Feature-Level Fusion of Surface Electromyography for Activity Monitoring
AbstractSurface 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
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Xi, X.; Tang, M.; Luo, Z. Feature-Level Fusion of Surface Electromyography for Activity Monitoring. Sensors 2018, 18, 614.
Xi X, Tang M, Luo Z. Feature-Level Fusion of Surface Electromyography for Activity Monitoring. Sensors. 2018; 18(2):614.Chicago/Turabian Style
Xi, Xugang; Tang, Minyan; Luo, Zhizeng. 2018. "Feature-Level Fusion of Surface Electromyography for Activity Monitoring." Sensors 18, no. 2: 614.
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