6,350 research outputs found
Modelling mobile health systems: an application of augmented MDA for the extended healthcare enterprise
Mobile health systems can extend the enterprise computing system of the healthcare provider by bringing services to the patient any time and anywhere. We propose a model-driven design and development methodology for the development of the m-health components in such extended enterprise computing systems. The methodology applies a model-driven design and development approach augmented with formal validation and verification to address quality and correctness and to support model transformation. Recent work on modelling applications from the healthcare domain is reported. One objective of this work is to explore and elaborate the proposed methodology. At the University of Twente we are developing m-health systems based on Body Area Networks (BANs). One specialization of the generic BAN is the health BAN, which incorporates a set of devices and associated software components to provide some set of health-related services. A patient will have a personalized instance of the health BAN customized to their current set of needs. A health professional interacts with their\ud
patients¿ BANs via a BAN Professional System. The set of deployed BANs are supported by a server. We refer to this distributed system as the BAN System. The BAN system extends the enterprise computing system of the healthcare provider. Development of such systems requires a sound software engineering approach and this is what we explore with the new methodology. The methodology is illustrated with reference to recent modelling activities targeted at real implementations. In the context of the Awareness project BAN implementations will be trialled in a number of clinical settings including epilepsy management and management of chronic pain
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Personalised mobile services supporting the implementation of clinical guidelines
Telemonitoring is emerging as a compelling application of Body Area Networks (BANs). We describe two health BAN systems developed respectively by a European team and an Australian team and discuss some issues encountered relating to formalization of clinical knowledge to support real-time analysis and interpretation of BAN data. Our example application is an evidence-based telemonitoring and teletreatment application for home-based rehabilitation. The application is intended to support implementation of a clinical guideline for cardiac rehabilitation following myocardial infarction. In addition to this the proposal is to establish the patient’s individual baseline risk profile and, by real-time analysis of BAN data, continually re-assess the current risk level in order to give timely personalised feedback. Static and dynamic risk factors are derived from literature. Many sources express evidence probabilistically, suggesting a requirement for reasoning with uncertainty; elsewhere evidence requires qualitative reasoning: both familiar modes of reasoning in KBSs. However even at this knowledge acquisition stage some issues arise concerning how best to apply the clinical evidence. Furthermore, in cases where insufficient clinical evidence is currently available, telemonitoring can yield large collections of clinical data with the potential for data mining in order to furnish more statistically powerful and accurate clinical evidence
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
Machine Learning Enabled Vital Sign Monitoring System
Internet of Things (IoT)- based remote health monitoring systems have an enormous potential of becoming an integral part of the future medical system. In particular, these systems can play life-saving roles for treating or monitoring patients with critical health issues. On the other hand, it can also reduce pressure on the health-care system by reducing unnecessary hospital visits of patients. Any health care monitoring system must be free from erroneous data, which may arise because of instrument failure or communication errors. In this thesis, machine-learning techniques are implemented to detect reliability and accuracy of data obtained by the IoT-based remote health monitoring. A system is a set-up where vital health signs, namely, blood pressure, respiratory rate, and pulse rate, are collected by using Spire Stone and iHealth Sense devices. This data is then sent to the intermediate device and then to the cloud. In this system, it is assumed that the channel for transmission of data (vital signs) from users to cloud server is error-free. Afterward, the information is extracted from the cloud, and two machine learning techniques, i.e., Support Vector Machines and K-Nearest Neighbor are applied to compare their accuracy in distinguishing correct and erroneous data. The thesis undertakes two different approaches of erroneous data detection. In the first approach, an unsupervised classifier called Auto Encoder (AE) is used for labeling data by using the latent features. Then the labeled data from AE is used as ground truth for comparing the accuracy of supervised learning models. In the second approach, the raw data is labeled based on the correlation between various features. The accuracy comparison is performed between strongly correlated features and weakly correlated features. Finally, the accuracy comparison between two approaches is performed to check which method is performing better for detecting erroneous data for the given dataset
HRMobile: A lightweight, local architecture for heart rate measurement
Heart rate and heart rate variability (HRV) are important metrics in the study of numerous physical and psychiatric conditions. Previously, measurement of heart rate was relegated to clinical settings, and was neither convenient nor captured a patient’s typical resting state. In effect, this made gathering heart rate data costly and introduced noise. The current prevalence of mobile phone technology and Internet access has increased the viability of remote health monitoring, thus presenting an opportunity to substantially improve the speed, convenience, and reliability of heart rate readings. Recent attention has focused on different methods for remote, non-contact heart rate measurement. Of these methods, video presents perhaps the best option for optimizing cost and convenience. This thesis introduces a lightweight architecture for estimating heart rate and HRV using a smartphone camera. The system presented here runs locally on a smartphone, requiring only a phone camera and 15s or more of continuous video of a subject’s face. No Internet connection or networking is necessary. Building the system to run locally in this manner means that this software confers benefits such as greater user privacy, offline availability, reliability, cost effectiveness, and speed. However, it also introduces added constraints on computational complexity. With these tradeoffs in mind, the system presented here is implemented within an Android mobile app. The performance of our approach fell short of that of existing state-of-the-art methods in terms of mean absolute error (MAE) of heart rate estimation, achieving MAE during validation that was over greater than other existing approaches. There are a number of factors which may contribute to this performance discrepancy, including limitations in the diversity of the data used with respect to gender, age, skin tone, and heart rate intensity. Further, remote photoplethysmographic (rPPG) signal generated by this architecture contains a large number of noise artifacts which are difficult to consistently remove through signal processing. This noise is the primary reason for the underperformance of this architecture, and could potentially be explained by model and feature engineering decisions which were made to address the risk of overfitting on the limited dataset used in this work
Towards Personalized Healthcare in Cardiac Population: The Development of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a ResNet-Based AF Detector
Cardiovascular diseases (CVDs) are the number one cause of death worldwide.
While there is growing evidence that the atrial fibrillation (AF) has strong
associations with various CVDs, this heart arrhythmia is usually diagnosed
using electrocardiography (ECG) which is a risk-free, non-intrusive, and
cost-efficient tool. Continuously and remotely monitoring the subjects' ECG
information unlocks the potentials of prompt pre-diagnosis and timely
pre-treatment of AF before the development of any life-threatening
conditions/diseases. Ultimately, the CVDs associated mortality could be
reduced. In this manuscript, the design and implementation of a personalized
healthcare system embodying a wearable ECG device, a mobile application, and a
back-end server are presented. This system continuously monitors the users' ECG
information to provide personalized health warnings/feedbacks. The users are
able to communicate with their paired health advisors through this system for
remote diagnoses, interventions, etc. The implemented wearable ECG devices have
been evaluated and showed excellent intra-consistency (CVRMS=5.5%), acceptable
inter-consistency (CVRMS=12.1%), and negligible RR-interval errors (ARE<1.4%).
To boost the battery life of the wearable devices, a lossy compression schema
utilizing the quasi-periodic feature of ECG signals to achieve compression was
proposed. Compared to the recognized schemata, it outperformed the others in
terms of compression efficiency and distortion, and achieved at least 2x of CR
at a certain PRD or RMSE for ECG signals from the MIT-BIH database. To enable
automated AF diagnosis/screening in the proposed system, a ResNet-based AF
detector was developed. For the ECG records from the 2017 PhysioNet CinC
challenge, this AF detector obtained an average testing F1=85.10% and a best
testing F1=87.31%, outperforming the state-of-the-art
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