A spherical representation of sensors and a model based approach for classification of human activities

Physical inactivity is a leading risk factor in public health and inactive people are more vulnerable to having non-communicable diseases (NCDs), for example, autoimmune diseases, strokes, most heart diseases, diabetes, chronic kidney disease, and others. In addition, levels of physical activity may be an indicator of health problems in older adult individuals, a particular problem in many societies where there is a growing ratio of old adults age 65 and over. Identifying levels of physical activity may have a significant effect on fitness and reducing healthcare costs in the future. Thus, finding approaches for measuring the individuals’ activities is an important need, in order to provide information about their quality of life and to examine their current health status. This thesis explores the possibility of using low-cost wearable accelerometer based inertial sensors to determine activities during daily living. Two data sources were used for this investigation. The first was a locally collected data set recorded from individuals with Parkinson’s disease in their own homes where they were asked to stand up from their favourite chair and then do different daily activities (Bridge data set). The second was a data set collected in a movement laboratory of the Fredrich-Alexander university and measures 19 participants doing daily activities (sit, stand, washing dishes, sweeping, walking, etc) in controlled conditions (Benchmark data set). Both studies used accelerometer based measurements as these are widely used in wearable and portable technologies such as smartphones, and are now finding use in health care applications. Two areas of research are considered. In the first, accelerometer data were considered in relation to the surface of a sphere of radius 1g (i.e. magnitude of the acceleration due to earth gravitate). This research looked at sensor placement, window size and novel features based on the ‘gravity sphere’. Decision Trees and Na¨ıve Bayes classifiers were used as a baseline classifier on both data sets and k-Nearest Neighbour was used on the Bench Mark data set only. The classification results of a small set of activities of a single individual from first data set show that Na¨ıve Bayes (NB) had a better overall accuracy rate than Decision Trees (DTs), where the results are 85.41% and 78.56% for both NB and DTs respectively. The second area of work considered the possibility of using models of the dynamic system of the human movement as the basis for movement classification. Data from the accelerometers were used to evaluate two approaches that exploited the modelling capacity of a system identification algorithm. The two methods, which are called Prediction Measuring (PM) and Model Matching (MM), used the recursive least square method to identify a model for each class (activity). The Benchmark data set was used to verify the proposed methods. PM method achieved better classification accuracy comparing to MM method, with 71% and 59% respectively

Comparison Between Gross Errors Detection Methods in Surveying Measurements

The least squares estimation method is commonly used to process measurements. In practice, redundant measurements are carried out to ensure quality control and to check for errors that could affect the results. Therefore, an insurance of the quality of these measurements is an important issue. Measurement errors of collected data have different levels of influence due to their number, measured accuracy and redundancy. The aim of this paper is to examine the detection of gross error capabilities in vertical control networks using three methods; Global Test, Data Snooping and Tau Test to compare the effectiveness of these three methods. With the least squares’ method, if there are gross errors in the observations, the sizes of the corresponding residuals may not always be larger than for other residuals that do not have gross errors. This makes it difficult to find (detect) it. Therefore, it is not certain that serious errors should be detected by just examining the magnitudes of the residuals alone. These methods are used in conjunction with developed programs to calculate critical values for the distributions (in real time) rather than look for these in statistical tables. The main conclusion reached is that the tau (τ) statistic is the most sensitive to the presence gross error detection; therefore, it is the one recommended to be used in gross error detection

Graphene growth at low temperatures using RF-plasma enhanced chemical vapour deposition

The advantage of plasma enhanced chemical vapour deposition (PECVD) method is the ability to deposit thin films at relatively low temperature. Plasma power supports the growth process by decomposing hydrocarbon to carbon radicals which will be deposited later on metal catalyst. In this work, we have successfully synthesis graphene on Ni and Co films at relatively low temperature and optimize the synthesis conditions by adjusting the plasma power. Low temperature growth of graphene was optimized at 600°C after comparing the quality of as-grown graphene at several temperatures from 400 to 800°C and by varying plasma powers in the range of 20 - 100 W. Raman analysis of the as-grown samples showed that graphene prefers lower plasma power of 40 W. The annihilation of graphene formation at higher plasma powers is attributed to the presence of high concentration of hydrogen radical from methane which recombines with carbon elements on thin film surface. The optimum graphene growth conditions were obtained at growth temperature of 600°C, plasma power of 40 W and growth time of 10 min with methane flow rate of 120 sccm

Clinical and echocardiographic features of children with rheumatic heart disease and their serum cytokine profile

Acute rheumatic fever (ARF) and rheumatic heart disease (RHD) constitute important public health problems in developing countries. Children with ARF and RHD seen at Children’s Hospital-Sudan from May 2008-2009 were examined clinically and by echocardiography. Blood cytokines (interleukin 10 (IL10), Tumor necrosis factor alpha (TNF- alpha) and interferon gamma (IFN-gamma) were done. Thirty six children were enrolled; 63% had established RHD, and 37% ARF. Mitral regurgitation (MR) was the most common lesion (94%).Ninety five percent of the valve lesions were severe. The serum interleukin IL10 level ranged between 3-6 pg/ml. TNF alpha levels were 9- 100 pg/ml in 12 patients (40%), 101-1000 pg/ml in 10 patients (33%) , more than 1000 in 8 patients (26%). The level of IFN gamma ranged between 2-7 pg/m in all patients except 2 (84 and 135 pg/ml). RHD is manifested with severe valvular lesions and a high TNF alpha indicating and ongoing inflammation.Pan African Medical Journal 2012; 13:3

Interpretable Deep Learning for Discriminating Pneumonia from Lung Ultrasounds

Lung ultrasound images have shown great promise to be an operative point-of-care test for the diagnosis of COVID-19 because of the ease of procedure with negligible individual protection equipment, together with relaxed disinfection. Deep learning (DL) is a robust tool for modeling infection patterns from medical images; however, the existing COVID-19 detection models are complex and thereby are hard to deploy in frequently used mobile platforms in point-of-care testing. Moreover, most of the COVID-19 detection models in the existing literature on DL are implemented as a black box, hence, they are hard to be interpreted or trusted by the healthcare community. This paper presents a novel interpretable DL framework discriminating COVID-19 infection from other cases of pneumonia and normal cases using ultrasound data of patients. In the proposed framework, novel transformer modules are introduced to model the pathological information from ultrasound frames using an improved window-based multi-head self-attention layer. A convolutional patching module is introduced to transform input frames into latent space rather than partitioning input into patches. A weighted pooling module is presented to score the embeddings of the disease representations obtained from the transformer modules to attend to information that is most valuable for the screening decision. Experimental analysis of the public three-class lung ultrasound dataset (PCUS dataset) demonstrates the discriminative power (Accuracy: 93.4%, F1-score: 93.1%, AUC: 97.5%) of the proposed solution overcoming the competing approaches while maintaining low complexity. The proposed model obtained very promising results in comparison with the rival models. More importantly, it gives explainable outputs therefore, it can serve as a candidate tool for empowering the sustainable diagnosis of COVID-19-like diseases in smart healthcare

Treatment-seeking behaviour for malaria in children under five years of age: implication for home management in rural areas with high seasonal transmission in Sudan

BACKGROUND: Effective management of malaria in children under the age of 5 requires mothers to seek, obtain, and use medication appropriately. This is linked to timely decision, accessibility, correct use of the drugs and follow-up. The aim of the study is to identify the basis on which fever was recognized and classified and exploring factors involved in selection of different treatment options. METHODS: Data was obtained by interviewing 96 mothers who had brought their febrile children to selected health facilities, conduction of 10 focus group discussions with mothers at village level as well as by observation. RESULTS: A high score of mothers' knowledge and recognition of fever/malaria was recorded. Mothers usually start care at home and, within an average of three days, they shift to health workers if there was no response. The main health-seeking behaviour is to consult the nearest health facility or health personnel together with using traditional medicine or herbs. There are also health workers who visit patients at home. The majority of mothers with febrile children reported taking drugs before visiting a health facility. The choice between the available options determined by the availability of health facilities, user fees, satisfaction with services, difficulty to reach the facilities and believe in traditional medicine. CONCLUSION: Mothers usually go through different treatment option before consulting health facilities ending with obvious delay in seeking care. As early effective treatment is the main theme of the control programme, implementation of malaria home management strategy is urgently needed to improve the ongoing practice

Exploring optical soliton solutions of the time fractional q-deformed Sinh-Gordon equation using a semi-analytic method

The $\mathsf{q}$-deformed Sinh-Gordon equation extends the classical Sinh-Gordon equation by incorporating a deformation parameter $\mathsf{q}$. It provides a framework for studying nonlinear phenomena and soliton dynamics in the presence of quantum deformations, leading to intriguing mathematical structures and potential applications in diverse areas of physics. In this work, we imply the homotopy analysis method, to obtain approximate solutions for the proposed equation, the error estimated from the obtained solutions reflects the efficiency of the solving method. The solutions were presented in the form of 2D and 3D graphics. The graphics clarify the impact of a set of parameters on the solution, including the deformation parameter $\mathsf{q}$, as well as the effect of time and the fractional order derivative

An efficient algorithm for data parallelism based on stochastic optimization

Deep neural network models can achieve greater performance in numerous machine learning tasks by raising the depth of the model and the amount of training data samples. However, these essential procedures will proportionally raise the cost of training deep neural network models. Accelerating the training process of deep neural network models in a distributed computing environment has become the most often utilized strategy for developers in order to better cope with a huge quantity of training overhead. The current deep neural network model is the stochastic gradient descent (SGD) technique. It is one of the most widely used training techniques in network models, although it is prone to gradient obsolescence during parallelization, which impacts the overall convergence. The majority of present solutions are geared at high-performance nodes with minor performance changes. Few studies have taken into account the cluster environment in high-performance computing (HPC), where the performance of each node varies substantially. A dynamic batch size stochastic gradient descent approach based on performance-aware technology is suggested to address the aforesaid difficulties (DBS-SGD). By assessing the processing capacity of each node, this method dynamically allocates the minibatch of each node, guaranteeing that the update time of each iteration between nodes is essentially the same, lowering the average gradient of the node. The suggested approach may successfully solve the asynchronous update strategy’s gradient outdated problem. The Mnist and cifar10 are two widely used image classification benchmarks, that are employed as training data sets, and the approach is compared with the asynchronous stochastic gradient descent (ASGD) technique. The experimental findings demonstrate that the proposed algorithm has better performance as compared with existing algorithms