Elicitation hybrid spatial features from HD-sEMG signals for robust classification of gestures in real-time

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.

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Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

Scientific Reports ◽

10.1038/s41598-021-89365-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Song-Quan Ong ◽

Hamdan Ahmad ◽

Gomesh Nair ◽

Pradeep Isawasan ◽

Abdul Hafiz Ab Majid

Keyword(s):

Deep Learning ◽

Aedes Aegypti ◽

Real Time ◽

Aedes Albopictus ◽

Automated Classification ◽

Expert Performance ◽

Deep Convolutional Neural Networks ◽

Significant Difference ◽

Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

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