Pain Level Detection From Facial Image Captured by Smartphone

Accurate symptom of cancer patient in regular basis is highly concern to the medical service provider for clinical decision making such as adjustment of medication. Since patients have limitations to provide self-reported symptoms, we have investigated how mobile phone application can play the vital role to help the patients in this case. We have used facial images captured by smart phone to detect pain level accurately. In this pain detection process, existing algorithms and infrastructure are used for cancer patients to make cost low and user-friendly. The pain management solution is the first mobile-based study as far as we found today. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. Here, angular distance, and support vector machines (SVMs) are used for the classification system. In this study, longitudinal data was collected for six months in Bangladesh. Again, cross-sectional pain images were collected from three different countries: Bangladesh, Nepal and the United States. In this study, we found that personalized model for pain assessment performs better for automatic pain assessment. We also got that the training set should contain varying levels of pain in each group: low, medium and high

The Design and Implementation of Intelligent Labor Contraction Monitoring System based on Wearable Internet of Things

In current clinical practice, pregnant women who have entered 37 weeks cannot correctly judge whether they are in labor based on their subjective feelings. Wrong judgment of labor contraction can lead to adverse pregnancy outcomes and endanger the safety of mothers and babies. It will also increase the healthcare pressure in the hospital and the healthcare efficiency is reduced. Therefore, it is very meaningful to be able to design a system for monitoring labor contraction based on objective data to assist pregnant women who have entered 37 weeks in deciding the suitable time to go to hospital. For the above requirements, this thesis designs and implements an intelligent labor contraction monitoring system based on wearable Internet of Things. The system combines the Internet of Things technology, wearable technology and machine learning technology to collect contraction data through wearable sensing device. It uses the Long Short-Term Memory (LSTM) neural network to classify and identify the collected contraction data and realize real-time processing. It improves the accuracy of model recognition to 93.75%. And the recognition results are fed back to the WeChat applet so that pregnant women can view them in real time. The prototype of the wearable sensing device has been integrated by 3D printing and the proof-of-concept system has been demonstrated. Pregnant women can use this system to detect the contraction status and view the contractions in real time through the WeChat applet results. They can judge whether it is suitable for labor, and this system assists in making decisions about the best time to go to hospital

Personalized data analytics for internet-of-things-based health monitoring

The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months

Questionable care: avoiding ineffective treatment

Overview In some hospitals, far too many people get a treatment they should not get, even when the evidence is clear that it is unnecessary or doesn’t work. Australia urgently needs a system to identify these outlier hospitals and make sure they are not putting patients at risk. To show how such a system could work, this report examines five treatments that should not be used on certain patients. One is treating osteoarthritis of the knee with an arthroscope – putting a tube inside the knee to remove tissue. Another is filling a backbone (vertebrae) with cement to treat fractures. A third is putting patients in a pressurised oxygen chamber when it will not help treat their specific condition. Expert guidance labels most of these five treatments do-not-do, yet in 2010-11 nearly 6000 people – or 16 people a day – received them. These procedures can harm. Some people who had them developed infections or other complications during their hospital visit. Some could have avoided the stress, cost, inconvenience and risk of a hospital stay altogether. Do-not-do treatments happen in all states, cities and rural areas, in public and private hospitals. But the ones we measured only happen in a minority of hospitals, some of which provided do-notdo treatments at 10 or 20 times the average rate. We also examined three procedures that are sometimes appropriate, but should not be offered routinely. Again, a few hospitals have very different treatment patterns from their peers. There are important reasons why clinicians sometimes choose inappropriate treatments. Evidence about treatments can be hard for clinicians to access, evaluate and use. Second, there is little systematic monitoring of where do-not-do treatments happen, leaving clinicians and hospitals in the dark about where problems might exist. Finally, the health system does not manage this problem well. There are rarely major negative consequences for providing ineffective care. In fact, there are incentives that go the other way – hospitals and clinicians get income for giving ineffective care. To fix the problem, the Australian Commission on Safety and Quality in Health Care should publish a list of do-not-do treatments. It should then identify public and private hospitals that provide these treatments more often than usual. There could be a good reason for a do-not-do treatment, but if some hospitals provide them consistently it is a real concern. These outlier hospitals should be asked to improve. If they do not, a clinical review by the state health department should check whether the hospital is providing the right care. If it is not, and if it still fails to improve, there should be consequences for the hospital’s management and funding. The approach in this report can easily be used for many more treatments, using evidence and data that governments already have. Governments should use the approach demonstrated in this report to make sure that far fewer people get the wrong treatment

Pragmatic Evaluation of Health Monitoring & Analysis Models from an Empirical Perspective

Implementing and deploying several linked modules that can conduct real-time analysis and recommendation of patient datasets is necessary for designing health monitoring and analysis models. These databases include, but are not limited to, blood test results, computer tomography (CT) scans, MRI scans, PET scans, and other imaging tests. A combination of signal processing and image processing methods are used to process them. These methods include data collection, pre-processing, feature extraction and selection, classification, and context-specific post-processing. Researchers have put forward a variety of machine learning (ML) and deep learning (DL) techniques to carry out these tasks, which help with the high-accuracy categorization of these datasets. However, the internal operational features and the quantitative and qualitative performance indicators of each of these models differ. These models also demonstrate various functional subtleties, contextual benefits, application-specific constraints, and deployment-specific future research directions. It is difficult for researchers to pinpoint models that perform well for their application-specific use cases because of the vast range of performance. In order to reduce this uncertainty, this paper discusses a review of several Health Monitoring & Analysis Models in terms of their internal operational features & performance measurements. Readers will be able to recognise models that are appropriate for their application-specific use cases based on this discussion. When compared to other models, it was shown that Convolutional Neural Networks (CNNs), Masked Region CNN (MRCNN), Recurrent NN (RNN), Q-Learning, and Reinforcement learning models had greater analytical performance. They are hence suitable for clinical use cases. These models' worse scaling performance is a result of their increased complexity and higher implementation costs. This paper compares evaluated models in terms of accuracy, computational latency, deployment complexity, scalability, and deployment cost metrics to analyse such scenarios. This comparison will help users choose the best models for their performance-specific use cases. In this article, a new Health Monitoring Metric (HMM), which integrates many performance indicators to identify the best-performing models under various real-time patient settings, is reviewed to make the process of model selection even easier for real-time scenarios

Inverted GUI Development for IoT with Applications in E-Health

In the context of Internet of Things (IoT), the research of this dissertation is concerned with the development of applications for end-user devices, i.e. devices through which the end-user directly interacts with systems. The complexity of such applications is partly due to network intricacies, and partly because GUI (Graphical User Interface) development is generally complicated and time consuming. We employ a middleware framework called PalCom to manage the former, and focus our research on the problems of the latter, by expanding the scope of PalCom to also enable GUI development. In particular, the research goal is a more efficient GUI development approach that does not require program code to be written.To enable end-users with little or no programming experience to participate in the GUI development process, we eliminate the need for programming by introducing a new development approach. We view this approach as “inverted” in that the development focus is on presenting functionality from an application model as graphical components in a GUI, rather than on retroactively attaching functionality to manually added graphical components. The inverted GUI development approach is supported in two steps. First, we design a language for describing GUIs, and implement interpreters that communicate with remotely hosted application models and render GUI descriptions as fully functional GUIs. Second, we implement a graphical editor for developing GUIs in order to make the language more accessible.The presented solution is evaluated by its application in a number of research projects in the domain of e-health. From the GUIs developed in those projects, we conclude that the GUI language is practically viable for building full-blown, professional grade GUIs. Furthermore, the presented graphical editor is evaluated by direct comparison to a market leading product in a controlled experiment. From this, we conclude that the editor is accessible to new users, and that it can be more efficient to use than the commercial alternative

“An unsuitable job for a woman? Gender and mental health nursing.”

On the evening of Wednesday 12th May 1920 a demonstration took place in the city of Wakefield, in West Yorkshire. According to the ‘Wakefield Express,’ a large procession with banners flying, and with the city’s band at its head, marched through the streets of Wakefield to the Green Market, where a large crowd had assembled. Two lorries acted as platforms for a series of speakers. Speakers on the platform included union officials, local councillors and representatives from the Discharged Sailors’ and Soldiers’ Association. Mr GW Newsome, secretary of the Wakefield branch of the National Asylum Workers’ Union (NAWU), proposed the following motion, ‘That this mass meeting … deplores the continued employment of female labour in male wards at the West Riding Mental Hospital – work which prior to the war was performed by men.

Charlotte Seymour Yapp

Charlotte Seymour Yapp was born in October 1879, in Ardwick, Manchester, to Moses Yapp, a railway guard, and his wife, Sophia (nee Seymour), a seamstress. She trained as a nurse at Aston Union Poor Law Infirmary at Erdington, near Birmingham, completing her training in 1903. She then gained her certificate in midwifery, did private work and worked as an infant health inspector in Lancashire. An active member of the Poor Law Nursing Association, posts followed in Keighley, Halifax, York, West Hartlepool and Tynemouth before her appointment, in 1914, as Matron of the Lake Hospital, Ashton-under-Lyne. She remained there for the rest of her career until she resigned, through ill health, in 1925. She died in 1934

TELEMEDICINE AND ELECTRONIC HEALTH RECORD IMPLEMENTATION IN RURAL AREA: A LITERATURE REVIEW

The Indonesian Minister of Health 2019 issued regulations regarding the implementation of telemedicine services between health service facilities. Telemedicine is aimed primarily at rural areas. This research aims to look at the quality of telemedicine-based services, which are documented in an electronic health record (EHR) with complete information. This research uses the narrative literature review method:  Garuda journal channels, Google Scholar, IEEE Explorer, ProQuest, PubMed, Science Direct, and Scopus. With the input-output process approach, eight scientific articles were published on countries with telemedicine/telehealth policies in rural areas. The implementation of telemedicine has advantages and disadvantages depending on the things supported and the target users. It must have policies, infrastructure, financial resources, and human resources to use, maintain and develop telemedicine. Telemedicine will help the health service process by increasing the degree of public health in rural areas if it is used on a large scale. The completeness of the EHR seems to be lacking in terms of informed consent. Still, a quality EHR can make it easier for health workers to enforce the history, establish the diagnosis, and provide patient healthcare