Login Register Cart 0
Generic placeholder image

International Journal of Sensors, Wireless Communications and Control

Editor-in-Chief

ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

An Approach for Evaluation and Recognition of Facial Emotions Using EMG Signal

Author(s): Sourav Maity and Karan Veer*

Volume 14, Issue 2, 2024

Published on: 05 January, 2024

Page: [113 - 121] Pages: 9

DOI: 10.2174/0122103279260571231213053403

Price: $65

Abstract

Background: Facial electromyography (fEMG) records muscular activities from the facial muscles, which provides details regarding facial muscle stimulation patterns in experimentation.

Objectives: The Principal Component Analysis (PCA) is mostly implemented, whereas the actual or unprocessed initial fEMG data are rendered into low-spatial units with minimizing the level of data repetition.

Methods: Facial EMG signal was acquired by using the instrument BIOPAC MP150. Four electrodes were fixed on the face of each participant for capturing the four different emotions like happiness, anger, sad and fear. Two electrodes were placed on arm for grounding purposes.

Results: The aim of this research paper is to propagate the functioning of PCA in synchrony with the subjective fEMG analysis and to give a thorough apprehension of the advanced PCA in the areas of machine learning. It describes its arithmetical characteristics, while PCA is estimated by implying the covariance matrix. Datasets which are larger in size are progressively universal and their interpretation often becomes complex or tough. So, it is necessary to minimize the number of variables and elucidate linear compositions of the data to explicate it on a huge number of variables with a relevant approach. Therefore, Principal Component Analysis (PCA) is applied because it is an unsupervised training method that utilizes advanced statistical concept to minimize the dimensionality of huge datasets.

Conclusion: This work is furthermore inclined toward the analysis of fEMG signals acquired for four different facial expressions using Analysis of Variance (ANOVA) to provide clarity on the variation of features.

Keywords: Facial EMG, features, principal component analysis, ANOVA, analysis, data recording.

« Previous Next »
Graphical Abstract

[1]
Laparra-Hernández J, Belda-Lois JM, Medina E, Campos N, Poveda R. EMG and GSR signals for evaluating user’s perception of different types of ceramic flooring. Int J Ind Ergon 2009; 39(2): 326-32.
[http://dx.doi.org/10.1016/j.ergon.2008.02.011]
[2]
Zhang Y, Cheng C, Zhang Y. Multimodal emotion recognition based on manifold learning and convolution neural network. Multimedia Tools Appl 2022; 81(23): 33253-68.
[http://dx.doi.org/10.1007/s11042-022-13149-8]
[3]
Lee YK, Pae DS, Hong DK, Lim MT, Kang TK. Emotion recognition with short-period physiological signals using bimodal sparse autoencoders. Intelligent Automation & Soft Computing 2022; 32(2): 657-73.
[http://dx.doi.org/10.32604/iasc.2022.020849]
[4]
Bornemann B, Winkielman P, der Meer E. Can you feel what you do not see? Using internal feedback to detect briefly presented emotional stimuli. Int J Psychophysiol 2012; 85(1): 116-24.
[http://dx.doi.org/10.1016/j.ijpsycho.2011.04.007] [PMID: 21571012]
[5]
Ngo HT, Gottumukkal R, Asari VK. A flexible and efficient hardware architecture for realtime face recognition based on eigenface. IEEE Computer Society Annual Symposium on VLSI.
[http://dx.doi.org/10.1109/ISVLSI.2005.5]
[6]
Boualleg AH, Bencheriet Ch, Tebbikh H. Automatic Face recognition using neural network-PCA. In: In Information and Communication Technologies. 2006; pp. 1920-5.
[http://dx.doi.org/10.1109/ICTTA.2006.1684683]
[7]
Maheswari VU, Prasad GV, Raju SV. A survey on local textural patterns for facial feature extraction. Int J Comput Vis Image Process 2018; 8(2): 1-26. [IJCVIP]
[http://dx.doi.org/10.4018/IJCVIP.2018040101]
[8]
Maheswari VU, Prasad GV, Raju SV. Facial expression analysis using local directional stigma mean patterns and convolutional neural networks. Inter J Knowledge-based and Inte Eng Sys 2021; 25(1): 119-28.
[http://dx.doi.org/10.3233/KES-210057]
[9]
Maheswari VU, Aluvalu R, Kantipudi MVVP, Chennam KK, Kotecha K, Saini JR. Driver drowsiness prediction based on multiple aspects using image processing techniques. IEEE Access 2022; 10: 54980-90.
[http://dx.doi.org/10.1109/ACCESS.2022.3176451]
[10]
Maheswari VU, Varaprasad G, Viswanadharaju S. Local double directional stride maximum patterns for facial expression retrieval. Int J Biom 2022; 14(3/4): 439-52.
[http://dx.doi.org/10.1504/IJBM.2022.124682]
[11]
Maheswari VU, Raju SV, Reddy KS. Local directional weighted threshold patterns (LDWTP) for facial expression recognition. 2019 Fifth International Conference on Image Information Processing (ICIIP) 167-70.
[http://dx.doi.org/10.1109/ICIIP47207.2019.8985829]
[12]
Rainoldi A. Spectral properties of the surface EMG can characterize/do not provide information about motor unit recruitment strategies and muscle fiber type. J Appl Physiol 2008; 105(5): 1678.
[PMID: 19031609]
[13]
Maity S, Veer K. A generalized review of human-computer interaction using electromyogram signals. Recent Pat Eng 2023; 17: 16-25.
[14]
Mohod Prakash S, Kalpna C. Face recognition using PCA. International Journal of Artificial Intelligence and Knowledge Discovery 2011; 1: 25-8.
[15]
Ebied RM. Feature Extraction using PCA and Kernel-PCA for Face Recognition. The 8th International Conference on Informatics and Systems Computational Intelligence and Multimedia Computing Track 72-7.
[16]
Kilby J, Gholam Hosseini H. Wavelet analysis of surface electromyography signals. Conf Proc IEEE Eng Med Biol Soc 2004; 2006: 384-7.
[PMID: 17271692]
[17]
Maitrot A, Lucas MF, Doncarli C, Farina D. Signal-dependent wavelets for electromyogram classification. Med Biol Eng Comput 2005; 43(4): 487-92.
[http://dx.doi.org/10.1007/BF02344730] [PMID: 16255431]
[18]
Gorsuch RL. Factor Analysis. Hillsdale, NJ, USA: Erlbaum 1983.
[19]
Flanders M, Herrmann U. Two components of muscle activation: scaling with the speed of arm movement. J Neurophysiol 1992; 67(4): 931-43.
[http://dx.doi.org/10.1152/jn.1992.67.4.931] [PMID: 1588392]
[20]
Ivanenko YP, Poppele RE, Lacquaniti F. Five basic muscle activation patterns account for muscle activity during human locomotion. J Physiol 2004; 556(1): 267-82.
[http://dx.doi.org/10.1113/jphysiol.2003.057174] [PMID: 14724214]
[21]
Cheung VC. Central and sensory contributions to the activation and organization of muscle synergies during natural motor behaviors. J Neurosci 2005; 25(27): 6419-34.
[22]
Yuille Alan L, Peter W. Hallinan, David S Cohen. Feature extraction from faces using deformable templates. International journal of computer vision 1992; 8: 99-111.
[23]
Perusquia-Hernandez M, Hirokawa M, Suzuki K. Spontaneous and posed smile recognition based on spatial and temporal patterns of facial EMG. 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). San Antonio, TX, USA. 2015; pp. 537-41.
[24]
Murugan PR, Varghese SM. EMG signal classification using ANN and ANFIS for neuro-muscular disorders. Int J Biomed Eng Technol 2014; 16(2): 156-68.
[http://dx.doi.org/10.1504/IJBET.2014.065657]
[25]
Spiewak C, Islam M, Zaman A, Rahman MH. A comprehensive study on EMG feature extraction and classifiers. Open Access J Biomed Eng Biosci 2018; 1(1): 1-10.
[http://dx.doi.org/10.32474/OAJBEB.2018.01.000104]
[26]
Veer K, Sharma T. A novel feature extraction for robust EMG pattern recognition. J Med Eng Technol 2016; 40(4): 149-54.
[http://dx.doi.org/10.3109/03091902.2016.1153739] [PMID: 27004618]
[27]
Tresch MC, Cheung VC. Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. J Neurophysiol 2006; 95(4): 2199-212.
[28]
Naik GR, Kumar DK, Arjunan SP, Palaniswami M, Begg R. Limitations and applications of ICA for surface electromyogram. Conf Proc IEEE Eng Med Biol Soc 5739-42.
[http://dx.doi.org/10.1109/IEMBS.2006.259844]
[29]
Tabernig CB, Acevedo RC. M-wave elimination from surface electromyogram of electrically stimulated muscles using singular value decomposition: Preliminary results. Med Eng Phys 2008; 30(6): 800-3.
[http://dx.doi.org/10.1016/j.medengphy.2007.09.001] [PMID: 17981071]
[30]
Morrison DF. Multivariate Statistical Methods. New York, USA: McGraw-Hill 1990.
[31]
Diamantaras KI, Kung SY. Principal component neural networks: theory and applications. New York, NY: Wiley 1996.
[32]
Flury B. Common principal components and related models. New York, NY: Wiley 1988.
[33]
Horn R, Johnson C. Matrix analysis. Cambridge, UK: Cambridge University Press 1985.
[http://dx.doi.org/10.1017/CBO9780511810817]
[34]
Karan V. Spectral and mathematical evaluation of electromyography signals for clinical use. In: International journal of biomathematics. 2016; 9: p. 1650094.
[35]
Gabriel KR. The biplot graphic display of matrices with application to principal component analysis. Biometrika 1971; 58(3): 453-67.
[http://dx.doi.org/10.1093/biomet/58.3.453]
[36]
Cadima J, Jolliffe IT. On relationships between uncentred and column-centred principal component analysis. Pak J Stat 2009; 25: 473-503.
[37]
Ringnér M. What is principal component analysis? Nat Biotechnol 2008; 26(3): 303-4.
[http://dx.doi.org/10.1038/nbt0308-303] [PMID: 18327243]
[38]
Lee D, Lee W, Lee Y, Pawitan Y. Super-sparse principal component analyses for high-throughput genomic data. BMC Bioinformatics 2010; 11(1): 296.
[http://dx.doi.org/10.1186/1471-2105-11-296] [PMID: 20525176]
[39]
Obukhov AM. Statistically homogeneous fields on a sphere. Usp Mat Nauk 1947; 2: 196-8.
[40]
Karan Veer. Identification and classification of upper limb motions using PCA. Biomedical Engineering/Biomedizinische Technik 2018; 63: 191-6.
[41]
Karan V. Wavelet Transform-Based Classification of Electromyogram Signals Using an Anova Technique. Neurophysiology 2015; 47(4): 302-9.
[http://dx.doi.org/10.1007/s11062-015-9537-7]
[42]
Lorenz EN. Empirical orthogonal functions and statistical weather prediction. In: Technical report, Statistical Forecast Project Report. 1956.

Rights & Permissions Print Cite
Article Metrics
16
1
© 2024 Bentham Science Publishers | Privacy Policy