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Machine Learning for sEMG Facial Feature Characterization
University of Turku (UTU),Department of Future Technology,Turku,Finland.ORCID iD: 0000-0003-2357-1108
University of Turku (UTU),Department of Future Technology,Turku,Finland.
University of Turku (UTU),Department of Future Technology,Turku,Finland.
2020 (English)In: Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA), Poznan, Poland: IEEE, 2020, p. 169-174Conference paper, Published paper (Refereed) [Artistic work]
Abstract [en]

Wearable e-health system, are frequently used for monitoring biomedical signals. These devices need to have advanced and applicable methods of feature selection and classifications for real time applications. Electromyogram (EMG) signal records the movement of the human muscle. EMG signal processing techniques aim to achieve the actual signal and among others, detect the state of signals related to positive and negative emotional expression. In our study, the data collected is from the facial muscle activity that is produced by the emotion of the facial expressions. The key challenge is in finding an accurate classification method of the measured signals. This paper investigates the promising techniques for the detection and classification of EMG signal using machinelearning theory. Here, we demonstrated Support Vector Machine (SVM) is an optimal method for classification of facial surface Electromyogram (sEMG) signal associated to pain dataset. The test results and the methods are able to analyze the patterns recognition of facial EMG signal classification. The result and the findings 99% accuracy with SVM method adds value on the classification algorithms of our EMG signal acquisitions platform.

Place, publisher, year, edition, pages
Poznan, Poland: IEEE, 2020. p. 169-174
Keywords [en]
— machine learning, facial sEMG, biosignal, classifications, support vector machine (SVM)
National Category
Engineering and Technology
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-266249DOI: 10.23919/SPA.2019.8936818OAI: oai:DiVA.org:kth-266249DiVA, id: diva2:1382493
Conference
2019 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)
Note

QC 20200115

Available from: 2020-01-03 Created: 2020-01-03 Last updated: 2020-01-15Bibliographically approved

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Publisher's full texthttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8936818&isnumber=8936656

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Kelati, AmlesetPlosila, JuhaTenhunen, Hannu

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