Feature-Level Fusion of Surface Electromyography for Activity Monitoring

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.

EMG Pattern Recognition in the Era of Big Data and Deep Learning

“…The development of EMG feature extraction and classification methods that are robust to noise is also important [64,65], as is the reduction of data (or dimensionality) when dealing with large-scale data sets. Determining relevant and meaningful features from a given larger set of features which may contain irrelevant, redundant, or noisy information is commonly accomplished using either feature selection [66][67][68][69] or feature projection methods [70][71][72][73]. When properly executed, these methods not only reduce the impact of noise and irrelevant information, but also the amount of computational time required for classification.…”

“…For feature selection, some potential and well-known population-based metaheuristic methods, such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO), have been found to be effective in selecting an optimal EMG feature set (e.g., [67][68][69]). These feature selection methods have been developed to work in parallel computing as well as on graphics processing units (GPU) [87,88].…”

A Review of EMG Techniques for Detection of Gait Disorders

“…Kim et al [70] identified synergies using iterative K-mean clustering and intraclass correlation. Hierarchical, model-based, fuzzy c means clustering has been employed to group gait patterns [69,[71][72][73]. Dolatabadi et al [71] used mixture model clustering on spatiotemporal gait pattern to classify pathological gait.…”

“…Pathological disorders such as cerebral palsy that show higher inter-stride variability can be analyzed with a hierarchical clustering method proposed by Rosati et al [72]. Feature Fusion technique with Davies Bouldin Index (DBI) based on fuzzy C means algorithm was used in a trip/fall study [73]. The DBI can be used to evaluate the clustering algorithm.…”

“…classification accuracy [74,75]. Feature level fusion is used to extract the feature space from daily life activities [73]. Patients with Parkinson's were classified with high accuracy using SVM with leave-one-out cross-validation [75].…”