A robust system that classifies various hand gestures would greatly help those using prosthetic limbs. Recently, emphasis has been placed on extracted features from the High Density - surface Electromyography (HD-sEMG) signals and the size of segmentation windows which augment the recognition accuracy. This paper proposes a hand gestures identification system, in which HD-sEMG signals are employed, and is supported by Force Myography (FMG) signals for this mission. Several feature types have been extracted from FMG and HD-sEMG signals such as MEAN, RMS, MAD, STD, and Variance, these features have been validated under some classifiers such as decision tree (DT), linear discriminant analysis (LDA), support vector machine SVM, and k-nearest neighbor (KNN), in which results showing that MEAN and RMS features are superior to others, while the best classifier is SVM. Several experiments have been achieved by the MATLAB platform to validate the proposed system, in which, a database of HD-sEMG signals comprising 65 isometric hand gestures is employed, where two (8×8) electrodes and 9 force sensors are used to collect the FMG data. This data was derived from 20 intact participants, the first preprocessing step was applied during the recording stage. Ten gestures are chosen to be classified from the 65 hand gestures. Results show the success of the proposed system while the classification accuracy arrived at 99.1%.
In recent years, the number of researches in the field of artificial limbs has increased significantly in order to improve the performance of the use of these limbs by amputees. During this period, High-Density surface Electromyography (HD-sEMG) signals have been employed for hand gesture identification, in which the performance of the classification process can be improved by using robust spatial features extracted from HD-sEMG signals. In this paper, several algorithms of spatial feature extraction have been proposed to increase the accuracy of the SVM classifier, while the histogram oriented gradient (HOG) has been used to achieve this mission. So, several feature sets have been extracted from HD-sEMG signals such as; features extracted based on HOG denoted by (H); features have been generated by combine intensity feature with H features denoted as (HI); features have been generated by combine average intensity with H features denoted as (AIH). The proposed system has been simulated by MATLAB to calculate the accuracy of the classification process, in addition, the proposed system is practically validated in order to show the ability to use this system by amputees. The results show the high accuracy of the classifier in real-time which leads to an increase in the possibility of using this system as an artificial hand.
With the recent developments of technology and the advances in artificial intelligence and machine learning techniques, it has become possible for the robot to understand and respond to voice as part of Human-Robot Interaction (HRI). The voice-based interface robot can recognize the speech information from humans so that it will be able to interact more naturally with its human counterpart in different environments. In this work, a review of the voice-based interface for HRI systems has been presented. The review focuses on voice-based perception in HRI systems from three facets, which are: feature extraction, dimensionality reduction, and semantic understanding. For feature extraction, numerous types of features have been reviewed in various domains, such as time, frequency, cepstral (i.e. implementing the inverse Fourier transform for the signal spectrum logarithm), and deep domains. For dimensionality reduction, subspace learning can be used to eliminate the redundancies of high-dimensional features by further processing extracted features to reflect their semantic information better. For semantic understanding, the aim is to infer from the extracted features the objects or human behaviors. Numerous types of semantic understanding have been reviewed, such as speech recognition, speaker recognition, speaker gender detection, speaker gender and age estimation, and speaker localization. Finally, some of the existing voice-based interface issues and recommendations for future works have been outlined.
In this paper a radial distribution feeder protection scheme against short circuit faults is introduced. It is based on utilizing the substation measured current signals in detecting faults and obtaining useful information about their types and locations. In order to facilitate important measurement signals features extraction such that better diagnosis of faults can be achieved, the discrete wavelet transform is exploited. The captured features are then utilized in detecting, identifying the faulted phases (fault type), and fault location. In case of a fault occurrence, the detection scheme will make a decision to trip out a circuit breaker residing at the feeder mains. This decision is made based on a criteria that is set to distinguish between the various system states in a reliable and accurate manner. After that, the fault type and location are predicted making use of the cascade forward neural networks learning and generalization capabilities. Useful information about the fault location can be obtained provided that the fault distance from source, as well as whether it resides on the main feeder or on one of the laterals can be predicted. By testing the functionality of the proposed scheme, it is found that the detection of faults is done fastly and reliably from the view point of power system protection relaying requirements. It also proves to overcome the complexities provided by the feeder structure to the accuracy of the identification process of fault types and locations. All the simulations and analysis are performed utilizing MATLAB R2016b version software package.