NDLI: Feature-Level Fusion of Surface Electromyography for Activity Monitoring

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Xugang Xi Minyan Tang Zhizeng Luo
Abstract	Surface electromyography (sEMG) signals are commonly used in activity monitoring and rehabilitation applications as they reflect effectively the motor intentions of users. However, real-time sEMG signals are non-stationary and vary to a large extent within the time frame of signals. Although previous studies have focused on the issues, their results have not been satisfactory. Therefore, we present a new method of conducting feature-level fusion to obtain a new feature space for sEMG signals. Eight activities of daily life (ADLs), including falls, were performed to obtain raw data from EMG signals from the lower limb. A feature set combining the time domain, time–frequency domain, and entropy domain was applied to the raw data to establish an initial feature space. A new projection method, the weighting genetic algorithm for GCCA (WGA-GCCA), was introduced to obtain the final feature space. Different tests were carried out to evaluate the performance of the new feature space. The new feature space created with the WGA-GCCA effectively reduced the dimensions and selected the best feature vectors dynamically while improving monotonicity. The Davies–Bouldin index (DBI) based on fuzzy c-means algorithms of the space obtained the lowest value compared with several fusion methods. It also achieved the highest accuracy when applied to support vector machine classifier.
e-ISSN	14248220
DOI	10.3390/s18020614
Journal	Sensors
Issue Number	2
Volume Number	18
Language	English
Publisher	MDPI AG
Publisher Date	2018-02-01
Publisher Place	Switzerland
Access Restriction	Open
Subject Keyword	Chemical Technology Surface Electromyography (semg) Feature-level Fusion Monitoring Davies–bouldin Index (dbi) Support Vector Machine (svm)
Content Type	Text
Resource Type	Article

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