Feature Analysis for Classification of Physical Actions using surface EMG Data

Based on recent health statistics, there are several thousands of people with limb disability and gait disorders that require a medical assistance. A robot assisted rehabilitation therapy can help them recover and return to a normal life. In this scenario, a successful methodology is to use the EMG signal based information to control the support robotics. For this mechanism to function properly, the EMG signal from the muscles has to be sensed and then the biological motor intention has to be decoded and finally the resulting information has to be communicated to the controller of the robot. An accurate detection of the motor intention requires a pattern recognition based categorical identification. Hence in this paper, we propose an improved classification framework by identification of the relevant features that drive the pattern recognition algorithm. Major contributions include a set of modified spectral moment based features and another relevant inter-channel correlation feature that contribute to an improved classification performance. Next, we conducted a sensitivity analysis of the classification algorithm to different EMG channels. Finally, the classifier performance is compared to that of the other state-of the art algorithm

An Improved Compound Gaussian Model for Bivariate Surface EMG Signals Related to Strength Training

Recent literature suggests that the surface electromyography (sEMG) signals have non-stationary statistical characteristics specifically due to random nature of the covariance. Thus suitability of a statistical model for sEMG signals is determined by the choice of an appropriate model for describing the covariance. The purpose of this study is to propose a Compound-Gaussian (CG) model for multivariate sEMG signals in which latent variable of covariance is modeled as a random variable that follows an exponential model. The parameters of the model are estimated using the iterative Expectation Maximization (EM) algorithm. Further, a new dataset, electromyography analysis of human activities database 2 (EMAHA-DB2) is developed. Based on the model fitting analysis on the sEMG signals from EMAHA-DB2, it is found that the proposed CG model fits more closely to the empirical pdf of sEMG signals than the existing models. The proposed model is validated by visual inspection, further validated by matching central moments and better quantitative metrics in comparison with other models. The proposed compound model provides an improved fit to the statistical behavior of sEMG signals. Further, the estimate of rate parameter of the exponential model shows clear relation to the training weights. Finally, the average signal power estimates of the channels shows distinctive dependency on the training weights, the subject's training experience and the type of activity.Comment: This article supersedes arXiv:2301.05417. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

IIDS: Design of Intelligent Intrusion Detection System for Internet-of-Things Applications

With rapid technological growth, security attacks are drastically increasing. In many crucial Internet-of-Things (IoT) applications such as healthcare and defense, the early detection of security attacks plays a significant role in protecting huge resources. An intrusion detection system is used to address this problem. The signature-based approaches fail to detect zero-day attacks. So anomaly-based detection particularly AI tools, are becoming popular. In addition, the imbalanced dataset leads to biased results. In Machine Learning (ML) models, F1 score is an important metric to measure the accuracy of class-level correct predictions. The model may fail to detect the target samples if the F1 is considerably low. It will lead to unrecoverable consequences in sensitive applications such as healthcare and defense. So, any improvement in the F1 score has significant impact on the resource protection. In this paper, we present a framework for ML-based intrusion detection system for an imbalanced dataset. In this study, the most recent dataset, namely CICIoT2023 is considered. The random forest (RF) algorithm is used in the proposed framework. The proposed approach improves 3.72%, 3.75% and 4.69% in precision, recall and F1 score, respectively, with the existing method. Additionally, for unsaturated classes (i.e., classes with F1 score < 0.99), F1 score improved significantly by 7.9%. As a result, the proposed approach is more suitable for IoT security applications for efficient detection of intrusion and is useful in further studies

“Escape” of aldosterone production in patients with left ventricular dysfunction treated with an angiotensin converting enzyme inhibitor: Implications for therapy

Despite the findings in randomized trials of a significant effect of angiotensin-converting enzyme (ACE) inhibitors in reducing morbidity and mortality of patients with symptomatic left ventricular dysfunction, the morbidity and mortality of these patients remains relatively high. One potential strategy to further improve morbidity and mortality in these patients is blockade of aldosterone. Many clinicians have assumed that ACE inhibitors would block both angiotensin II and aldosterone. However, there are data to suggest that aldosterone production may “escape” despite the use of an ACE inhibitor. An escape of aldosterone production has several important consequences, including: sodium retention, potassium and magnesium loss, myocardial collagen production, ventricular hypertrophy, myocardial norepinephrine release, endothelial dysfunction, and a decrease in serum high density lipoprotein cholesterol. Due to the potential importance of these mechanisms, the finding that there is a significant correlation between aldosterone production and mortality in patients with heart failure, as well as evidence that an aldosterone antagonist, spironolactone, when administered to patients with heart failure treated with conventional therapy including an ACE inhibitor results in increased diuresis and symptomatic improvement, an international prospective multicenter study has been organized, the Randomized Aldactone Evaluation Study (RALES Pilot Study), to evaluate the safety of blocking the effects of aldosterone in patients with heart failure treated with an ACE inhibitor.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44631/1/10557_2004_Article_BF00877755.pd

Implementation and Performance Comparison of Some Heuristic Algorithms for Block Sorting

An implementation framework has been developed in this thesis for a well-known APX-hard combinatorial optimization problem known as Block Sorting. The motivation for the study of this problem comes from applications such as computational biology and optical character recognition. While existing Block Sorting research has been theoretically focused on the development and analysis of several approximation algorithms for Block Sorting, little or no work has been carried out thus far on the implementation of the proposed approximation algorithms. The conceptualization of an implementation framework and illustrating its use by experimenting with the existing approximation algorithms will provide means for discovering newer approaches to handling this important problem. As the main contribution, the research in this thesis provides a new greedy algorithm for Block Sorting in which each block move either reduces the number of blocks by two or three blocks, or reduces by one the number of reversals or inversions in the orig- inal permutation. Experimental results for all algorithms are also provided along with a comparison of their performance using the number of block moves and approximation ratios as performance metrics when sorting permutations of a given order, and as the order of permutations is varied. Preliminary results from the experimentation were shared at the 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) [1]. To the best of our knowledge, this is the first work that has been focused on the implementation and experimental performance analysis of any algorithm for Block Sorting. We believe the results presented in this thesis will be useful for researchers and practitioners working in this area

Central nervous system stimulant from the sea anemone

Typescript.Bibliography: leaves 87-94.ix, 94 l illusA central nervous system stimulant has been isolated from the sea anemone, Stoichactis kenti. A chromatographically homogeneous fraction has been obtained from the crude extract by dialysis and gel filtration on Sephadex G-50. Attempts were made to purify the active fraction further by ion exchange column chromatography, but the specific activity of the active fraction was not increased. The active substance was found to be water soluble, heat and acid labile and stable to alkali. It showed a positive color reaction with ninhydrin on paper (circular) chromatography, using n-butanol: acetic acid:water (4:1:5) system. Steroids, steroidal glycosides, nucleic acids, lipids and carbohydrates were found to be absent in the active fraction, when tested with specific reagents. The active fraction has a characteristic u.v. maximum at 277.5 nm. Determination of protein by Lowry's method and estimation of nitrogen by Kjeldahl's method indicated the active fraction was rich in protein. An acid and alkaline hydrolysis of the active fraction was carried out and hydrolysates were analyzed both by two dimensional chromatography and Technicon auto aminoacid analyzer. The following amino acids were identified by comparing with standard aminoacids: cysteine, aspartic acid (asparagine), threonine, serine, glutamic acid (glutamine), proline, glycine, alanine, valine, cystine, isoleucine, leucine, tyrosine, phenylalanine, lysine, histidine and arginine. From these results it was concluded that the active fraction was a polypeptide containing seventeen different aminoacids. Based on behavior on Sephadex G-50, the approximate molecular weight of the active fraction was estimated to be in the range of 2,500 - 3,000. Signs of central nervous system stimulatory activity produced by the active fraction in male mice included fighting episodes, increased motor activity and clonic convulsions. The ED50 of the active fraction based on fighting episodes was 6.4 mg/kg. The fighting episodes occurred at a frequency of 2-3 times/minute. After the administration of the active fraction intraperitoneally, the fighting episodes started within 4-6 minutes, peaked at 15 minutes and waned within about 30 minutes. The LD50 dose of the active fraction was 12.2 mg/kg. Toxic symptoms such as ataxia, catalepsy and tonic convulsions were observed before death. Phenobarbital sodium, chlorpromazine and methocarbamol completely blocked the fighting response of the active fraction even at the ED100 dose level but did not change the LD50. The antagonism of the active fraction induced stimulant activity (as measured by fighting episodes) by these drugs suggests that this activity was probably mediated centrally. Reserpine and tetrabenazine pretreatment markedly increased the stimulant effect of the active fraction by decreasing the ED50 of the active fraction by 50%. Such treatment increased toxicity twofold. a-methyl p-tyrosine methylester Hel (α-MPT) pretreatment did not alter the ED50, while the LD50 was significantly decreased. When α-MPT treatment was incorporated in reserpine or tetrabenazine treated animals, the stimulatory activity of the active fraction was completely blocked even at the ED100 (9.3 mg/kg) dose level. The active fraction produced a significant decrease in brain norepinephrine content at the ED50 and the ED100 doses during the stimulation period. Both the active fraction and reserpine produced a hyperthermic response in mice. DL-dopa treatment restored the active fraction induced stimulant action (fighting episodes) which was abolished after combined treatment with a-MPT and reserpine and reserpine and disulfiram. DL-dopa also increased the LD50 of the active fraction. The active fraction at the ED50 dose significantly decreased brain dopamine content. Pretreatment with p-chlorophenylalanine did not alter the ED50 and the LD50 of the active fraction. No change in brain serotonin content was observed after administration of the active fraction at the ED50 dose. The active fraction at the ED50 dose significantly inhibited the re-uptake mechanism of norepinephrine during the stimulation period. It also elevated normetanephrine levels at the ED50 dose. Propranolol but not phentolamine treatment completely blocked the stimulatory action of the active fraction, with no change in the LD50. Atropine treatment decreased the toxicity, with no change in the ED50. On the other hand physostigmine blocked the stimulatory action and increased toxicity by twofold. In conclusion, the results suggest that the active fraction causes stimulant action by releasing active norepinephrine from functional pools and inhibiting its re-uptake, thus making more norepinephrine available at adrenergic receptors

Application of pattern recognition and adaptive DSP methods for spatio-temporal analysis of satellite based hydrological datasets

Data assimilation of satellite-based observations of hydrological variables with full numerical physics models can be used to downscale these observations from coarse to high resolution to improve microwave sensor-based soil moisture observations. Moreover, assimilation can also be used to predict related hydrological variables, e.g., precipitation products can be assimilated in a land information system to estimate soil moisture. High quality spatio-temporal observations of these processes are vital for a successful assimilation which in turn needs a detailed analysis and improvement. In this research, pattern recognition and adaptive signal processing methods are developed for the spatio-temporal analysis and enhancement of soil moisture and precipitation datasets. These methods are applied to accomplish the following tasks: (i) a consistency analysis of level-3 soil moisture data from the Advanced Microwave Scanning Radiometer – EOS (AMSR-E) against in-situ soil moisture measurements from the USDA Soil Climate Analysis Network (SCAN). This method performs a consistency assessment of the entire time series in relation to others and provides a spatial distribution of consistency levels. The methodology is based on a combination of wavelet-based feature extraction and oneclass support vector machines (SVM) classifier. Spatial distribution of consistency levels are presented as consistency maps for a region, including the states of Mississippi, Arkansas, and Louisiana. These results are well correlated with the spatial distributions of average soil moisture, and the cumulative counts of dense vegetation; (ii) a modified singular spectral analysis based interpolation scheme is developed and validated on a few geophysical data products including GODAE’s high resolution sea surface temperature (GHRSST). This method is later employed to fill the systematic gaps in level-3 AMSR-E soil moisture dataset; (iii) a combination of artificial neural networks and vector space transformation function is used to fuse several high resolution precipitation products (HRPP). The final merged product is statistically superior to any of the individual datasets over a seasonal period. The results have been tested against ground based measurements of rainfall over our study area and average accuracies obtained are 85% in the summer and 55% in the winter 2007

Implementation and Performance Comparison of Some Heuristic Algorithms for Block Sorting

Block Sorting is an APX-hard combinatorial optimization problem motivated by applications in genome rearrangements. While previous works on block sorting have mainly focused on theoretical analyses of approximation algorithms, in this work we develop an implementation framework for block sorting in the Java programming language. Our framework uses a simple data structure for a block and includes methods to: manipulate blocks; extract useful information about blocks at various stages in a block sorting algorithm; and perform block moves. We use this framework to implement algorithms for block sorting, based on three different heuristics: 1) each block move guarantees a reduction of at least one block; 2) block moves are performed only on those blocks which are not members of the longest increasing subsequence (of blocks) in the original permutation; and 3) each block move either reduces the number of blocks by two or three blocks, or reduces the number of reversals or inversions in the original permutation. We analyze and compare these algorithms for their performance as the order of permutations is varied, using the measures of number of block moves and approximation ratios when sorting kernelized permutations of a given order. To the best of our knowledge this is the first work that is focused on implementation and experimental performance analysis of any algorithm for block sorting. We believe our results will be useful for researchers and practitioners working in this area