15 November 2017 Surface EMG signals based motion intent recognition using multi-layer ELM
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Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106051H (2017) https://doi.org/10.1117/12.2288037
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China
Abstract
The upper-limb rehabilitation robot is regard as a useful tool to help patients with hemiplegic to do repetitive exercise. The surface electromyography (sEMG) contains motion information as the electric signals are generated and related to nerve-muscle motion. These sEMG signals, representing human’s intentions of active motions, are introduced into the rehabilitation robot system to recognize upper-limb movements. Traditionally, the feature extraction is an indispensable part of drawing significant information from original signals, which is a tedious task requiring rich and related experience. This paper employs a deep learning scheme to extract the internal features of the sEMG signals using an advanced Extreme Learning Machine based auto-encoder (ELMAE). The mathematical information contained in the multi-layer structure of the ELM-AE is used as the high-level representation of the internal features of the sEMG signals, and thus a simple ELM can post-process the extracted features, formulating the entire multi-layer ELM (ML-ELM) algorithm. The method is employed for the sEMG based neural intentions recognition afterwards. The case studies show the adopted deep learning algorithm (ELM-AE) is capable of yielding higher classification accuracy compared to the Principle Component Analysis (PCA) scheme in 5 different types of upper-limb motions. This indicates the effectiveness and the learning capability of the ML-ELM in such motion intent recognition applications.
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Jianhui Wang, Jianhui Wang, Lin Qi, Lin Qi, Xiao Wang, Xiao Wang, } "Surface EMG signals based motion intent recognition using multi-layer ELM", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106051H (15 November 2017); doi: 10.1117/12.2288037; https://doi.org/10.1117/12.2288037
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