Performance evaluation of pattern recognition networks using electromyography signal and time-domain features for the classification of hand gestures

Vasanthi, S.; Jayasree, T.

doi:10.1177/0954411920912119

Cited by 10 publications

(6 citation statements)

References 28 publications

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“…The combined information of time and frequency signals is referred as time frequency techniques, and its transformations provide highly nonstationary information of the signals [ 12 ]. Earlier studies have incorporated the time-domain and time-frequency features for building finer classification model [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

Bakiya

Anitha²,

Sridevi³

et al. 2022

Behavioural Neurology

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Electromyograms (EMG) are a recorded galvanic action of nerves and muscles which assists in diagnosing the disorders associated with muscles and nerves. The efficient discrimination of abnormal EMG signals, myopathy and amyotrophic lateral sclerosis, engage crucial role in automatic diagnostic assistance tools, since EMG signals are nonstationary signals. Hence, for computer-aided identification of abnormalities, extraction of features, selection of superlative feature subset, and developing an efficient classifier are indispensable. Initially, time domain and Wigner-Ville transformed time-frequency features were extracted from abnormal EMG signals for experiments. The selection of substantial characteristics from time and time-frequency features was performed using bat algorithm. Extensively, deep neural network classifier is modelled for selected feature subset using bat algorithm from extracted time and time-frequency features. The performance of deep neural network exerting selected features from bat algorithm was compared with conventional artificial neural network. Results demonstrate that the deep neural network modelled with layers 2 and 3 ( neurons = 2 and 4) using time domain features is efficient in classifying the abnormalities of EMG signals with an accuracy, sensitivity, and specificity of 100% and also exhibited finer performance. Correspondingly, the developed conventional single layer artificial neural network ( neurons = 7 ) with time domain features has shown an accuracy of 83.3%, sensitivity of 100%, and specificity of 71.42%. The work materializes the significance of conventional and deep neural network using time and time-frequency features in diagnosing the abnormal signals exists in neuromuscular system using efficient classification.

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Section: Introductionmentioning

confidence: 99%

Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

Bakiya

Anitha²,

Sridevi³

et al. 2022

Behavioural Neurology

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“…To circumvent this, the MP layer should label the low-quality link as inactive, and no packets should be scheduled to inactive pathways. In addition, if the communication quality via that way improves, the presently inactive path should be reactivated [32,33].…”

Section: Resultsmentioning

confidence: 99%

A Novel Analytical Framework Is Developed for Wireless Heterogeneous Networks for Video Streaming Applications

Kumarganesh¹,

Anthoniraj

Kumar³

et al. 2022

Journal of Mathematics

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Due to bandwidth and time limits, delivering high-quality video streaming facility offers real-time wireless networks, but assuring quality of experience (QoE) is rather difficult. A network host can deliver its data stream via numerous different network pathways. At the same time, using multipath data transmission, where video streaming may be supplied through Internet protocols (IPs) as well as broadband with bidirectional communication between video sources and consumers, is one way to address this difficult problem. In this work, a novel framework is developed for wireless heterogeneous networks for video streaming applications using concurrent multipath transfer (CMT). The performance of frame-level delay can be enhanced for better video quality using this proposed method. The network congestion on end-to-end path utilised in CMT is not dependent of one another, and we operate under prior assumption that receiver’s announced window does not limit the sender. The analytical results show that the proposed framework outperforms existing methods in terms of performance with minimum retransmission delay.

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“…The separation between the data is coined as decision hyperplane, and the separation from the hyper-plane is termed margin. Optimum target of SVM training is to obtain the hyper-plane having highest margin and to provide accurate classification [26]. Usage of linear kernel reduces the risk of over-fitting the data and increases the efficiency of classification by reducing the overall complexity.…”

Section: Feature Selection Using Genetic Algorithmmentioning

confidence: 99%

Design and Development of Polymer-Based Optical Fiber Sensor for GAIT Analysis

Mamidipaka¹,

Suman²,

Kumar

et al. 2023

International Journal of Polymer Science

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In the present scenario like COVID-19 pandemic, to maintain physical distance, the gait-based biometric is a must. Human gait identification is a very difficult process, but it is a suitable distance biometric that also gives good results at low resolution conditions even with face features that are not clear. This study describes the construction of a smart carpet that measures ground response force (GRF) and spatio-temporal gait parameters (STGP) using a polymer optical fiber sensor (POFS). The suggested carpet contains two light detection units for acquiring signals. Each unit obtains response from 10 nearby sensors. There are 20 intensity deviation sensors on a fiber. Light-emitting diodes (LED) are triggered successively, using the multiplexing approach that is being employed. Multiplexing is dependent on coupling among the LED and POFS sections. Results of walking experiments performed on the smart carpet suggested that certain parameters, including step length, stride length, cadence, and stance time, might be used to estimate the GRF and STGP. The results enable the detection of gait, including the swing phase, stance, stance length, and double supporting periods. The suggested carpet is dependable, reasonably priced equipment for gait acquisition in a variety of applications. Using the sensor data, gait recognition is performed using genetic algorithm (GA) and particle swarm optimization (PSO) technique. GA- and PSO-based gait template analyses are performed to extract the features with respect to the gait signals obtained from polymer optical gait sensors (POGS). The techniques used for classification of the obtained signals are random forest (RF) and support vector machine (SVM). The accuracy, sensitivity, and specificity results are obtained using SVM classifier and RF classifier. The results obtained using both classifiers are compared.

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Performance evaluation of pattern recognition networks using electromyography signal and time-domain features for the classification of hand gestures

Cited by 10 publications

References 28 publications

Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

A Novel Analytical Framework Is Developed for Wireless Heterogeneous Networks for Video Streaming Applications

Design and Development of Polymer-Based Optical Fiber Sensor for GAIT Analysis

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