E-HEALTH SYSTEM DISTRIBUTION

In both Latvia and in many other countries, E-health is an already existing and functioning health care system that has also brought many problems and unclear answers to many issues of public interest. It is therefore urgent and important to look at the key features of this system and the specifics of the operation to determine how the existing situation can be rectified, which is not as brilliant as it was planned in the mass media and other sources. The research objective is to analyze the main steps of the E-health system, identify existing gaps, problems and offer concrete solutions. Research tasks include analyzing the concept of E-health; study the historical development of E-health; to consider the order of issuing the recipe

A Lightweight Security Model using Delta Probabilistic Hashing Technique for Secured Data Transmission in IoT Systems

Secure data transmission is one of the most pressing concerns for resource constrained IoT devices. There is a need for an efficient and lightweight solution for data security in IoT applications.  The proposed study is primarily concerned with the creation of a lightweight data security model for an IoT data transmission-based application for data transmission and storage in smart cities. The fundamental contribution of the proposed security model is the creation of a distinct hashing algorithm based on the specific pattern of the previous sequential change in value. The Delta Probabilistic Hashing (DPH) based architecture of key extraction technique for data security will perform this. Based on the hash key signature approach, this kind of safe data handling procedure provides a random key for data encryption, which will simplify the model and speed up the algorithm's operation. Our approach's main features are enhanced efficiency and throughput, lightweight data security, and adaptive key generation

A Gestão de Projetos aplicada no desenvolvimento de uma API para a Indústria da Saúde

Os projetos tecnológicos na área da saúde são cruciais, não só porque possibilitam um melhor dia-a-dia aos pacientes como também a otimização de custos, por outro lado, os doentes que sofrem de doenças crónicas necessitam de um maior e mais permanente acompanhamento de equipas médicas para monitorização das doenças. Este trabalho visa o desenvolvimento de uma aplicação que permite a recolha de informação dos níveis de glicose dos pacientes com diabetes, processando esses dados de forma a que seja possível obter informação real e projetar a evolução da doença, onde se incluí a possibilidade de enviar notificações para que o paciente efetue medições em falta assim como sugestões para um maior e melhor controlo da doença. O foco deste trabalho é a gestão do projeto permitindo a concretização do mesmo, para tal, recorre-se à metodologia investigação-ação e à guia PMBOK ®, de forma a aplicar as melhores práticas de gestão de projetos assim como uma pequena abordagem aos desafios tecnológicos e terapêuticos que são inerentes à diabetes.Health technology projects are crucial, not only because they enable better day life for patients, but also for cost optimization. On the other hand, patients suffering from chronic diseases need more attention and permanent health care from medical teams helping them to get disease monitoring. This work aims to develop a program that allows the collection of information on the glycemia levels of patients with diabetes, processing this data in order to obtain real information and to predict the evolution of the disease, including the possibility of sending notifications for the patient to make missing measurements as well as suggestions for greater and better control of the disease. The focus of this work is the management of the project allowing the implementation of the project, using the research-action methodology and the PMBOK ® guide, in order to apply the best practices of project management as well as an overview to the technological challenges and therapies that are inherent in diabetes.N/

Successful Strategies for Implementing Health Information Technology in Primary Care Practice

Health information technology (HIT) owner-practitioners who adopt effective strategies for HIT implementation can improve primary facility care delivery and profitability. However, some HIT owner-practitioners have ineffective implementation strategies, so they have not realized the total revenue increases of more than 8%. Grounded in general systems theory, the purpose of this multiple case study was to explore successful strategies primary care practitioners (PCPs) use to implement HIT to improve primary facility care delivery and profitability. The participants included 6 owner-practitioners located in Queens County, NY, who successfully implemented HIT to improve facility care delivery and profitability. Data were collected through face-to-face interviews and a review of relevant practice documents. Data were analyzed using thematic analysis, yielding 3 themes: HIT education and training, costs of transitioning to HIT, and focusing on expected benefits of successful HIT implementation. By providing information on effective HIT strategies, the findings from this study could impact social change because PCPs may rely on faster and more accurate health information data to offer better diagnoses and enhance treatments for patients

Identification of Persons and Several Demographic Features based on Motion Analysis of Various Daily Activities using Wearable Sensors

In recent years, there has been an increasing interest in using the capabilities of wearable sensors, including accelerometers, gyroscopes and magnetometers, to recognize individuals while undertaking a set of normal daily activities. The past few years have seen considerable research exploring person recognition using wearable sensing devices due to its significance in different applications, including security and human-computer interaction applications. This thesis explores the identification of subjects and related multiple biometric demographic attributes based on the motion data of normal daily activities gathered using wearable sensor devices. First, it studies the recognition of 18 subjects based on motion data of 20 daily living activities using six wearable sensors affixed to different body locations. Next, it investigates the task of classifying various biometric demographic features: age, gender, height, and weight based on motion data of various activities gathered using two types of accelerometers and one gyroscope wearable sensors. Initially, different significant parameters that impact the subjects' recognition success rates are investigated. These include studying the performance of the three sensor sources: accelerometer, gyroscope, and magnetometer, and the impact of their combinations. Furthermore, the impact of the number of different sensors mounted at different body positions and the best body position to mount sensors are also studied. Next, the analysis also explored which activities are more suitable for subject recognition, and lastly, the recognition success rates and mutual confusion among individuals. In addition, the impact of several fundamental factors on the classification performance of different demographic features using motion data collected from three sensors is studied. Those factors include the performance evaluation of feature-set extracted from both time and frequency domains, feature selection, individual sensor sources and multiple sources. The key findings are: (I) Features extracted from all three sensor sources provide the highest accuracy of subjects recognition. (2) The recognition accuracy is affected by the body position and the number of sensors. Ankle, chest, and thigh positions outperform other positions in terms of the recognition accuracy of subjects. There is a depreciating association between the subject classification accuracy and the number of sensors used. (3) Sedentary activities such as watching tv, texting on the phone, writing with a pen, and using pc produce higher classification results and distinguish persons efficiently due to the absence of motion noise in the signal. (4) Identifiability is not uniformly distributed across subjects. (5) According to the classification results of considered biometric features, both full and selected features-set derived from all three sources of two accelerometers and a gyroscope sensor provide the highest classification accuracy of all biometric features compared to features derived from individual sensors sources or pairs of sensors together. (6) Under all configurations and for all biometric features classified; the time-domain features examined always outperformed the frequency domain features. Combining the two sets led to no increase in classification accuracy over time-domain alone

Adaptive monitoring system for e-health smart homes

International audienceExisting e-health monitoring systems mainly operate in isolation from the requirements of modern healthcare institutions. They do not include optimized techniques which learn the patient's behavior for predicting future important changes. We propose a new context-aware e-health monitoring system targeted at the elderly and isolated persons living alone. It monitors daily living activities and evaluates dependency based on geriatric scales used by health professionals. Its adaptive framework collects only relevant contextual data for evaluating health status. By monitoring the achievement of daily activities, the system learns the behavior of the monitored person. It is then able to detect risky behavioral changes by using our novel forecasting approach based on the extension of the Grey model GM(1, 1). In order to evaluate our system, we use a Markovian model built for generating long term realistic scenarios. By simulation, we compare the performances of our system to traditional monitoring approaches with various synthetically generated scenarios and profiles. Results show that with minimal sensing and data collection, our system accurately evaluates a person's dependency, predicts its health condition, and detects abnormal situations while preserving system resources