WBSN based safe lifestyle: A case study of heartrate monitoring system

A Heart is the vital organ of the body. According to the “world health statistics, 2017” by WHO, about 460,000 people die due to fatal heart attacks every year. To reduce the death rate due to fatal heart attacks and malfunctioning of the cardiovascular system, this paper proposed a Wireless Body Sensor Network (WBSN) based, portable, easily affordable, miniatured, accurate “Heartrate Monitoring System (HMS)”. HMS can be used to regularly examine the cardiac condition at home or hospital to avoid or early detection of any serious condition. Heartrate Monitoring Algorithm (HMA) was designed to observe the spread heartbeat spectrum and worked at the backend of HMS. A case study was performed for forty healthy young subjects. Each subject data was computed for (sub) ̅-3S_

An m-health application for cerebral stroke detection and monitoring using cloud services

[EN] Over 25 million people suffered from cerebral strokes in a span of 23 years. Many systems are being developed to monitor and improve the life of patients that suffer from different diseases. However, solutions for cerebral strokes are hard to find. Moreover, due to their widespread utilization, smartphones have presented themselves as the most appropriate devices for many e-health systems. In this paper, we propose a cerebral stroke detection solution that employs the cloud to store and analyze data in order to provide statistics to public institutions. Moreover, the prototype of the application is presented. The three most important symptoms of cerebral strokes were considered to develop the tasks that are conducted. Thus, the first task detects smiles, the second task employs voice recognition to determine if a sentence is repeated correctly and, the third task determines if the arms can be raised. Several tests were performed in order to verify the application. Results show its ability to determine whether users have the symptoms of cerebral stroke or not.This work has been partially supported by the pre-doctoral student grant "Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2014)" by the "Ministerio de Educacion, Cultura y Deporte", with reference: FPU14/02953.García-García, L.; Tomás Gironés, J.; Parra-Boronat, L.; Lloret, J. (2019). An m-health application for cerebral stroke detection and monitoring using cloud services. International Journal of Information Management. 45:319-327. https://doi.org/10.1016/j.ijinfomgt.2018.06.004S3193274

Analysis & Numerical Simulation of Indian Food Image Classification Using Convolutional Neural Network

Recognition of Indian food can be assumed to be a fine-grained visual task owing to recognition property of various food classes. It is therefore important to provide an optimized approach to segmentation and classification for different applications based on food recognition. Food computation mainly utilizes a computer science approach which needs food data from various data outlets like real-time images, social flat-forms, food journaling, food datasets etc, for different modalities. In order to consider Indian food images for a number of applications we need a proper analysis of food images with state-of-art-techniques. The appropriate segmentation and classification methods are required to forecast the relevant and upgraded analysis. As accurate segmentation lead to proper recognition and identification, in essence we have considered segmentation of food items from images. Considering the basic convolution neural network (CNN) model, there are edge and shape constraints that influence the outcome of segmentation on the edge side. Approaches that can solve the problem of edges need to be developed; an edge-adaptive As we have solved the problem of food segmentation with CNN, we also have difficulty in classifying food, which has been an important area for various types of applications. Food analysis is the primary component of health-related applications and is needed in our day to day life. It has the proficiency to directly predict the score function from image pixels, input layer to produce the tensor outputs and convolution layer is used for self- learning kernel through back-propagation. In this method, feature extraction and Max-Pooling is considered with multiple layers, and outputs are obtained using softmax functionality. The proposed implementation tests 92.89% accuracy by considering some data from yummly dataset and by own prepared dataset. Consequently, it is seen that some more improvement is needed in food image classification. We therefore consider the segmented feature of EA-CNN and concatenated it with the feature of our custom Inception-V3 to provide an optimized classification. It enhances the capacity of important features for further classification process. In extension we have considered south Indian food classes, with our own collected food image dataset and got 96.27% accuracy. The obtained accuracy for the considered dataset is very well in comparison with our foregoing method and state-of-the-art techniques.

Rethinking the Meaning of Cloud Computing for Healthcare: A Taxonomic Perspective and Future Research Directions

Background: Cloud computing is an innovative paradigm that provides users with on-demand access to a shared pool of configurable computing resources such as servers, storage, and applications. Researchers claim that information technology (IT) services delivered via the cloud computing paradigm (ie, cloud computing services) provide major benefits for health care. However, due to a mismatch between our conceptual understanding of cloud computing for health care and the actual phenomenon in practice, the meaningful use of it for the health care industry cannot always be ensured. Although some studies have tried to conceptualize cloud computing or interpret this phenomenon for health care settings, they have mainly relied on its interpretation in a common context or have been heavily based on a general understanding of traditional health IT artifacts, leading to an insufficient or unspecific conceptual understanding of cloud computing for health care. Objective: We aim to generate insights into the concept of cloud computing for health IT research. We propose a taxonomy that can serve as a fundamental mechanism for organizing knowledge about cloud computing services in health care organizations to gain a deepened, specific understanding of cloud computing in health care. With the taxonomy, we focus on conceptualizing the relevant properties of cloud computing for service delivery to health care organizations and highlighting their specific meanings for health care. Methods: We employed a 2-stage approach in developing a taxonomy of cloud computing services for health care organizations. We conducted a structured literature review and 24 semistructured expert interviews in stage 1, drawing on data from theory and practice. In stage 2, we applied a systematic approach and relied on data from stage 1 to develop and evaluate the taxonomy using 14 iterations. Results: Our taxonomy is composed of 8 dimensions and 28 characteristics that are relevant for cloud computing services in health care organizations. By applying the taxonomy to classify existing cloud computing services identified from the literature and expert interviews, which also serves as a part of the taxonomy, we identified 7 specificities of cloud computing in health care. These specificities challenge what we have learned about cloud computing in general contexts or in traditional health IT from the previous literature. The summarized specificities suggest research opportunities and exemplary research questions for future health IT research on cloud computing. Conclusions: By relying on perspectives from a taxonomy for cloud computing services for health care organizations, this study provides a solid conceptual cornerstone for cloud computing in health care. Moreover, the identified specificities of cloud computing and the related future research opportunities will serve as a valuable roadmap to facilitate more research into cloud computing in health care

The Meaningful Use of Cloud Computing in Healthcare

This dissertation focuses on the meaning of cloud computing for healthcare and its meaningful use in the healthcare industry. If used in a meaningful way, cloud computing is argued to be able to provide major benefits to the healthcare industry. Surprisingly, the benefits promised by using cloud computing often do not hold in practice, and the deployment of cloud computing services in healthcare organizations could lead to countereffects for healthcare. Although existing research studies cover a wide range of domains in healthcare, they often do not explain the way in which cloud computing could support healthcare in a systematic manner. In reply to that insufficiency in the research, this dissertation aims to investigate the phenomenon of cloud computing in healthcare organizations and to answer the following overarching research question: How can cloud computing support healthcare organizations in a meaningful way (i.e., meaningful use)? This dissertation conducted four research studies by employing established explorative research methods. The dissertation begins with a study (study 1) that investigates the basic properties of cloud computing services and their specific meanings for the healthcare industry, and suggests concrete directions for studies related to the meaningful use of cloud computing in healthcare. Study 2 focuses on the identification of industry-specific factors for the adoption of cloud computing services in healthcare, and studies 3 and 4 on an investigation of the way in which cloud computing supports collaborative activities in healthcare, respectively. Both focuses belong to research directions suggested by study 1. By addressing the overarching research question, this dissertation could deepen our understanding of the use of information technology (IT) artefacts that advances information systems theories, not only regarding cloud computing itself but also in terms of more general health IT levels