing the user's intention of sit-to-stand and stand-to-sit. Potential applications include rehabilitation robots for hemiparesis patients and exoskeleton control. © 2015 IEEE.
URI
http://hdl.handle.net/20.500.11750/1753
DOI
10.1109/AIM.2015.7222568
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
Files:
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Collection:
Convergence Research Center for Wellness2. Conference Papers


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