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Aims: To investigate the genetic diversity, population structure and mating-type distribution among the eco-distinct isolates of Magnaporthe oryzae from Karnataka, India., Methods and Results: A set of 38 isolates of M. oryzae associated with leaf blast disease of rice were collected from different rice ecosystems of Karnataka, India, and analysed for their diversity at actin, β-tubulin, calmodulin, translation elongation factor 1-α (TEF-1-α), and internal transcribed spacer (ITS) genes/region. The isolates were grouped into two clusters based on the multilocus sequence diversity, the majority being in cluster-IA (n = 37), and only one isolate formed cluster-IB. Population structure was analysed using 123 SNP data to understand the genetic relationship. Based on K = 2 and ancestry threshold of >70%, blast strains were classified into two subgroups (SG1 and SG2) whereas, based on K = 4 and ancestry threshold of >70%, blast strains were classified into four subgroups (SG1, SG2, SG3 and SG4). We have identified 13 haplotype groups where haplotype group 2 was predominant (n = 20) in the population. The Tajima's and Fu's Fs neutrality tests exhibited many rare alleles. Further, the mating-type analysis was also performed using MAT1 gene-specific primers to find the potentiality of sexual reproduction in different ecosystems. The majority of the isolates (54.5%) had MAT1-2 idiomorph, whereas 45.5% of the isolates possessed MAT1-1 idiomorph., Conclusions: The present study found the genetically homogenous population of M. oryzae by multilocus sequence analysis. Both mating types, MAT1-1 and MAT1-2, were found within the M. oryzae population of Karnataka., Significance and Impact of Study: The study on the population structure and sexual mating behaviour of M. oryzae is important in developing region-specific blast-resistant rice cultivars. This is the first report of MAT1 idiomorphs distribution in the M. oryzae population in any Southern state of India., (© 2022 Society for Applied Microbiology.)

Rice is a globally important crop and highly vulnerable to rice blast disease (RBD). We studied the spatial distribution of RBD by considering the 2-year exploratory data from 120 sampling sites over varied rice ecosystems of Karnataka, India. Point pattern and surface interpolation analyses were performed to identify the spatial distribution of RBD. The spatial clusters of RBD were generated by spatial autocorrelation and Ripley's K function. Further, inverse distance weighting (IDW), ordinary kriging (OK), and indicator kriging (IK) approaches were utilized to generate spatial maps by predicting the values at unvisited locations using neighboring observations. Hierarchical cluster analysis using the average linkage method identified two main clusters of RBD severity. From the Local Moran's I, most of the districts were clustered together (at I > 0), except the coastal and interior districts (at I < 0). Positive spatial dependency was observed in the Coastal, Hilly, Bhadra, and Upper Krishna Project ecosystems (p > 0.05), while Tungabhadra and Kaveri ecosystem districts were clustered together at p < 0.05. From the kriging, Hilly ecosystem, middle and southern parts of Karnataka were found vulnerable to RBD. This is the first intensive study in India on understanding the spatial distribution of RBD using geostatistical approaches, and the findings from this study help in setting up ecosystem-specific management strategies against RBD., (© 2022. The Author(s).)

Objective: Myoelectric control requires fast and stable identification of a movement from data recorded from a comfortable and straightforward system., Methods: We consider a new real-time pre-processing method applied to a single differential surface electromyogram (EMG): deconvolution, providing an estimation of the cumulative firings of motor units. A 2 channel-10 class finger movement problem has been investigated on 10 healthy subjects. We have compared raw EMG and deconvolution signals, as sources of information for two specific classifiers (based on either Support Vector Machines or k-Nearest Neighbours), with classical time-domain input features selected using Mutual Component Analysis., Results: Using the proposed pre-processing technique, classification performances statistically improve. For example, the true positive rates of the best-tested configurations were 80.9% and 86.3% when using the EMG and its deconvoluted signal, respectively., Conclusion: Even considering the limited dataset and range of classification approaches investigated, our preliminary results indicate the potential usefulness of the deconvolution pre-processing., Significance: Deconvolution of EMG is a fast pre-processing that could be easily embedded in different myoelectric control applications.

Aims: To investigate the diversity of eco-distinct isolates of Magnaporthe oryzae for their morphological, virulence and molecular diversity and relative distribution of five Avr genes., Methods and Results: Fifty-two M. oryzae isolates were collected from different rice ecosystems of southern India. A majority of them (n = 28) formed a circular colony on culture media. Based on the disease reaction on susceptible cultivar (cv. HR-12), all 52 isolates were classified in to highly virulent (n = 28), moderately virulent (n = 11) and less-virulent (13) types. Among the 52 isolates, 38 were selected for deducing internal transcribed spacer (ITS) sequence diversity. For deducing phylogeny, another set of 36 isolates from other parts of the world was included, which yielded two distinct phylogenetic clusters. We identified eight haplotype groups and 91 variable sites within the ITS sequences, and haplotype-group-2 (Hap&#95;2) was predominant (n = 24). The Tajima's and Fu's Fs neutrality tests exhibited many rare alleles. Furthermore, PCR analysis for detecting the presence of five Avr genes in the different M. oryzae isolates using Avr gene-specific primers in PCR revealed that Avr-Piz-t, Avr-Pik, Avr-Pia and Avr-Pita were present in 73.68%, 73.68%, 63.16% and 47.37% of the isolates studied, respectively; whereas, Avr-Pii was identified only in 13.16% of the isolates., Conclusions: Morpho-molecular and virulence studies revealed the significant diversity among eco-distinct isolates. PCR detection of Avr genes among the M. oryzae population revealed the presence of five Avr genes. Among them, Avr-Piz-t, Avr-Pik and Avr-Pia were more predominant., Significance and Impact of the Study: The study documented the morphological and genetic variability of eco-distinct M. oryzae isolates. This is the first study demonstrating the distribution of the Avr genes among the eco-distinct population of M. oryzae from southern India. The information generated will help plant breeders to select appropriate resistant gene/s combinations to develop blast disease-resistant rice cultivars., (© 2021 The Society for Applied Microbiology.)

As a definition, Human-Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs' complexity, so their usefulness should be carefully evaluated for the specific application.

In the current era, one of biggest challenges is to shorten the breeding cycle for rapid generation of a new crop variety having high yield capacity, disease resistance, high nutrient content, etc. Advances in the "-omics" technology have revolutionized the discovery of genes and bio-molecules with remarkable precision, resulting in significant development of plant-focused metabolic databases and resources. Metabolomics has been widely used in several model plants and crop species to examine metabolic drift and changes in metabolic composition during various developmental stages and in response to stimuli. Over the last few decades, these efforts have resulted in a significantly improved understanding of the metabolic pathways of plants through identification of several unknown intermediates. This has assisted in developing several new metabolically engineered important crops with desirable agronomic traits, and has facilitated the de novo domestication of new crops for sustainable agriculture and food security. In this review, we discuss how "omics" technologies, particularly metabolomics, has enhanced our understanding of important traits and allowed speedy domestication of novel crop plants., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Kumar, Sharma, Suresh, Ramrao, Veershetty, Kumar, Priscilla, Hangargi, Narasanna, Pandey, Naik, Thomas and Kumar.)

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Saha, Mamun, Ahmed, Mostafa, Naik, Darvishi, Khandoker and Baumert.)

The majority of the most economically important plant and crop species are enriched with the availability of high-quality reference genome sequences forming the basis of gene discovery which control the important biochemical pathways. The transcriptomics and proteomics resources have also been made available for many of these plant species that intensify the understanding at expression levels. However, still we lack integrated studies spanning genomics-transcriptomics-proteomics, connected to metabolomics, the most complicated phase in phenotype expression. Nevertheless, for the past few decades, emphasis has been more on metabolome which plays a crucial role in defining the phenotype (trait) during crop improvement. The emergence of modern high throughput metabolome analyzing platforms have accelerated the discovery of a wide variety of biochemical types of metabolites and new pathways, also helped in improving the understanding of known existing pathways. Pinpointing the causal gene(s) and elucidation of metabolic pathways are very important for development of improved lines with high precision in crop breeding. Along with other -omics sciences, metabolomics studies have helped in characterization and annotation of a new gene(s) function. Hereby, we summarize several areas in the field of crop development where metabolomics studies have made its remarkable impact. We also assess the recent research on metabolomics, together with other omics, contributing toward genetic engineering to target traits and key pathway(s).

Background: The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named 'Low-Complex Movement recognition-Net' (LoCoMo-Net) built with convolution neural network (CNN) for recognition of wrist and finger flexion movements; grasping and functional movements; and force pattern from single channel surface electromyography (sEMG) recording. The network consists of a two-stage pipeline: 1) input data compression; 2) data-driven weight sharing. Methods: The proposed framework was validated on two different datasets- our own dataset (DS1) and publicly available NinaPro dataset (DS2) for 16 movements and 50 movements respectively. Further, we have prototyped the proposed LoCoMo-Net on Virtex-7 Xilinx field-programmable gate array (FPGA) platform and validated for 15 movements from DS1 to demonstrate its feasibility for real-time execution. Results: The effectiveness of the proposed LoCoMo-Net was verified by a comparative analysis against the benchmarked models using the same datasets wherein our proposed model outperformed Twin- Support Vector Machine (SVM) and existing CNN based model by an average classification accuracy of 8.5 % and 16.0 % respectively. In addition, hardware complexity analysis is done to reveal the advantages of the two-stage pipeline where approximately 27 %, 49 %, 50 %, 23 %, and 43 % savings achieved in lookup tables (LUT's), registers, memory, power consumption and computational time respectively. Conclusion: The clinical significance of such sEMG based accurate and low-complex movement recognition system can be favorable for the potential improvement in quality of life of an amputated persons.

Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in turn, the Fractional norm (FN) feature is evaluated from the extracted coefficients at each subband. Then, the AF detection is carried out using a deep learning approach known as the Hierarchical Extreme Learning Machine (H-ELM) from the FN features. The proposed method is evaluated by considering normal and AF pathological ECG signals from public databases. The experimental results reveal that the proposed multi-rate cosine filter bank based on FN features is effective for the detection of AF pathology with an accuracy, sensitivity and specificity values of 99.40%, 98.77%, and 100%, respectively. The performance of the proposed diagnostic features of the ECG signal is compared with other existing features for the detection of AF. The low-frequency subband FN features found to be more significant with a difference of the mean values as 0.69 between normal and AF classes.

Human machine interfaces (HMIs) are employed in a broad range of applications, spanning from assistive devices for disability to remote manipulation and gaming controllers. In this study, a new piezoresistive sensors array armband is proposed for hand gesture recognition. The armband encloses only three sensors targeting specific forearm muscles, with the aim to discriminate eight hand movements. Each sensor is made by a force-sensitive resistor (FSR) with a dedicated mechanical coupler and is designed to sense muscle swelling during contraction. The armband is designed to be easily wearable and adjustable for any user and was tested on 10 volunteers. Hand gestures are classified by means of different machine learning algorithms, and classification performances are assessed applying both, the 10-fold and leave-one-out cross-validations. A linear support vector machine provided 96% mean accuracy across all participants. Ultimately, this classifier was implemented on an Arduino platform and allowed successful control for videogames in real-time. The low power consumption together with the high level of accuracy suggests the potential of this device for exergames commonly employed for neuromotor rehabilitation. The reduced number of sensors makes this HMI also suitable for hand-prosthesis control., (Copyright © 2020 Esposito, Andreozzi, Gargiulo, Fratini, D’Addio, Naik and Bifulco.)

Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology- Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%.

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

Groundtruth is a Matlab Graphical User Interface (GUI) developed for the identification of key features and artifacts within physiological signals. The ultimate aim of this GUI is to provide a simple means of assessing the performance of new sensors. Secondary, to this is providing a means of providing marked data, enabling assessment of automated artifact rejection and feature identification algorithms. With the emergence of new wearable sensor technologies, there is an unmet need for convenient assessment of device performance, and a faster means of assessing new algorithms. The proposed GUI allows interactive marking of artifact regions as well as simultaneous interactive identification of key features, e.g., respiration peaks in respiration signals, R-peaks in Electrocardiography signals, etc. In this paper, we present the base structure of the system, together with an example of its use for two simultaneously worn respiration sensors. The respiration rates are computed for both original as well as artifact removed data and validated using Bland-Altman plots. The respiration rates computed based on the proposed GUI (after artifact removal process) demonstrated consistent results for two respiration sensors after artifact removal process. Groundtruth is customizable, and alternative processing modules are easy to add/remove. Groundtruth is intended for open-source use.

Background and Objective: The congestive heart failure (CHF) is a life-threatening cardiac disease which arises when the pumping action of the heart is less than that of the normal case. This paper proposes a novel approach to design a classifier-based system for the automated detection of CHF., Methods: The approach is founded on the use of the Stockwell (S)-transform and frequency division to analyze the time-frequency sub-band matrices stemming from electrocardiogram (ECG) signals. Then, the entropy features are evaluated from the sub-band matrices of ECG. A hybrid classification scheme is adopted taking the sparse representation classifier and the average of the distances from the nearest neighbors into account for the detection of CHF. The proposition is validated using ECG signals from CHF subjects and normal sinus rhythm from public databases., Results: The results reveal that the proposed system is successful for the detection of CHF with an accuracy, a sensitivity and a specificity values of 98.78%, 98.48%, and 99.09%, respectively. A comparison with the existing approaches for the detection of CHF is accomplished., Conclusions: The time-frequency entropy features of the ECG signal in the frequency range from 11 Hz to 30 Hz have higher performance for the detection of CHF using a hybrid classifier. The approach can be used for the automated detection of CHF in tele-healthcare monitoring systems., (Copyright © 2019 Elsevier B.V. All rights reserved.)

This paper applies a nonparametric modelling method with kernel-based regularization to estimate the carbon dioxide production during jogging exercises. The kernel selection and regularization strategies have been discussed; several commonly used kernels are compared regarding the goodness-of-fit, sensitivity, and stability. Based on that, the most appropriate kernel is then selected for the construction of the regularization term. Both the onset and offset of the jogging exercises are investigated. We compare the identified nonparametric models, which include both impulse response models and step response models for the two periods, as well as the relationship between oxygen consumption and carbon dioxide production. The result statistically indicates that the steady-state gain of the carbon dioxide production in the onset of exercise is bigger than that in the offset while the response time of both onset and offset are similar. Compared with oxygen consumption, the response speed of carbon dioxide production is slightly slower in both onset and offset period while its steady-state gains are similar for both periods. The effectiveness of the kernel-based method for the dynamic modelling of cardiorespiratory response to exercise is also well demonstrated. Graphical Abstract Comparison between VO 2 and VCO 2 during onset and offset of exercise.

Accurate detection and classification of life-threatening ventricular arrhythmia episodes such as ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) from electrocardiogram (ECG) is a challenging problem for patient monitoring and defibrillation therapy. This paper introduces a novel method for detection and classification of life-threatening ventricular arrhythmia episodes. The ECG signal is decomposed into various oscillatory modes using digital Taylor-Fourier transform (DTFT). The magnitude feature and a novel phase feature namely the phase difference (PD) are evaluated from the mode Taylor-Fourier coefficients of ECG signal. The least square support vector machine (LS-SVM) classifier with linear and radial basis function (RBF) kernels is employed for detection and classification of VT vs. VF, non-shock vs. shock and VF vs. non-VF arrhythmia episodes. The accuracy, sensitivity, and specificity values obtained using the proposed method are 89.81, 86.38, and 93.97%, respectively for the classification of Non-VF and VF episodes. Comparison with the performance of the state-of-the-art features demonstrate the advantages of the proposition.

Background and Objective: EEG is a non-invasive tool for neuro-developmental disorder diagnosis and treatment. However, EEG signal is mixed with other biological signals including Ocular and Muscular artifacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners which may result in less accurate diagnosis. Many existing methods require reference electrodes, which will create discomfort to the patient/children and cause hindrance to the diagnosis of the neuro-developmental disorder and Brain Computer Interface in the pervasive environment. Therefore, it would be ideal if these artifacts can be removed real time on the hardware platform in an automated fashion and then the denoised EEG can be used for online diagnosis in a pervasive personalized healthcare environment without the need of any reference electrode., Methods: In this paper we propose a reliable, robust and automated methodology to solve the aforementioned problem. The proposed methodology is based on the Haar function based Wavelet decompositions with simple threshold based wavelet domain denoising and artifacts removal schemes. Subsequently hardware implementation results are also presented. 100 EEG data from Physionet, Klinik für Epileptologie, Universität Bonn, Germany, Caltech EEG databases and 7 EEG data from 3 subjects from University of Southampton, UK have been studied and nine exhaustive case studies comprising of real and simulated data have been formulated and tested. The proposed methodology is prototyped and validated using FPGA platform., Results: Like existing literature, the performance of the proposed methodology is also measured in terms of correlation, regression and R-square statistics and the respective values lie above 80%, 79% and 65% with the gain in hardware complexity of 64.28% and improvement in hardware delay of 53.58% compared to state-of-the art approaches. Hardware design based on the proposed methodology consumes 75 micro-Watt power., Conclusions: The automated methodology proposed in this paper, unlike the state of the art methods, can remove blink and muscular artifacts real time without the need of any extra electrode. Its reliability and robustness is also established after exhaustive simulation study and analysis on both simulated and real data. We believe the proposed methodology would be useful in next generation personalized pervasive healthcare for Brain Computer Interface and neuro-developmental disorder diagnosis and treatment., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Surface electromyography (sEMG) data acquired during lower limb movements has the potential for investigating knee pathology. Nevertheless, a major challenge encountered with sEMG signals generated by lower limb movements is the intersubject variability, because the signals recorded from the leg or thigh muscles are contingent on the characteristics of a subject such as gait activity and muscle structure. In order to cope with this difficulty, we have designed a three-step classification scheme. First, the multichannel sEMG is decomposed into activities of the underlying sources by means of independent component analysis via entropy bound minimization. Next, a set of time-domain features, which would best discriminate various movements, are extracted from the source estimates. Finally, the feature selection is performed with the help of the Fisher score and a scree-plot-based statistical technique, prior to feeding the dimension-reduced features to the linear discriminant analysis. The investigation involves 11 healthy subjects and 11 individuals with knee pathology performing three different lower limb movements, namely, walking, sitting, and standing, which yielded an average classification accuracy of 96.1% and 86.2%, respectively. While the outcome of this study per se is very encouraging, with suitable improvement, the clinical application of such an sEMG-based pattern recognition system that distinguishes healthy and knee pathological subjects would be an attractive consequence.

Freezing of gait (FOG) is an episodic gait disturbance affecting initiation and continuation of locomotion in many Parkinson's disease (PD) patients, causing falls and a poor quality of life. FOG can be experienced on turning and start hesitation, passing through doorways or crowded areas dual tasking, and in stressful situations. Electroencephalography (EEG) offers an innovative technique that may be able to effectively foresee an impending FOG. From data of 16 PD patients, using directed transfer function (DTF) and independent component analysis (ICA) as data pre-processing, and an optimal Bayesian neural network as a predictor of a transition of 5 seconds before the impending FOG occurs in 11 in-group PD patients, we achieved sensitivity and specificity of 85.86% and 80.25% respectively in the test set (5 out-group PD patients). This study therefore contributes to the development of a non-invasive device to prevent freezing of gait in PD.

The purpose of this study was to determine whether electromyography (EMG) muscle activities around the knee differ during sit-to-stand (STS) and returning task for females wearing shoes with different heel heights. Sixteen healthy young women (age = 25.2 ± 3.9 years, body mass index = 20.8 ± 2.7 kg/m 2 ) participated in this study. Electromyography signals were recorded from the two muscles, vastus medialis (VM) and vastus lateralis (VL) that involve in the extension of knee. The participants wore shoes with five different heights, including 4, 6, 8, 10, and 12 cm. Surface electromyography (sEMG) data were acquired during STS and stand-to-sit-returning (STSR) tasks. The data was filtered using a fourth order Butterworth (band pass) filter of 20-450 Hz frequency range. For each heel height, we extracted median frequency (MDF) and root mean square (RMS) features to measure sEMG activities between VM and VL muscles. The experimental results (based on MDF and RMS-values) indicated that there is imbalance between vasti muscles for more elevated heels. The results are also quantified with statistical measures. The study findings suggest that there would be an increased likelihood of knee imbalance and fatigue with regular usage of high heel shoes (HHS) in women.

The purpose of this study was to investigate differences in lower limb muscle activation patterns for females wearing shoes with different heel heights during Sit to Stand Task (STS). Ten female participants with no prior history of neurological disorders participated in this study. Surface electromyography (sEMG) characteristics were recorded for four different heel heights (ranging from 4cm to 10cm) while performing the STS task. Signal processing analysis suggests that muscle activities increases on elevated heel heights, which may induce muscle imbalance for frequent STS tasks. In addition, results of muscle utilisation (percentage) for different heel heights suggest that lower limb muscles tend to compensate in order to maintain postural balance.

In recent years, significant advancements have taken place in human activity recognition using various machine learning approaches. However, feature engineering have dominated conventional methods involving the difficult process of optimal feature selection. This problem has been mitigated by using a novel methodology based on deep learning framework which automatically extracts the useful features and reduces the computational cost. As a proof of concept, we have attempted to design a generalized model for recognition of three fundamental movements of the human forearm performed in daily life where data is collected from four different subjects using a single wrist worn accelerometer sensor. The validation of the proposed model is done with different pre-processing and noisy data condition which is evaluated using three possible methods. The results show that our proposed methodology achieves an average recognition rate of 99.8% as opposed to conventional methods based on K-means clustering, linear discriminant analysis and support vector machine.

This paper introduces a novel shape memory alloy (SMA) material for the controllability in the shape recovery of traditional SMA for orthopedic devices and rehabilitation techniques. The proposed material is formed by doping nano-ferromagnetic particle into porous NiTi alloy. The finite element analysis of shape memory effect property of the different distribution of nano-ferromagnetic particle is done and compared for same load and boundary conditions. The comparative analysis of the percentage change in volume deformation when load is released (for 2 nd step) shows an average of 2.55 % with standard deviation of 1.69 whereas on thermal loading (for 3 rd step) shows an average of 94.94% with standard deviation of 7.75 for all heterogeneous distribution of nano-particles in porous NiTi alloy. Our findings are, all the different conditions of heterogeneous distributions of nano-ferromagnetic particle doped NiTi alloy exhibits its inherent SME property.

Freezing of Gait (FOG) is a highly debilitating and poorly understood symptom of Parkinson's disease (PD), causing severe immobility and decreased quality of life. Turning Freezing (TF) is known as the most common sub-type of FOG, also causing the highest rate of falls in PD patients. During a TF, the feet of PD patients appear to become stuck whilst making a turn. This paper presents an electroencephalography (EEG) based classification method for detecting turning freezing episodes in six PD patients during Timed Up and Go Task experiments. Since EEG signals have a time-variant nature, time-frequency Stockwell Transform (S-Transform) techniques were used for feature extraction. The EEG sources were separated by means of independent component analysis using entropy bound minimization (ICA-EBM). The distinctive frequency-based features of selected independent components of EEG were extracted and classified using Bayesian Neural Networks. The classification demonstrated a high sensitivity of 84.2%, a specificity of 88.0% and an accuracy of 86.2% for detecting TF. These promising results pave the way for the development of a real-time device for detecting different sub-types of FOG during ambulation.

This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.

A great many people suffer from neurological movement disorders that render typical hardware interface devices ineffective. A need exists for a universal interface device that can be trained to accept a wide range of inputs across varying types and severities of movement disorders. In this regard, this paper details the design, testing and optimization of an accelerometer-based gesture identification system. A Bluetooth-enabled IMU mounted on the wrist provides hand motion trajectory information to a local terminal. Several techniques are applied to decrease the intra-class variance and reduce classifier complexity including filtering, segmentation and temporal scaling. Datasets consisted of 520 training samples, 260 validation samples and a further 520 testing samples. A multi-layer feed forward artificial neural network (ML-FFNN) was used to classify the input space into 26 different classes. Initial system accuracy, using arbitrary hyperparameters was 77.69% with final optimized accuracy at 99.42%.

Gait initiation Failure (GIF) is the situation in which patients with Parkinson's disease (PD) feel as if their feet get "stuck" to the floor when initiating their first steps. GIF is a subtype of Freezing of Gait (FOG) and often leads to falls and related injuries. Understanding of neurobiological mechanisms underlying GIF has been limited by difficulties in eliciting and objectively characterizing such gait phenomena in the clinical setting. Studies investigating the effects of GIF on brain activity using EEG offer the potential to study such behavior. In this preliminary study, we present a novel methodology where wavelet transform was used for feature extraction and Support Vector Machine for classifying GIF events in five patients with PD and FOG. To deal with the large amount of EEG data, a Principal Component Analysis (PCA) was applied to reduce the data dimension from 15 EEG channels into 6 principal components (PCs), retaining 93% of the information. Independent Component Analysis using Entropy Bound Minimization (ICA-EBM) was applied to 6 PCs for source separation with the aim of improving detection ability of GIF events as compared to the normal initiation of gait (Good Starts). The results of this analysis demonstrated the correct identification of GIF episodes with an 83.1% sensitivity, 89.5% specificity and 86.3% accuracy. These results suggest that our proposed methodology is a promising non-invasive approach to improve GIF detection in PD and FOG.

Background: Congenital talipes equinovarus (CTEV), also known as clubfoot, is common congenital orthopedic foot deformity in children characterized by four components of foot deformities: hindfoot equinus, hindfoot varus, midfoot cavus, and forefoot adduction. Although a number of conservative and surgical methods have been proposed to correct the clubfoot deformity, the relapses of the clubfoot are not uncommon. Several previous literatures discussed about the technical details of Ponseti method, adherence of Ponseti protocol among walking age or older children. However there is a necessity to investigate the relapse pattern, compliance of bracing, number of casts used in treatment and the percentages of surgical referral under two years of age for clear understanding and better practice to achieve successful outcome without or reduce relapse. Therefore this study aims to review the current evidence of Ponseti method (manipulation, casting, percutaneous Achilles tenotomy, and bracing) in the management of clubfoot under two years of age., Materials and Methods: Articles were searched from 2000 to 2015, in the following databases to identify the effectiveness of Ponseti method treatment for clubfoot: Medline, Cumulative Index to Nursing and Allied Health Literature (CINHAL), PubMed, and Scopus. The database searches were limited to articles published in English, and articles were focused on the effectiveness of Ponseti method on children with less than 2 years of age., Results: Of the outcome of 1095 articles from four electronic databases, twelve articles were included in the review. Pirani scoring system, Dimeglio scoring system, measuring the range of motion and rate of relapses were used as outcome measures., Conclusions: In conclusion, all reviewed, 12 articles reported that Ponseti method is a very effective method to correct the clubfoot deformities. However, we noticed that relapses occur in nine studies, which is due to the non-adherence of bracing regime and other factors such as low income and social economic status.

This paper presents a two-class electroencephal-ography-based classification for classifying of driver fatigue (fatigue state versus alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction, and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8%, and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor), and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC = 0.93) against other methods such as power spectral density as feature extractor (AUC-ROC = 0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.

Background: One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals., Methods: This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia., Results: Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.

Surface electromyography (sEMG)-based pattern recognition studies have been widely used to improve the classification accuracy of upper limb gestures. Information extracted from multiple sensors of the sEMG recording sites can be used as inputs to control powered upper limb prostheses. However, usage of multiple EMG sensors on the prosthetic hand is not practical and makes it difficult for amputees due to electrode shift/movement, and often amputees feel discomfort in wearing sEMG sensor array. Instead, using fewer numbers of sensors would greatly improve the controllability of prosthetic devices and it would add dexterity and flexibility in their operation. In this paper, we propose a novel myoelectric control technique for identification of various gestures using the minimum number of sensors based on independent component analysis (ICA) and Icasso clustering. The proposed method is a model-based approach where a combination of source separation and Icasso clustering was utilized to improve the classification performance of independent finger movements for transradial amputee subjects. Two sEMG sensor combinations were investigated based on the muscle morphology and Icasso clustering and compared to Sequential Forward Selection (SFS) and greedy search algorithm. The performance of the proposed method has been validated with five transradial amputees, which reports a higher classification accuracy ( > 95%). The outcome of this study encourages possible extension of the proposed approach to real time prosthetic applications.

This paper presents an electroencephalography (EEG) based-classification of between pre- and post-mental load tasks for mental fatigue detection from 65 healthy participants. During the data collection, eye closed and eye open tasks were collected before and after conducting the mental load tasks. For the computational intelligence, the system uses the combination of principal component analysis (PCA) as the dimension reduction method of the original 26 channels of EEG data, power spectral density (PSD) as feature extractor and Bayesian neural network (BNN) as classifier. After applying the PCA, the dimension of the data is reduced from 26 EEG channels in 6 principal components (PCs) with above 90% of information retained. Based on this reduced dimension of 6 PCs of data, during eyes open, the classification pre-task (alert) vs. post-task (fatigue) using Bayesian neural network resulted in sensitivity of 76.8 %, specificity of 75.1% and accuracy of 76% Also based on data from the 6 PCs, during eye closed, the classification between pre- and post-task resulted in a sensitivity of 76.1%, specificity of 74.5% and accuracy of 75.3%. Further, the classification results of using only 6 PCs data are comparable to the result using the original 26 EEG channels. This finding will help in reducing the computational complexity of data analysis based on 26 channels of EEG for mental fatigue detection.

In this paper we introduce Shape Memory Alloy (SMA) for designing the tibial part of Total Knee Arthroplasty (TKA) by exploiting the shape-memory and pseudo-elasticity property of the SMA (e.g. NiTi). This would eliminate the drawbacks of the state-of-the art PMMA based knee-spacer including fracture, sustainability, dislocation, tilting, translation and subluxation for tackling the Osteoarthritis especially for the aged people of 45-plus or the athletes. In this paper a Computer Aided Design (CAD) model using SolidWorks for the knee-spacer is presented based on the proposed SMA adopting the state-of-the art industry-standard geometry that is used in the PMMA based spacer design. Subsequently Ansys based Finite Element Analysis is carried out to measure and compare the performance between the proposed SMA based model with the state-of-the art PMMA ones. 81% more bending is noticed in the PMMA based spacer compared to the proposed SMA that would eventually cause fracture and tilting or translation of spacer. Permanent shape deformation of approximately 58.75% in PMMA based spacer is observed compared to recoverable 11% deformation in SMA when same load is applied on both separately.

Nowadays portable devices with more number of sensors are used for gait assessment and monitoring for elderly and disabled. However, the problem with using multiple sensors is that if they are placed on the same platform or base, there could be cross talk between them, which could change the signal amplitude or add noise to the signal. Hence, this study uses wavelet PCA as a signal processing technique to separate the original sensor signal from the signal obtained from the sensors through the integrated unit to compare the two types of walking (with and without an exoskeleton). This comparison using wavelet PCA will enable the researchers to obtain accurate sensor data and compare and analyze the data in order to further improve the design of compact portable devices used to monitor and assess the gait in stroke or paralyzed subjects. The advantage of designing such systems is that they can also be used to assess and monitor the gait of the stroke subjects at home, which will save them time and efforts to visit the laboratory or clinic.

In this paper we propose an on-the-fly reconfigurable hardware-software codesign based reconfigurable solution for real-time protein identification. Reconfigurable string matching is performed in the disciplines of protein identification and biomarkers discovery. With the generation of plethora of sequenced data and number of biomarkers for several diseases, it is becoming necessary to have an accelerated processing and on-the-fly reconfigurable system design methodology to bring flexibility to its usage in the medical science community without the need of changing the entire hardware every time with the advent of new biomarker or protein. The proteome database of human at UniProtKB (Proteome ID up000005640) comprising of 42132 canonical and isoform proteins with variable database-size are used for testing the proposed design and the performance of the proposed system has been found to compare favorably with the state-of-the-art approaches with the additional advantage of real-time reconfigurability.

An accurate and computationally efficient quantitative analysis of electromyography (EMG) signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and several related applications. Since it is often the case that the measured signals are the mixtures of electric potentials that emanate from surrounding muscles (sources), many EMG signal processing approaches rely on linear source separation techniques such as the independent component analysis (ICA). Nevertheless, naive implementations of ICA algorithms do not comply with the task of extracting the underlying sources from a single-channel EMG measurement. In this respect, the present work focuses on a classification method for neuromuscular disorders that deals with the data recorded using a single-channel EMG sensor. The ensemble empirical mode decomposition algorithm decomposes the single-channel EMG signal into a set of noise-canceled intrinsic mode functions, which in turn are separated by the FastICA algorithm. A reduced set of five time domain features extracted from the separated components are classified using the linear discriminant analysis, and the classification results are fine-tuned with a majority voting scheme. The performance of the proposed method has been validated with a clinical EMG database, which reports a higher classification accuracy (98%). The outcome of this study encourages possible extension of this approach to real settings to assist the clinicians in making correct diagnosis of neuromuscular disorders.

Identification of simple and complex finger flexion movements using surface electromyography (sEMG) and a muscle activation strategy is necessary to control human-computer interfaces such as prosthesis and orthoses. In order to identify these movements, sEMG sensors are placed on both anterior and posterior muscle compartments of the forearm. In general, the accuracy of myoelectric classification depends on several factors, which include number of sensors, features extraction methods, and classification algorithms. Myoelectric classification using a minimum number of sensors and optimal electrode configuration is always a challenging task. Sometimes, using several sensors including high density electrodes will not guarantee high classification accuracy. In this research, we investigated the dependence and independence nature of anterior and posterior muscles during simple and complex finger flexion movements. The outcome of this research shows that posterior parts of the hand muscles are dependent and hence responsible for most of simple finger flexion. On the other hand, this study shows that anterior muscles are responsible for most complex finger flexion. This also indicates that simple finger flexion can be identified using sEMG sensors connected only on anterior muscles (making posterior placement either independent or redundant), and vice versa is true for complex actions which can be easily identified using sEMG sensors on posterior muscles. The result of this study is beneficial for optimal electrode configuration and design of prosthetics and other related devices using a minimum number of sensors.

An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p <; 0.05).

Surface electromyography (sEMG) is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. The sEMG is a noninvasive, easy to record signal of superficial muscles from the skin surface. Considering the nonstationary characteristics of sEMG, recent feature selection of hand gesture recognition using sEMG signals necessitate designers to use nonnegative matrix factorization (NMF)-based methods. This method exploits both the additive and sparse nature of signals by extracting accurate and reliable measurements of sEMG features using a minimum number of sensors. The testing has been conducted for simple and complex finger flexions using several experiments with artificial neural network classification scheme. It is shown, both by simulation and experimental studies, that the proposed algorithm is able to classify ten finger flexions (five simple and five complex finger flexions) recorded from two sEMG sensors up to 92% (95% for simple and 87% for complex flexions) accuracy. The recognition performances of simple and complex finger flexions are also validated with NMF permutation matrix analysis.

This study uses multiscale principal component analysis (MSPCA) signal processing technique in order to distinguish the two different surfaces, tiled (regular) and cobbled (irregular) using accelerometry data (recorded from MTx sensors). Two MTx sensors were placed on the head and trunk of the subject while the subject walked freely over the regular and irregular surfaces during a free walk. 3D acceleration signals, vertical, medio lateral (ML) and anterior-posterior (AP) were recorded for the head and trunk segments and compared for the free walk on a defined route. The magnitude of the ML and AP acceleration obtained from the MTx sensors (for both head & trunk) was higher when walking over the irregular (cobbled) surface as compared to the regular (tiled) surface. The accelerometry data was initially analysed using MSPCA and was later classified using naïve Bayesian classifier with >86% accuracy. This research study demonstrates that MSPCA can be used to distinguish the regular and irregular surfaces. The proposed method could be very useful as an automated method for classification of the two surfaces.

In this paper, we propose a high speed architecture design methodology for the Under-determined Blind Source Separation (UBSS) algorithm using our recently proposed high speed Discrete Hilbert Transform (DHT) targeting real time applications. In UBSS algorithm, unlike the typical BSS, the number of sensors are less than the number of the sources, which is of more interest in the real time applications. The DHT architecture has been implemented based on sub matrix multiplication method to compute M point DHT, which uses N point architecture recursively and where M is an integer multiples of N. The DHT architecture and state of the art architecture are coded in VHDL for 16 bit word length and ASIC implementation is carried out using UMC 90 - nm technology @V DD = 1V and @ 1MHZ clock frequency. The proposed architecture implementation and experimental comparison results show that the DHT design is two times faster than state of the art architecture.

Electroencephalograms (EEGs) are progressively emerging as a significant measure of brain activity and are very effective tool for the diagnosis and treatment of mental and brain diseases and disorders including sleep apnea, Alzheimer's disease and Neurodevelopmental disorders. However, EEG signal is mixed with other biological signals including Ocular and Muscular artefacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners resulting less accurate diagnosis. In this paper we propose a real-time low-complexity and reliable system design methodology to remove these artefacts and noise in an automated fashion to aid online diagnosis under the pervasive personalized healthcare set-up without the need of any reference electrode. The simulation and hardware performance of the proposed methodology are measured and compared in terms of correlation and regression statistics lying above 80% and 67% which are much improved over the state-of-the art methodologies.

This paper introduces a dual-mode low complex on-chip methodology for processing of ECG (Electrocardiogram) and EEG (Electroencephalography) signals, wherein based on the input switch the architecture can be dynamically configured to operate either as an ECG bio-marker or EEG signal de-noising system. In both the modes the signal processing technique depends on the output of the DWT (Discrete Wavelet Transform), hence a low complex methodology has been developed in which both ECG and EEG processing blocks sharing the same DWT block resulting in low area and low power consumption. The integrated ECG and EEG methodology has been implemented in Matlab, for verifying the ECG processing block the ECG database is taken from MIT-BIH PTBDB and IITH DB, similarly for EEG processing block the EEG signals are taken from PhysioNet database. The outcome of methodology in Matlab is equal to the results obtained from individual ECG and EEG blocks.

Due to lack of morphological methods to identify sex at early stage in the plants with long juvenile period the application of molecular markers is expected to facilitate breeding program. The objective of this study is to identify molecular markers linked to sex determination of the plant Simarouba glauca which assists in crop improvement program. Random amplified polymorphic DNA primers were tested on dioeceious and hermaphrodite plant Simarouba glauca . A set of eighty five RAPD primers were screened out of which only five primers were found to be associated with sex. The primer OPU-10 is male specific and OPD-19 primer is female specific. Another primer OPU-19 produced a unique amplification in only hermaphrodite individuals. Female and hermaphrodite specific primer OPS-05 amplified an amplicon in female and hermaphrodite and was absent in male plant. Primer OPW-03 produced amplicon specific to male and hermaphrodite plants and was absent in female plants.

Pigeonpea (Cajanus cajan (L) Millsp.) is an important food legume crop of rain fed agriculture in the arid and semiarid tropics of the world. It has deep and extensive root system which serves a number of important physiological and metabolic functions in plant development and growth. In order to identify genes associated with pigeonpea root, ESTs were generated from the root tissues of pigeonpea (GRG-295 genotype) by normalized cDNA library. A total of 105 high quality ESTs were generated by sequencing of 250 random clones which resulted in 72 unigenes comprising 25 contigs and 47 singlets. The ESTs were assigned to 9 functional categories on the basis of their putative function. In order to validate the possible expression of transcripts, four genes, namely, S-adenosylmethionine synthetase, phosphoglycerate kinase, serine carboxypeptidase, and methionine aminopeptidase, were further analyzed by reverse transcriptase PCR. The possible role of the identified transcripts and their functions associated with root will also be a valuable resource for the functional genomics study in legume crop.

This paper reports the classification of finger flexion and extension of surface Electromyography (EMG) and Cyberglove data using the modified Independent Component Analysis (ICA) weight matrix. The finger flexion and extension data are processed through Principal Component Analysis (PCA), and next separated using modified ICA for each individual with customized weight matrix. The extension and flexion features of sEMG and Cyberglove (extracted from modified ICA) were classified using Linear Discriminant Analysis (LDA) with near 90% classification accuracy. The applications of this study include Human Computer Interface (HCI), virtual reality and neural prosthetics.