Towards near real-time assessment of surgical skills: A comparison of feature extraction techniques

2020 ◽  
Vol 187 ◽  
pp. 105234 ◽  
Author(s):  
Nguyen Xuan Anh ◽  
Ramesh Mark Nataraja ◽  
Sunita Chauhan
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Surgical Skills ◽  
Extraction Techniques ◽  
Time Assessment

scholarly journals Colour Feature Extraction Techniques for Real Time System of Oil Palm Fresh Fruit Bunch Maturity Grading

2020 ◽  
Vol 540 ◽  
pp. 012092
Author(s):  
Meftah Salem M Alfatni ◽  
Abdul Rashid Mohamed Shariff ◽  
Osama M. Ben Saaed ◽  
Atia Mahmod Albhbah ◽  
Aouache Mustapha
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Oil Palm ◽  
Fresh Fruit ◽  
Extraction Techniques ◽  
Time System ◽  
Fresh Fruit Bunch ◽  
Real Time System ◽  
Colour Feature

scholarly journals Design and Implementation of Electromyography (EMG) based Real-Time Pattern Recognition model for Prosthetic hand Control

2021 ◽  
Author(s):  
Pasan Yashoda Jayaweera
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Window Size ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Extraction Techniques ◽  
Time Pattern ◽  
Feed Forward ◽  
Prosthetic Devices ◽  
Detection Techniques

Surface electromyogram (EMG) signals are a key component in myoelectric control systems utilized in modern prosthetic devices. Despite extensive study into EMG gesture detection techniques for hand and arm gestures, most prosthetic devices rely on direct control approaches that are often confined to single movements. The purpose of this paper is to investigate various feature extraction techniques and to compare various machine learning algorithms, window sizes to identify the most suitable algorithm, window size for real-time gesture recognition. For this purpose, a publicly available pre-labeled 2-channel EMG dataset was used as EMG signals. Feature sets for each window size were extracted using various feature extraction techniques and fed into support vector machines, k-nearest neighbors, ensemble learning, and feed-forward artificial neural network (ANN) classifiers. The feed-forward neural networks classifier was determined to be the best classifier based on its accuracies, sizes, and prediction delays for each window size. The maximum accuracy of the feed-forward ANN classifier was ≈87% with a 300-millisecond window size. the use of the majority voting technique was considered in terms of the number of votes and the window sizes.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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