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Multichannel surface EMG and machine learning for classification of facial expressions

Diederiks, V.L. (2021) Multichannel surface EMG and machine learning for classification of facial expressions.

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Abstract:Facial expressions are an important aspect of non-verbal communication, showing reactions and attention. In patients with Disorders of Consciousness (DOC) facial expressions are commonly less pronounced. Diagnosis of these patients is partly based on their response to external stimuli, measured by their facial expression. As these can be difficult to objectively measure, a high misdiagnosis rate exists. Development of a method to detect and identify expressions could support diagnosis and possibly improve communication between patients and caregivers or loved-ones. The main goal of this research is to evaluate to what extent facial surface electromyography (sEMG) signals can be used to classify four facial expressions (happiness, anger, sadness and fear) in healthy subjects. In addition, micro expressions are evoked and measured to mimic the diminished expressions of DOC patients. Lastly the predictive value of various channels is evaluated to determine the most efficient experimental set-up. An experimental protocol using a 32-channel unipolar micro-electrode set-up was designed to obtain EMG signals. Twenty-nine models were included in in this study. It was found that the model Subspace K-Nearest Neighbor (KNN) and feature Difference Absolute Mean Value (DAMV) performed best at classifying expressions of subjects it had not been trained on (with a test accuracy of 55.7%). Happiness was most often identified correctly. Additionally, this research has demonstrated that micro expressions, evoked by exposure to images of facial expressions, occur and can be measured with sEMG. The model Subspace KNN and feature Waveform Length (WL) succeeds in predicting these micro expressions for one of the subjects with a test accuracy of 47.1%. Evaluation of the predictive value of the 32 channels shows that a comparable test accuracy (53.4%) is obtained for a subset of only 15 channels with model Subspace KNN and feature WL. To develop a method applicable in clinical practice further research is needed, this research provides a good starting point.
Item Type:Essay (Master)
Faculty:TNW: Science and Technology
Subject:42 biology, 44 medicine, 50 technical science in general
Programme:Biomedical Engineering MSc (66226)
Link to this item:http://purl.utwente.nl/essays/86525
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