A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal

Abstract

Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy.

Ensemble Wavelet Decomposition-Based Detection of Mental States Using Electroencephalography Signals

“…The current work explores the application of 27 statistical and nonlinear features to detect three mental states. These features are the standard deviation, Hurst exponent, average energy, wavelength, V order, skewness, kurtosis, Hjorth mobility, Higuchi fractal dimension, Lyapunov exponent, differential absolute standard deviation value, absolute value of the summation of an exponential root, absolute value of the sum of square root, normalized first difference, normalized second difference, mean value of the square root, difference variance value, log energy, absolute energy, simple square integral, slope sign change, peak amplitude, minima, peak amplitude, zero crossing rate, interquartile range, and trimean [ 46 , 47 , 48 , 49 , 50 ].…”

Feature Extraction Based on Sparse Coding Approach for Hand Grasp Type Classification

“…This can help improve recognition accuracy. Finally, deep-learning based methods [23,24] have recently emerged as a powerful tool for hand-based biometric recognition. These methods have shown promising performance for capturing discriminative features from hand images.…”

“…For example, most feature extraction techniques are specific to hand kinematic datasets derived from hand image datasets. Additionally, some techniques are only suitable for specific hand analyses using EMG signals [24,25] and are difficult to extend to other types of hand kinematic data. Moreover, most of the traditional feature extraction methods are focused on extracting features based on subspace-learningbased approaches while not adequately exploiting the consistency and complementary information among the other types of hand kinematic data, especially hand kinematic time-series formats.…”

LST-EMG-Net: Long short-term transformer feature fusion network for sEMG gesture recognition

“…Numerous gesture recognition studies have used traditional classifiers for manual features. For example, support vector machines (SVMs) (Alseed and Tasoglu, 2022;Briouza et al, 2022), k-nearest neighbors (KNN) (Baygin et al, 2022), linear discriminant analysis (LDA) (Narayan, 2021), hidden Markov models (HMMs) (Hu and Wang, 2020), and random forests (RF) (Xue et al, 2019;Jia et al, 2021) have made some progress. However, the accuracy and stability of traditional learning algorithms do not yet satisfy practical application requirements when applied to large-scale datasets consisting of larger numbers of hand gestures or wrist movements.…”