sEMG FEATURE EXTRACTION USING HYBRID TECHNIQUES FOR POWER X.A

This research is about the surface Electromyography (sEMG) feature extraction using hybrid method for Powered Exoskeleton Arm (Power X.A) application. The main objective of this research is to investigate the feature extraction techniques for EMG signal processing. This report is divided into 5 chapters. The first chapter is about the introduction, the second chapter is on the literature review and theory of this research, the third chapter is on the methodology used in this project, the fourth chapter is the discussion of the results and the final chapter is the conclusion and recommendation of this research. EMG is the biomedical signal and widely in used in clinical applications. This research can be divided into 3 parts where the 1st part is on the design on the experimental procedure, the 2nd part is on the signal acquisition and the 3rd part is on the feature extraction based on hybrid techniques. The raw EMG signal was collected from different test subjects and further processed in MATLAB to obtain the clean EMG signal. The most powerful EMG feature extraction which is wavelet techniques and mean absolute value was used for this research. The result shows that Daubechies wavelet order 7 in level 1 and 2 gives the best performance in EMG feature extraction

sEMG FEATURE EXTRACTION USING HYBRID TECHNIQUES FOR POWER X.A

This research is about the surface Electromyography (sEMG) feature extraction using hybrid method for Powered Exoskeleton Arm (Power X.A) application. The main objective of this research is to investigate the feature extraction techniques for EMG signal processing. This report is divided into 5 chapters. The first chapter is about the introduction, the second chapter is on the literature review and theory of this research, the third chapter is on the methodology used in this project, the fourth chapter is the discussion of the results and the final chapter is the conclusion and recommendation of this research. EMG is the biomedical signal and widely in used in clinical applications. This research can be divided into 3 parts where the 1st part is on the design on the experimental procedure, the 2nd part is on the signal acquisition and the 3rd part is on the feature extraction based on hybrid techniques. The raw EMG signal was collected from different test subjects and further processed in MATLAB to obtain the clean EMG signal. The most powerful EMG feature extraction which is wavelet techniques and mean absolute value was used for this research. The result shows that Daubechies wavelet order 7 in level 1 and 2 gives the best performance in EMG feature extraction

Epidemiology and Control of Legionellosis, Singapore

To determine trends and clinical and epidemiologic features of legionellosis in Singapore, we studied cases reported during 2000–2009. During this period, 238 indigenous and 33 imported cases of legionellosis were reported. Cases were reported individually and sporadically throughout each year. Although the annual incidence of indigenous cases had decreased from 0.46 cases per 100,000 population in 2003 to 0.16 cases per 100,000 in 2009, the proportion of imported cases increased correspondingly from 6.2% during 2000–2004 to 27.3% during 2005–2009 (p<0.0005). The prevalence of Legionella bacteria in cooling towers and water fountains was stable (range 12.1%–15.3%) during 2004–August 2008

Non-invasive health prediction from visually observable features [version 2; peer review: 1 approved, 1 approved with reservations]

Background: The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person’s health. Methods: In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set. Results: Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time. Conclusions: The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches

Epidemic Hand, Foot and Mouth Disease Caused by Human Enterovirus 71, Singapore

Singapore experienced a large epidemic of hand, foot and mouth disease (HFMD) in 2000. After reviewing HFMD notifications from doctors and child-care centers, we found that the incidence of HFMD rose in September and declined at the end of October. During this period, 3,790 cases were reported. We performed enteroviral cultures on 311 and 157 specimens from 175 HFMD patients and 107 non-HFMD patients, respectively; human enterovirus 71 (HEV71) was the most frequently isolated virus from both groups. Most of the HFMD patients were <4 years of age. Three HFMD and two non-HFMD patients died. Specimens from two HFMD and both non-HFMD patients were culture positive for HEV71; a third patient was possibly associated with the virus. Autopsies performed on all three HFMD and one of the non-HFMD case-patients showed encephalitis, interstitial pneumonitis, and myocarditis. A preparedness plan for severe HFMD outbreaks provided for the prompt, coordinated actions needed to control the epidemic

Plasmodium knowlesi malaria an emerging public health problem in Hulu Selangor, Selangor, Malaysia (2009–2013) : epidemiologic and entomologic analysis.

Background: While transmission of the human Plasmodium species has declined, a significant increase in Plasmodium knowlesi/Plasmodium malariae cases was reported in Hulu Selangor, Selangor, Malaysia. Thus, a study was undertaken to determine the epidemiology and the vectors involved in the transmission of knowlesi malaria. Methods: Cases of knowlesi/malariae malaria in the Hulu Selangor district were retrospectively reviewed and analyzed from 2009 to 2013. Mosquitoes were collected from areas where cases occurred in order to determine the vectors. Leucosphyrus group of mosquitoes were genetically characterized targeting the nuclear internal transcribed spacer 2 (ITS2) and mitochondrial cytochrome c oxidase subunit I (CO1). In addition, temporal and spatial analyses were carried out for human cases and vectors. Results: Of the 100 microscopy diagnosed P. knowlesi/P. malariae cases over the 5 year period in the Hulu Selangor district, there was predominance of P. knowlesi/P. malariae cases among the young adults (ages 20–39 years; 67 cases; 67%). The majority of the infected people were involved in occupations related to agriculture and forestry (51; 51%). No death was recorded in all these cases. Five hundred and thirty five mosquitoes belonging to 14 species were obtained during the study. Anopheles maculatus was the predominant species (49.5%) followed by Anopheles letifer (13.1%) and Anopheles introlatus (11.6%). Molecular and phylogenetic analysis confirmed the species of the Leucosphyrus group to be An. introlatus. In the present study, only An. introlatus was positive for oocysts. Kernel Density analysis showed that P. knowlesi hotspot areas overlapped with areas where the infected An. introlatus was discovered. This further strengthens the hypothesis that An. introlatusis is the vector for P. knowlesi in the Hulu Selangor district. Unless more information is obtained on the vectors as well as macaque involved in the transmission, it will be difficult to plan effective control strategies. The utilization of modern analytical tools such as GIS (Geographic Information System) is crucial in estimating hotspot areas for targeted control strategies. Conclusions: Anopheles introlatus has been incriminated as vector of P. knowlesi in Hulu Selangor. The cases of P. knowlesi are on the increase and further research using molecular techniques is needed

Validation of the Children’s Eating Behavior Questionnaire in 5 and 6 Year-Old Children: The GUSTO Cohort Study

Revised subscales of the Children’s Eating Behavior Questionnaire (CEBQ) have been proposed to be more appropriate for assessing appetitive traits in Singaporean 3 year-olds, but the CEBQ has not yet been validated in older children in this population. The current study aimed to validate the CEBQ at ages 5 (n = 653) and 6 (n = 449) in the ethnically diverse GUSTO cohort. Confirmatory factor analysis (CFA) examined whether the established eight-factor model of the CEBQ was supported in this sample. Overall, the CFA showed a poor model fit at both ages 5 and 6. At both ages 5 and 6, an exploratory factor analysis revealed a six-factor structure: food fussiness, enjoyment of food, slowness in eating, emotional undereating, emotional overeating and desire to drink. Cronbach’s alpha estimates ranged from 0.70 to 0.85 for all subscales. Criterion validity was tested by correlating subscales with the weight status of 6 years of age. At age 5 and 6, lower scores of slowness of eating while higher scores of enjoyment of food was associated with child overweight. At age 6, higher scores of desire to drink was also associated child overweight. In conclusion, a revised six factor-structure of the CEBQ at ages 5 and 6 were more appropriate for examining appetitive traits in this sample

Search for neutral MSSM Higgs bosons decaying to a pair of tau leptons in pp collisions

Peer reviewe