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Sensors 2018, 18(2), 614;

Feature-Level Fusion of Surface Electromyography for Activity Monitoring

School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
Author to whom correspondence should be addressed.
Received: 4 January 2018 / Revised: 1 February 2018 / Accepted: 14 February 2018 / Published: 17 February 2018
(This article belongs to the Special Issue Sensors for Health Monitoring and Disease Diagnosis)
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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. View Full-Text
Keywords: surface electromyography (sEMG); feature-level fusion; monitoring; Davies–Bouldin Index (DBI); support vector machine (SVM) surface electromyography (sEMG); feature-level fusion; monitoring; Davies–Bouldin Index (DBI); support vector machine (SVM)

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Xi, X.; Tang, M.; Luo, Z. Feature-Level Fusion of Surface Electromyography for Activity Monitoring. Sensors 2018, 18, 614.

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