Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
4 results
Search Results
Item Feature selection for myoelectric pattern recognition using two channel surface electromyography signals(Institute of Electrical and Electronics Engineers Inc., 2017) Powar, O.S.; Chemmangat, K.Pattern recognition scheme is used for discriminating various classes of hand motion with feature extracted from the surface electromyography signals. However, while using a relatively large feature set for classification process, the computational complexity increases tremendously. To overcome this, the paper implements feature selection technique using wrapper evaluation and four different search methods without significantly affecting the classification accuracy. The performance of the features is tested on surface electromyography data collected from seven subjects, with eight classes of movements. Practical results indicate that using feature selection methods can achieve the same accuracy with lesser number of features. © 2017 IEEE.Item Comparison of pre-processing filters on the performance of sEMG based pattern recognition(Institute of Electrical and Electronics Engineers Inc., 2019) Powar, O.S.; Chemmangat, K.The noise present in the surface electromyography (sEMG) signals is a significant problem in the control of the rehabilitation scheme. Different noise reducing methods have been discussed and considered individually in previous studies. However, there is limited work on the comparison of different noise reduction strategies. To achieve good performance of Myoelectric Control (MEC) system, the selection of filters becomes essential. The vital contribution of this work is a study into the comparison of three denoising methods including Butterworth filter, Weiner filter and Spectral Subtraction (SS) filter that has been used to remove the noise from sEMG signal. Performance evaluation of the three noise reduction methods is done regarding classification accuracy and computation time. The three denoising methods have been validated on the recorded sEMG of seven healthy subjects while performing eight classes of movements from the two muscle positions on the right forearm. The accuracy is compared with four classifiers namely, J48, k-nearest neighbors (KNN), Naive Bayes and Linear Discriminant Analysis (LDA). Results show that the Butterworth filter provides marginally better performance than the other two filters regarding classification accuracy; when computation time is considered SS filter offers significant savings. A visual inspection of the output of the Weiner filter hints at its utility as a muscle activity onset detection tool. © 2019 IEEE.Item Improved Robustness of EMG Pattern Recognition for Transradial Amputees with EMG Features Against Force Level Variations(Institute of Electrical and Electronics Engineers Inc., 2023) Powar, O.S.; Chemmangat, K.Feature extraction is an essential process for removing the unwanted part and interference of the Electromyography (EMG) signal, and to extract the useful information hidden in it. Inorder to obtain high performance of Myoelectric Control (MEC), the choice of features plays an important role. The studies carried out earlier to overcome force level variation have used features which are redundant, affecting the robustness and the classification performance. This study's main objective is to assess a database's performance consisting of nine upper limb amputee subjects with EMG data recorded at three different force levels when six motions were classified using twenty different time domain features that are frequently found in the literature. Training is carried out at one force level, and the other two unknown force levels are used for testing. Out of the twenty features, the one that is the most stable is displayed for each force level. The results show that root mean square (RMS) feature outperformed other features for training at low and medium force levels, and Wilson amplitude (WAMP) feature for training at a high force level, when compared with the most widely used linear discriminant analysis (LDA) classifier. The average classification accuracy for the nine amputee subjects trained with the RMS feature at low and medium force levels was 42% and 51.78% percent, respectively. For high force level, when trained using WAMP feature, an accuracy of 46.78% has been obtained. The features are verified using histogram plots. This study will help select those features which are not important for robust classification of hand movements. © 2023 IEEE.Item Feature Selection and Ranking in EMG Analysis for Hand Movement Classification(Institute of Electrical and Electronics Engineers Inc., 2023) Chandrika, P.R.; Powar, O.S.; Chemmangat, K.Surface Electromyography has gained tremendous significance in the recent years due to its suitability and reliability in a wide range of applications like automatic prosthetic control, diagnosis of neuromuscular disorders, in robotics and many such fields. Considering such applications, identification of various muscular movements is necessary and hence, EMG pattern recognition is needed. This paper focusses on a generalised EMG pattern recognition of various hand movements. The data from Ninapro Database - 4 has been used for pattern recognition. The database has Surface Electromyogram (sEMG) data of 52 various hand movements. The data was subjected to pre-processing, feature extraction and classification. An average accuracy of 64.87% was obtained for a combination of seven features in the time (temporal) domain, using Linear Discriminant Analysis (LDA) as the classification model. The obtained classification accuracies are compared and discussed with respect to the state-of-the-art literature. © 2023 IEEE.