Browsing by Author "Abdullah, A."

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    A Computationally Efficient sEMG based Silent Speech Interface using Channel Reduction and Decision Tree based Classification
    (Elsevier B.V., 2020) Abdullah, A.; Chemmangat, K.
    Silent Speech Interface is one of the promising areas of Human-Computer Interaction research. The surface electromyography based silent speech interface is a technique where the electric activity of facial muscles are used to detect speech. The existing sEMG based SSI techniques use complex machine learning algorithms and too many number of electrodes on the subject's face. It creates inconvenience to the user who might have undergone laryngectomy. More number of electrodes becomes highly invasive to the user, while complex classification algorithms increase the computational cost and prevents real time implementation of sEMG based SSI. Thus the objective of this research work was to develop a less complex and computationally less expensive model to classify words. To achieve this goal channel reduction technique and the use of Decision Tree based classification algorithm was employed. Only the time domain features are used as input to the classification algorithm. The motive was to exploit the advantage of computational ease in extracting the time domain features as compared to the frequency domain features. The sEMG data of the words used in this work are obtained from the complete utterance of the sentences and not by individual utterances of the word. Our algorithm was able to achieve a word accuracy of 95.17% even after applying a channel reduction, thereby allowing us to use only the data of 5 channels, in place of a conventional seven channel setup. © 2020 The Authors. Published by Elsevier B.V.
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    Application of Fractal Analysis based Feature Extractor for Channel Reduction of Silent Speech Interface Using Facial Electromyography
    (Intelligent Network and Systems Society, 2023) Abdullah, A.; Powar, O.S.; Chemmangat, K.
    Surface electromyography (sEMG) based silent speech interface (SSI) is an actively investigated topic among the broad area of human computer interaction studies which is currently dominated by acoustic sound based speech recognition research. This research is an attempt to help people who have an impaired vocal system if they are having no issues with their facial muscle functions. The basic idea is to reduce the total number of sEMG electrodes that has to be affixed on the face thereby reducing the invasiveness of the silent speech recognition module. This is achieved by incorporating a new detrended fluctuation analysis (DFA) based feature along with the already existing features associated with electromyographic signals. DFA is used for the first time in literature in the area of surface electromyography based silent speech recognition. The main idea is to incorporate the DFA feature along with the state-of-the-art features to improve the performance of a sEMG based SSI model so that an efficient channel reduced model can be realised. Different channel combinations were tried to analyse the impact of each channel in word recognition accuracy and the optimal channel combination was identified. As a result of this research work, a reduced channel setup with 5 electrodes was proposed in place of the conventional 7 channel data acquisition setup. This was achieved while maintaining an accuracy of 83.88 % and 92.92 % using the decision tree (DT) model and K-nearest neighbours (KNN) model respectively © 2023, International Journal of Intelligent Engineering and Systems.All Rights Reserved.