Deciphering and classifying surface electromyography (sEMG) signals is highly essential in rehabilitation robotics, myoelectric prosthetic control, sign languages and human–computer-interface. Researchers have generally focused on utilizing multi-channel sEMG signals for gesture classification. However, in case of amputees, the residual muscles are limited and the gesture classification needs to be reframed with minimum sEMG channels. Moreover, another key problem in the gesture classification system is the nonlinear and non-stationary nature of sEMG signals, leading to poor generalization ability. Hence, to address the aforementioned problems, this study puts forward a novel deep learning classifier framework which leverages the potentials of variational mode decomposition (VMD) technique and a hybrid convolutional neural network–long short term memory (CNN–LSTM) classifier model to recognize the hand gestures from single channel sEMG signals. Collecting the sEMG signals from forearm muscles of 25 intact subjects for ten functional and grasping actions, this work implements a VMD technique to identify the prominent frequency modes in the sEMG signals. From the decomposed modes of sEMG signals, the prominent intrinsic mode functions (IMFs) are extracted through spectral analysis to minimize the computation burden on the hybrid classifier model. Furthermore, as the muscle contractions result in substantial temporal dependencies, this work exploits the potentials of CNN and LSTM networks and extracts the spatiotemporal features of the sEMG signals for various hand gestures. The experimental results corroborate that the proposed classifier framework can achieve an average classification accuracy of 98.04% and provide 3% improvement in the classification accuracy compared to conventional CNN classifier.

To improve the classification accuracy of hand movements from sEMG signals, this paper puts forward a unified hand gesture classification framework which exploits the potentials of variational mode decomposition (VMD) and multi-class support vector machine (SVM). Acquiring the sEMG signals from 25 intact subjects for ten functional activities in real-time, we implement a non-recursive adaptive decomposition technique to sEMG signals and perform power spectral analysis to identify the dominant narrow-band intrinsic mode functions (IMFs) that contain prominent biomarkers. Subsequently, to compute the optimal feature vectors from a set of entropy measures, this work investigates the performance of two techniques namely minimum redundancy and maximum relevance (MRMR) technique and kernel principal component analysis (kPCA). After extracting the optimal set of entropy features, the proposed approach implements a multi-class SVM based on one-vs-one (OVO) strategy to classify the hand gestures. The performance of the multi-class SVM compared with those of the K-nearest neighbor (KNN) and naïve bayes (NB) classifiers highlight that multi-class SVM offers superior performance with an average classification accuracy of 99.98%. Moreover, for statistical analysis of the experimental results, this work performs Friedman test to analyze the significance of the SVM, KNN and NB classifier performances. Finally, the performance comparison of the proposed approach with those of the state-of-the-art techniques highlights the superiority of the proposed framework to improve the hand gesture classification accuracy.

Parkinson's disease (PD) is a progressive, debilitating neurological movement disorder that affects the person's muscle control, movement, speech, cognition and dexterity. For diagnosing PD in a clinical setting, in addition to the neurological examinations, clinicians use the unified Parkinson disease rating scale (UPDRS) to assess the motor and non-motor impairments. Such a clinical assessment highly depends on the experience and expertise of the clinicians, and it may result in biased evaluation. Hence, to assist the clinicians, we put forward a gait analysis-based deep convolutional neural network (DCNN) framework which leverages the potentials of variational mode decomposition (VMD) technique with the recurrence plots (RP) to enhance the PD severity classification performance. Specifically, transforming the VMD modes of vertical ground reaction force (VGRF) time series data into two-dimensional texture images to capture the temporal dependency, this work trains the DCNN classifier through recurrence images for its ability to extract the discriminative features among the PD severity levels. For evaluation, this study utilises the VGRF dataset of 93 PD subjects and 73 healthy controls from Physiobank for three different walking tests. Consequently, utilising VMD, RP and DCNN in a unified framework, this investigation shows that the PD severity rating can be significantly enhanced through DCNN model that is trained using RP of dominant intrinsic mode functions (IMFs). The novelty of the proposed framework lies in identifying the prominent gait biomarkers through dominant IMFs from power spectral analysis for reducing the computational burden of DCNN. Moreover, to handle the data over-fitting issue in the classifier, L2 regularisation technique, which penalises the weight parameters of the nodes, is used in combination with the dropout layer. Experimental results underscore that the proposed VMD-RP-DCNN architecture can address the spectral overlapping issue in VGRF decomposition and achieve an average PD severity prediction accuracy of 98.45%.

This paper puts forward a novel entropy features based multi-class SVM classifier framework to predict the limb movement of the transradial amputees from the surface electromyography (sEMG) signals. The major challenges with the sEMG signal are nonlinear and non-stationary characteristics and susceptibility to noise. Consequently, a robust and an effective feature extraction framework which is invariant to force level variations is central in sEMG based prosthesis control. To address the aforementioned challenges, this study leverages the potential of variational mode decomposition (VMD) technique to identify the prominent frequency modes of the sEMG signals, and performs the spectral evaluation of the decomposed sEMG modes to identify the dominant ones to extract the entropy features. Subsequently, we evaluate the efficacy of four nonlinear optimal feature selection techniques and identify the prominent entropy features to train the multi-class SVM model that can predict the gestures. Specifically, to handle the nonlinearly separable input data, this study implements a kernelization named a radial basis function (RBF), which has good generalization and noise tolerance features. The efficacy of the proposed framework is tested using the publicly available datasets that contain gestures from transradial and congenital amputees for functional gestures. Experimental results obtained for various gestures with dynamic force levels underscore that the proposed framework is highly robust against the force level variations and can achieve a classification accuracy of 99.07%.

Diabetic retinopathy (DR) is a leading eye disease which damages the blood vessels in the retina. Initially DR is asymptomatic and eventually ends up with severe or complete vision loss. The main reason of the DR is diabetic mellitus (DM) which is a type of diabetic where the pancreases fail to prod

There are many causes that leads to lower limb disabilities. Accidents, injuries, congenital defects, sprains, etc. are some of the most common reasons. The patients with such disabilities usually prefer the wheelchair as the most common augmentative option because of easy availability and user friendliness. Although there has been great advancements in the wheelchair design, (e.g. stair-climbing, standing, sports), there are certain areas in which developments has not been considered. One such area is the movement of the patient to the bed. The project aims to improve the self-sufficiency of the user to move to the bed by them. The wheelchair design involves four steps that aids in efficiently moving the user to the bed. The in depth study and work of the adjustable wheelchair will be discussed further in this paper

Deep learning has a huge potential in healthcare for uncovering the hidden patterns from large volume of clinical data to diagnose different diseases. This paper presents a novel deep learning architecture based long short term memory (LSTM) network for severity rating of Parkinson's disease (PD) using gait pattern. Unlike machine learning (ML) algorithms, the LSTM network avoids the need for hand crafted features and learns the long-term temporal dependencies in the gait cycle for robust diagnosis of PD. The primary advantage of the LSTM network is that it solves the vanishing gradient problem by introducing the memory blocks in place of self-connected hidden units, thereby deciding when to learn new information. Three distinct gait datasets containing vertical ground reaction force (VGRF) recordings for different walking scenarios are used for training the LSTM network. To avoid data overfitting, the proposed approach utilizes dropout and L2 regularization techniques. For solving the cost function, Adam, a stochastic gradient-based optimizer, is employed and the severity of PD is categorized based on unified Parkinson's disease rating scale (UPDRS) and Hoehn and Yahr (H&Y) scale. The experimental results reveal that Adam optimized LSTM network can effectively learn the gait kinematic features and offer an average accuracy of 98.6% for binary classification and 96.6% for multi-class classification, with an accuracy improvement of 3.4% in comparison with the related techniques. (C) 2021 Elsevier B.V. All rights reserved.

Parkinsons disease (PD) is the second most neurodegenerative disease, which results in gradual loss of movements. To diagnose PD in a clinical setting, clinicians generally use clinical manifestations like motor and non-motor symptoms and rate the severity based on unified Parkinsons disease rating scale (UPDRS). Such clinical assessment largely depends on the expertise and experience of the clinicians and it is subjective leading to variation in assessment between clinicians. As the gait of people with Parkinson's generally differs from gait of healthy age-matched adults, the assessment of gait abnormalities can lead to not only the diagnosis of PD but also the rating of severity level based on motor symptoms. Hence, in this paper, a data-driven gait classification framework using the supervised machine learning algorithms is presented. Using the publicly available gait datasets acquired using vertical ground reaction force (VGRF) sensors, we present a correlation based feature extraction technique for improved stage classification of PD. Significant biomarkers from spatiotemporal gait features are obtained based on the correlation, and the normal distribution of the gait dataset is assessed using the Shapiro-Wilk test. Subsequently, four supervised machine learning algorithms, namely, K-nearest neighbours (KNN), Naive Bayes (NB), Ensemble classifier (EC) and Support vector machine (SVM) are used to rate the severity level of PD according to the Hoehn and Yahr (H&Y) scale. The performance of the classifiers, assessed using the confusion matrix and parallel coordinate plots, highlights that SVM can result in a classification accuracy of 98.4%. Moreover, with minimal gait feature set acquired based on the rank correlation, the proposed approach outperforms several other state-of-the-art methods that have used the same dataset for PD stage classification. (c) 2021 IPEM. Published by Elsevier Ltd. All rights reserved.

While diagnosing Parkinson’s disease (PD), neurologists often use several clinical manifestations of the subject and rate the severity level based on the Unified Parkinson Disease Rating Scale (UPDRS). This kind of rating largely depends on the expertise of the doctors, which is not only subjective but also inefficient. Hence, in this paper, a machine learning based gait classification system which can assist the clinician to diagnose the stages of PD is presented. Gait pattern, which plays a significant role in assessing the human mobility, is a significant biomarker to classify whether the subject is healthy or affected with PD. Hence, we utilize the vertical ground reaction force (VGRF) gait dataset and extract the minimal feature vector using the statistical analysis. Subsequently, the normal distribution of the data is validated using the Shapiro–Wilk test, and from the spatial and temporal features of gait pattern, the salient biomarkers are identified using the correlation based feature selection technique. Four supervised machine learning algorithms namely decision tree (DT), support vector machine (SVM), ensemble classifier (EC) and Bayes classifier (BC) are used for statistical and kinematic analyses which predict the severity of PD. The classifier efficacy quantified using the accuracy, sensitivity and specificity highlights that the proposed framework can effectively rate the severity of PD based on Hohen and Yahr (H&Y) scale. Moreover, comparing the accuracy of the proposed PD classification approach with those of the other state-of-the-art approaches, which utilized the same gait dataset, reveal that the proposed method outperforms several other PD classification methods.

Having finally taken a foothold by the radial growth of the internet generation in the recent years, steganography serves as a mean for private, secure and sometimes malicious communication. From a technical eye, this thesis focuses on amalgamation of two methods for securing global cyber data. To achieve this perfect blend, A-B MAP Cipher is performed in order to tune the cyber data to cipher text pursued by bit replacement in audio signal with cipher bits by adopting ASET. This multilevel cladding ensures high level security to the global cyber data.