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

Efficient strategies for finger movement classification using surface electromyogram signals

One of the famous research areas in biomedical engineering and pattern recognition is finger movement classification. For hand and finger gesture recognition, the most widely used signals are the surface electromyogram (sEMG) signals. With the help of sEMG signals, four proposed techniques of finger movement classification are presented in this work. The first technique proposed is a dynamic graph construction and graph entropy-based classification of sEMG signals. The second technique proposed encompasses the ideas of dimensionality reduction utilizing local tangent space alignment (LTSA) and local linear co-ordination (LLC) with evolutionary algorithms (EA), Bayesian belief networks (BBN), extreme learning machines (ELM), and a hybrid model called EA-BBN-ELM was developed for the classification of sEMG signals. The third technique proposed utilizes the ideas of differential entropy (DE), higher-order fuzzy cognitive maps (HFCM), empirical wavelet transformation (EWT), and another hybrid model with DE-FCM-EWT and machine learning classifiers was developed for the classification of sEMG signals. The fourth technique proposed uses the ideas of local mean decomposition (LMD) and fuzzy C-means clustering along with a combined kernel least squares support vector machine (LS-SVM) classifier. The best classification accuracy results (of 98.5%) were obtained using the LMD-fuzzy C-means clustering technique classified with a combined kernel LS-SVM model. The second-best classification accuracy (of 98.21%) was obtained using the DE-FCM-EWT hybrid model with SVM classifier. The third best classification accuracy (of 97.57%) was obtained using the LTSA-based EA-BBN-ELM model.

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

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.

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

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.

A machine learning approach to identify hand actions from single-channel sEMG signals

Surface Electromyographic (sEMG) signal is a prime source of information to activate prosthetic hand such that it is able to restore a few basic hand actions of amputee, making it suitable for rehabilitation. In this work, a non-invasive single channel sEMG amplifier is developed that captures sEMG signal for three typical hand actions from the lower elbow muscles of able bodied subjects and amputees. The recorded sEMG signal detrends and has frequencies other than active frequencies. The Empirical Mode Decomposition Detrending Fluctuation Analysis (EMD-DFA) is attempted to de-noise the sEMG signal. A feature vector is formed by extracting eight features in time domain, seven features each in spectral and wavelet domain. Prominent features are selected by Fuzzy Entropy Measure (FEM) to ease the computational complexity and reduce the recognition time of classification. Classification of different hand actions is attempted based on multi-class approach namely Partial Least Squares Discriminant Analysis (PLS-DA) to control the prosthetic hand. It is inferred that an accuracy of 89.72% & 84% is observed for the pointing action whereas the accuracy for closed fist is 81.2% & 79.54% while for spherical grasp it is 80.6% & 76% respectively for normal subjects and amputees. The performance of the classifier is compared with Linear Discriminant Analysis (LDA) and an improvement of 5% in mean accuracy is observed for both normal subjects and amputees. The mean accuracy for all the three different hand actions is significantly high (83.84% & 80.18%) when compared with LDA. The proposed work frame provides a fair mean accuracy in classifying the hand actions of amputees. This methodology thus appears to be useful in actuating the prosthetic hand.

Does the Score on the MRC Strength Scale Reflect Instrumented Measures of Maximal Torque and Muscle Activity in Post-Stroke Survivors?

It remains unknown whether variation of scores on the Medical Research Council (MRC) scale for muscle strength is associated with operator-independent techniques: dynamometry and surface electromyography (sEMG). This study aimed to evaluate whether the scores of the MRC strength scale are associated with instrumented measures of torque and muscle activity in post-stroke survivors with severe hemiparesis both before and after an intervention. Patients affected by a first ischemic or hemorrhagic stroke within 6 months before enrollment and with complete paresis were included in the study. The pre- and post-treatment assessments included the MRC strength scale, sEMG, and dynamometry assessment of the triceps brachii (TB) and biceps brachii (BB) as measures of maximal elbow extension and flexion torque, respectively. Proprioceptive-based training was used as a treatment model, which consisted of multidirectional exercises with verbal feedback. Each treatment session lasted 1 h/day, 5 days a week for a total 15 sessions. Nineteen individuals with stroke participated in the study. A significant correlation between outcome measures for the BB (MRC and sEMG p = 0.0177, ρ = 0.601; MRC and torque p = 0.0001, ρ = 0.867) and TB (MRC and sEMG p = 0.0026, ρ = 0.717; MRC and torque p = 0.0001, ρ = 0.873) were observed post intervention. Regression models revealed a relationship between the MRC score and sEMG and torque measures for both the TB and BB. The results confirmed that variation on the MRC strength scale is associated with variation in sEMG and torque measures, especially post intervention. The regression model showed a causal relationship between MRC scale scores, sEMG, and torque assessments.