Analyze the Human Movements to Help CNS to Shape the Synergy using CNMF and Pattern Recognition

© 2017 The Authors. The Biomedical Signals have been studied for developing human control systems to improving the quality of life. The EMG signal is one of the main types of biomedical signals. It is a convoluted signal. This signal (EMG signal) controlled by the Central nervous system (CNS). It has been a long time expected that the human central nervous system (CNS) uses flexible combinations of some muscles synergy (MS) to solve and control redundant movements. Synergy muscles activities are different in a single muscle. In the concept of Synergy muscle, the CNS does not directly control the activation of a large number of muscles. There are two main movements can help CNS to shape the synergy. The automatic body response and the voluntary actions. These activities remain not too bright. Some studies support the hypothesis that the automatic body responses could be used as a reference to familiarize the voluntary efforts. It has been validating by analyzing the human voluntary movement and the automatic mechanical motions from the muscle synergy. Based on the validation, there was a proposition that the automatic synergy motion may express some features which could support the CNS to shape the voluntary synergy motion using the nonnegative matrix factorization (NMF). Thus the target of the presenting work is to analyses the human movements from the muscle synergy to help CNS shapes the synergy movement by suggestion using the concatenated non-negative matrix factorization (CNMF) method and the pattern recognition method. Then compare the two results and see if that help CNS to shape the synergy movements and which method has more accuracy

Upper limb recovery prediction after stroke rehabilitation based on regression method

© Springer Nature Switzerland AG 2019. In this paper, we investigate the possibility of a machine-learning algorithm using the Support Victor Machine Regression (SVMR) to predict the motor functional recovery of moderate post stroke patients during their rehabilitation program. To train the model, we used the recorded electromyography (EMG) signals from the upper limb muscles of the patients during their initial rehabilitation sessions. Then we tested the trained model to predict the later muscles performance of the patient during the same sessions. The results of this pilot study were promising; data were, to some extent, predictable. We believe such research direction could be essential to motivate the patient to complete the designed rehabilitation program and can assist the therapist to innovate proper rehabilitation menu for individual patients

Multi label restaurant classification using support vector machine

Many internet websites are hosted with a vast amount of information about restaurants which are not identified properly according to some predefined features to fit users’ interests. Thus, restaurant classification was needed to solve this problem. Restaurant classification has become very important for individuals and food business applications to spread their services via the Internet. In this paper, a modest model is proposed to classify restaurants based on their predefined features which are used as factors affecting restaurant's ratings. The usage of multi label classification is utilized for labelling to maintain acceptable requirements for restaurant's services. Two proposed labels are suggested resulted from the output of two classifiers each operate on a specific set of features. Support vector machine is used for classification because of its effectiveness in restaurant's label separation. The final prediction label is yielded after applying the proposed hypothesis rules. The experimental results conducting Zomato dataset show that the proposed multi label model achieved approximately about 88% for prediction accuracy. Using the proposed model for classification had led to get a collection of accepted restaurants according to user favorites

Label self-advised support vector machine (LSA-SVM)-automated classification of foot drop rehabilitation case study

© 2019 Veterinary World. All rights reserved. Stroke represents a major health problem in our society. One of the effects of stroke is foot drop. Foot drop (FD) is a weakness that occurs in specific muscles in the ankle and foot such as the anterior tibialis, gastrocnemius, plantaris and soleus muscles. Foot flexion and extension are normally generated by lower motor neurons (LMN). The affected muscles impact the ankle and foot in both downward and upward motions. One possible solution for FD is to investigate the movement based on the bio signal (myoelectric signal) of the muscles. Bio signal control systems like electromyography (EMG) are used for rehabilitation devices that include foot drop. One of these systems is function electrical stimulation (FES). This paper proposes new methods and algorithms to develop the performance of myoelectric pattern recognition (M-PR), to improve automated rehabilitation devices, to test these methodologies in offline and real-time experimental datasets. Label classifying is a predictive data mining application with multiple applications in the world, including automatic labeling of resources such as videos, music, images and texts. We combine the label classification method with the self-advised support vector machine (SA-SVM) to create an adapted and altered label classification method, named the label self-advised support vector machine (LSA-SVM). For the experimental data, we collected data from foot drop patients using the sEMG device, in the Metro Rehabilitation Hospital in Sydney, Australia using Ethical Approval (UTS HREC NO. ETH15-0152). The experimental results for the EMG dataset and benchmark datasets exhibit its benefits. Furthermore, the experimental results on UCI datasets indicate that LSA-SVM achieves the best performance when working together with SA-SVM and SVM. This paper describes the state-of-the-art procedures for M-PR and studies all the conceivable structures

Autism Spectrum Disorder Classification via Local and Global Feature Representation of Facial Image

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects social communication and interaction. Early diagnosis of ASD can mitigate the severity and help with ideal treatment direction. Computer vision-based methods with traditional machine learning and deep learning are employed in the literature for automatic diagnosis. Recently, deep learning with a facial image-based ASD classification has gained interest due to its ease of collection and non-invasiveness. We observed that the existing approaches utilized either local or global features of facial images to diagnose ASD. However, its important to consider both local and global features to obtain fine-grained details and larger contextual information for accurate detection and classification. This paper proposes a sequencer-based patch-wise Local Feature Extractor along with a Global Feature Extractor. Finally, the features from these modules are aggregated to obtain the final feature for the classification of ASD. Experiments on a publicly available Autism Facial Image Dataset demonstrate that our proposed framework achieves state-of-the-art performance. We achieved accuracy, precision, recall, and F1-score of 94.7%, 94.0%, 95.3%, and 94.6%, respectively

E2ETCA: End-to-end training of CNN and attention ensembles for rice disease diagnosis

Rice is one of the most important crops worldwide. Diseases of the rice plant can drastically reduce crop yield and even lead to complete loss of production. Early diagnosis can reduce the severity and help efforts to establish effective treatment and reduce the usage of pesticides. Traditional machine learning approaches have already been employed for automatic diagnosis. However, they heavily rely on manual preprocessing of images and handcrafted features, which is challenging, time-consuming, and may require domain expertise. Recently, a single end-to-end deep learning (DL)-based approach was employed to diagnose rice diseases. However, it is not highly robust, nor is it generalizable to every dataset. Hence, we propose a novel end-to-end training of convolutional neural network (CNN) and attention (E2ETCA) ensemble framework that fuses the features of two CNN-based state-of-the-art (SOTA) models along with those of an attention-based vision transformer model. These fused features are utilized for diagnosis by the addition of an extra fully connected layer with softmax. The whole procedure is performed end-to-end, which is very important for real-world applications. Additionally, we feed the extracted features into a traditional machine learning approach support vector machine for classification and further analysis. To verify the effectiveness of our proposed E2ETCA framework, we demonstrate it on three publicly available datasets: the Mendeley Rice Leaf Disease Image Samples dataset, the Kaggle Rice Diseases Image dataset, the Bangladesh Rice Research Institute dataset, and a combination of these three datasets. On the basis of various evaluation metrics (accuracy, precision, recall, and F1-score), our proposed E2ETCA framework exhibits superior performance to existing SOTA approaches for rice disease diagnosis, which can also be generalizable in similar other domains

The Role of Dopant Concentration on Conductivity and Mobility of CdTe Thin Films

Films of CdTe pure and doped with various atomic percentages of Al and Sb (0.5, 1.5 & 2.5) were prepared, and their electrical properties were investigated. The films were prepared by thermal evaporation on glass substrates at two substrate temperatures (Ts=RT & 423 K). The results showed that the conduction phenomena of all the investigated CdTe thin films on glass substrates are caused by two distinct mechanisms. Room temperature DC conductivity increases by a factor of four for undoped CdTe thin films as Ts increases and by 1-2 orders of magnitude with increasing dopant percentage of Al and Sb. In general, films doped with Sb are more efficient than Al-doped films. The activation energy (Ea2) decreases with increasing Ts and dopant percentage for both Al and Sb. Undoped CdTe films deposited at RT are p-type convert to n-type with increasing Ts and upon doping with Al at more than 0.5%. The carrier concentration decreases as Ts increases while it increases with increasing dopant percentage. Hall mobility decreases more than three times as Al increases whereas it increases about one order of magnitude with increasing Sb percentage in CdTe thin films deposited at 423 K and RT, respectively

Environmental disclosure in Spain: Corporate characteristics and media exposure

Social and environmental issues have become a major concern for accounting research over the past two decades. Social and Environmental Accounting has attracted the attention of a number of researchers attempting to understand, explain and predict the disclosure of information on the social and environmental implications of business activities. Empirical research has hypothesized that size, profitability and the potential environmental impact of the firm are the main factors explaining the amount of information disclosed. On the other hand, several studies have focused on the motivations for disclosing environmental information, hypothesizing that disclosures are aimed at building or sustaining corporate legitimacy. We test the main hypotheses developed to date by empirical research with regard to the disclosure of environmental information based on a sample of companies listed on the Madrid Stock Exchange. Results of a content analysis show that firms disclosing environmental information tend to be larger, have higher risk (measured by the beta coefficient) and operate in industries that have a high potential environmental impact. The environmental implications of the activities carried out by these companies also seem to receive more attention from print media. Our results also provide evidence that two factors directly associated with the amount of environmental information disclosed are the potential environmental impact of the industry and the extent of media coverage of the firms

Ketamine-based sedation use in mechanically ventilated critically ill patients with COVID-19: A multicenter cohort study

Backgrounds: Ketamine possesses analgesia, anti-inflammation, anticonvulsant, and neuroprotection properties. However, the evidence that supports its use in mechanically ventilated critically ill patients with COVID-19 is insufficient. The study's goal was to assess ketamine's effectiveness and safety in critically ill, mechanically ventilated (MV) patients with COVID-19. Methods: Adult critically ill patients with COVID-19 were included in a multicenter retrospective-prospective cohort study. Patients admitted between March 1, 2020, and July 31, 2021, to five ICUs in Saudi Arabia were included. Eligible patients who required MV within 24 hours of ICU admission were divided into two sub-cohort groups based on their use of ketamine (Control vs. Ketamine). The primary outcome was the length of stay (LOS) in the hospital. P/F ratio differences, lactic acid normalization, MV duration, and mortality were considered secondary outcomes. Propensity score (PS) matching was used (1:2 ratio) based on the selected criteria. Results: In total, 1,130 patients met the eligibility criteria. Among these, 1036 patients (91.7 %) were in the control group, whereas 94 patients (8.3 %) received ketamine. The total number of patients after PS matching, was 264 patients, including 88 patients (33.3 %) who received ketamine. The ketamine group's LOS was significantly lower (beta coefficient (95 % CI): −0.26 (−0.45, −0.07), P = 0.008). Furthermore, the PaO2/FiO2 ratio significantly improved 24 hours after the start of ketamine treatment compared to the pre-treatment period (6 hours) (124.9 (92.1, 184.5) vs. 106 (73.1, 129.3; P = 0.002). Additionally, the ketamine group had a substantially shorter mean time for lactic acid normalization (beta coefficient (95 % CI): −1.55 (−2.42, −0.69), P 0.01). However, there were no significant differences in the duration of MV or mortality. Conclusions: Ketamine-based sedation was associated with lower hospital LOS and faster lactic acid normalization but no mortality benefits in critically ill patients with COVID-19. Thus, larger prospective studies are recommended to assess the safety and effectiveness of ketamine as a sedative in critically ill adult patients

Investigating Pneumonia Etiology Among Refugees and the Lebanese population (PEARL): A study protocol

Background: Community-acquired pneumonia (CAP), a leading cause of mortality, mainly affects children in developing countries. The harsh circumstances experienced by refugees include various factors associated with respiratory pathogen transmission, and clinical progression of CAP. Consequently, the etiology of CAP in humanitarian crisis situations may differ to that of settled populations, which would impact appropriate case management. Therefore, the Pneumonia Etiology Among Refugees and the Lebanese population (PEARL) study was initiated with the objective of identifying the causal pathogenic microorganisms in the respiratory tract of children and adults from both the refugee and host country population presenting with signs of CAP during a humanitarian crisis. Methods: PEARL, a prospective, multicentric, case-control study, will be conducted at four primary healthcare facilities in Tripoli and the Bekaa valley over 15 months (including two high-transmission seasons/winters). Sociodemographic and medical data, and biological samples will be collected from at least 600 CAP cases and 600 controls. Nasopharyngeal swabs, sputum, urine and blood samples will be analyzed at five clinical pathology laboratories in Lebanon to identify the bacterial and viral etiological agents of CAP. Transcriptomic profiling of host le