Cognitive Ergonomics Evaluation Assisted by an Intelligent Emotion Recognition Technique

In recent times there is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer assisted devices. Surface Electromyogram (sEMG) is a non-invasive measure of the muscle activities but is not reliable because there are multiple simultaneously active muscles. This research first establishes the conditions for the applicability of Independent Component Analysis (ICA) pattern recognition techniques for sEMG. Shortcomings related to order and magnitude ambiguity have been identified and a mitigation strategy has been developed by using a set of unmixing matrix and neural network weight matrix corresponding to the specific user. The experimental results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user.

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ICA as Pattern Recognition Technique for Gesture Identification

Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-61350-429-1.ch020 ◽

2012 ◽

pp. 367-387

Author(s):

Ganesh Naik ◽

Dinesh Kant Kumar ◽

Sridhar Arjunan

Keyword(s):

Pattern Recognition ◽

Marked Improvement ◽

Mitigation Strategy ◽

The Other ◽

Computer Assisted ◽

Pattern Recognition Techniques ◽

Non Invasive ◽

Robust System ◽

Network Weight ◽

Recognition Technique

In recent times there is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer assisted devices. Surface Electromyogram (sEMG) is a non-invasive measure of the muscle activities but is not reliable because there are multiple simultaneously active muscles. This research first establishes the conditions for the applicability of Independent Component Analysis (ICA) pattern recognition techniques for sEMG. Shortcomings related to order and magnitude ambiguity have been identified and a mitigation strategy has been developed by using a set of unmixing matrix and neural network weight matrix corresponding to the specific user. The experimental results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user.

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Emotion Detection using Facial Recognition Technique

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2174104 ◽

2021 ◽

pp. 413-417

Author(s):

Ritvik Tiwari ◽

Rudra Thorat ◽

Vatsal Abhani ◽

Shakti Mahapatro

Keyword(s):

Deep Learning ◽

Facial Expression ◽

Emotion Recognition ◽

Facial Expressions ◽

Facial Recognition ◽

Research Field ◽

Emotion Detection ◽

Human Machine Interfaces ◽

Different Types ◽

Recognition Technique

Emotion recognition based on facial expression is an intriguing research field, which has been presented and applied in various spheres such as safety, health and in human machine interfaces. Researchers in this field are keen in developing techniques that can prove to be an aid to interpret, decode facial expressions and then extract these features in order to achieve a better prediction by the computer. With advancements in deep learning, the different types of prospects of this technique are exploited to achieve a better performance. We spotlight these contributions, the architecture and the databases used and present the progress made by comparing the proposed methods and the results obtained. The interest of this paper is to guide the technology enthusiasts by reviewing recent works and providing insights to make improvements to this field.

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Classification of saline water for irrigated agriculture using near infrared spectroscopy coupled with pattern recognition techniques

E3S Web of Conferences ◽

10.1051/e3sconf/201911600043 ◽

2019 ◽

Vol 116 ◽

pp. 00043

Author(s):

Ravipat Lapcharoensuk ◽

Jirawat Phuphanutada ◽

Patthranit Wongpromrat

Keyword(s):

Pattern Recognition ◽

Water Samples ◽

Near Infrared ◽

Saline Water ◽

Nir Spectroscopy ◽

Test Method ◽

Pattern Recognition Technique ◽

Pattern Recognition Techniques ◽

Recognition Technique

This research aimed to create near infrared (NIR) spectroscopy models for the classification of saline water with a pattern recognition technique. A total of 112 water samples were collected from the Tha Chin river basin in Thailand. Water samples with salinity less than 0.2 g/l were identified as suitable for agriculture, while water samples with salinity higher than 0.2 g/l were found to be unsuitable. The NIR spectra of water samples were recorded using a Fourier transform (FT) NIR spectrometer in the wavenumber of 12,500–4,000 cm-1. The salinity of each water sample was analysed by electrical conductivity meter. Identification models were established with 5 supervised pattern recognition techniques including k-nearest neighbour (k-NN), support vector machine (SVM), artificial neural network (ANN), soft independent modelling of class analogies (SIMCA), and partial least squares-discriminant analysis (PLS-DA). The performance of the NIR model was carried out with a split-test method. About 80% of spectra (90 spectra) were randomly selected to develop the classification models. After model development, the NIR spectroscopy models were used to classify the categories of the remaining samples (22 samples). The ANN model showed the highest performance for classifying saline water with precision, recall, F-measure and accuracy of 84.6%, 100.0%, 91.7% and 90.9%, respectively. Other techniques presented satisfactory classification results with accuracy greater than 68.2%. This point indicated that NIR spectroscopy coupled with the pattern recognition technique could be applied to classify saline water for agricultural use according to salinity level in natural resources.

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Interpretation of Infrared Spectra Using Pattern Recognition Techniques

Applied Spectroscopy ◽

10.1366/000370273774333254 ◽

1973 ◽

Vol 27 (5) ◽

pp. 371-376 ◽

Cited By ~ 31

Author(s):

Robert W. Liddell ◽

Peter C. Jurs

Keyword(s):

Pattern Recognition ◽

Feature Selection ◽

Ir Spectra ◽

Infrared Spectra ◽

Training Procedure ◽

Pattern Recognition Technique ◽

Pattern Recognition Techniques ◽

Recognition Technique

The pattern recognition technique utilizing adaptive binary pattern classifiers has been applied to the interpretation of infrared spectra. The binary pattern classifiers have been trained to determine the chemical classes of x-y digitized infrared spectra. High predictive abilities have been obtained in classifying unknown spectra. A new training procedure for binary pattern classifiers has been developed, and it has been used to classify ir spectra into chemical classes. Pattern classifiers trained in the conventional way and by the new procedure have been used in conjunction with feature selection, and it is shown that a small fraction of the data is necessary to classify these infrared spectra successfully into chemical classes.

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