A Priority-based Fair Queuing (PFQ) Model for Wireless Healthcare System

Healthcare is a very active research area, primarily due to the increase in the elderly population that leads to increasing number of emergency situations that require urgent actions. In recent years some of wireless networked medical devices were equipped with different sensors to measure and report on vital signs of patient remotely. The most important sensors are Heart Beat Rate (ECG), Pressure and Glucose sensors. However, the strict requirements and real-time nature of medical applications dictate the extreme importance and need for appropriate Quality of Service (QoS), fast and accurate delivery of a patient’s measurements in reliable e-Health ecosystem. As the elderly age and older adult population is increasing (65 years and above) due to the advancement in medicine and medical care in the last two decades; high QoS and reliable e-health ecosystem has become a major challenge in Healthcare especially for patients who require continuous monitoring and attention. Nevertheless, predictions have indicated that elderly population will be approximately 2 billion in developing countries by 2050 where availability of medical staff shall be unable to cope with this growth and emergency cases that need immediate intervention. On the other side, limitations in communication networks capacity, congestions and the humongous increase of devices, applications and IOT using the available communication networks add extra layer of challenges on E-health ecosystem such as time constraints, quality of measurements and signals reaching healthcare centres. Hence this research has tackled the delay and jitter parameters in E-health M2M wireless communication and succeeded in reducing them in comparison to current available models. The novelty of this research has succeeded in developing a new Priority Queuing model ‘’Priority Based-Fair Queuing’’ (PFQ) where a new priority level and concept of ‘’Patient’s Health Record’’ (PHR) has been developed and integrated with the Priority Parameters (PP) values of each sensor to add a second level of priority. The results and data analysis performed on the PFQ model under different scenarios simulating real M2M E-health environment have revealed that the PFQ has outperformed the results obtained from simulating the widely used current models such as First in First Out (FIFO) and Weight Fair Queuing (WFQ). PFQ model has improved transmission of ECG sensor data by decreasing delay and jitter in emergency cases by 83.32% and 75.88% respectively in comparison to FIFO and 46.65% and 60.13% with respect to WFQ model. Similarly, in pressure sensor the improvements were 82.41% and 71.5% and 68.43% and 73.36% in comparison to FIFO and WFQ respectively. Data transmission were also improved in the Glucose sensor by 80.85% and 64.7% and 92.1% and 83.17% in comparison to FIFO and WFQ respectively. However, non-emergency cases data transmission using PFQ model was negatively impacted and scored higher rates than FIFO and WFQ since PFQ tends to give higher priority to emergency cases. Thus, a derivative from the PFQ model has been developed to create a new version namely “Priority Based-Fair Queuing-Tolerated Delay” (PFQ-TD) to balance the data transmission between emergency and non-emergency cases where tolerated delay in emergency cases has been considered. PFQ-TD has succeeded in balancing fairly this issue and reducing the total average delay and jitter of emergency and non-emergency cases in all sensors and keep them within the acceptable allowable standards. PFQ-TD has improved the overall average delay and jitter in emergency and non-emergency cases among all sensors by 41% and 84% respectively in comparison to PFQ model

Wiki-health: from quantified self to self-understanding

Today, healthcare providers are experiencing explosive growth in data, and medical imaging represents a significant portion of that data. Meanwhile, the pervasive use of mobile phones and the rising adoption of sensing devices, enabling people to collect data independently at any time or place is leading to a torrent of sensor data. The scale and richness of the sensor data currently being collected and analysed is rapidly growing. The key challenges that we will be facing are how to effectively manage and make use of this abundance of easily-generated and diverse health data. This thesis investigates the challenges posed by the explosive growth of available healthcare data and proposes a number of potential solutions to the problem. As a result, a big data service platform, named Wiki-Health, is presented to provide a unified solution for collecting, storing, tagging, retrieving, searching and analysing personal health sensor data. Additionally, it allows users to reuse and remix data, along with analysis results and analysis models, to make health-related knowledge discovery more available to individual users on a massive scale. To tackle the challenge of efficiently managing the high volume and diversity of big data, Wiki-Health introduces a hybrid data storage approach capable of storing structured, semi-structured and unstructured sensor data and sensor metadata separately. A multi-tier cloud storage system—CACSS has been developed and serves as a component for the Wiki-Health platform, allowing it to manage the storage of unstructured data and semi-structured data, such as medical imaging files. CACSS has enabled comprehensive features such as global data de-duplication, performance-awareness and data caching services. The design of such a hybrid approach allows Wiki-Health to potentially handle heterogeneous formats of sensor data. To evaluate the proposed approach, we have developed an ECG-based health monitoring service and a virtual sensing service on top of the Wiki-Health platform. The two services demonstrate the feasibility and potential of using the Wiki-Health framework to enable better utilisation and comprehension of the vast amounts of sensor data available from different sources, and both show significant potential for real-world applications.Open Acces

Czy jest możliwa automatyczna interpretacja EKG za pomocą zdjęcia wykonanego smartfonem?

Introduction. Nowadays there are no tools enabling fast digitalization and supporting interpretation of paper ECG recordings. Popularity and availability of devices like smartphone could be used in clinical practice as tools enhancing the interpretation of ECG recordings. The aim of the study is to determine possibilities of using free mobile application and smartphone in assessment of QRS frequency and electrical axis of the heart as a basis for more advanced analysis. Materials and methods. Fifty recordings of 12-lead ECG at 25 mm/s generated by devices of various producers were qualified for the analysis. Each of the recordings was assessed as diagnostic by two cardiologists, who also measured the frequency of QRS complexes, electrical axis of the heart, duration of QT, amplitude and duration of QRS complexes and T waves. Afterwards, automatic interpretation of ECG recordings was performed with educational mobile application eEKG (available for free at AppStore and Google Play) and iPhone 5s. The pictures of ECG waveforms were taken according to instruction of the application. The results of expert assessment and automatic interpretation were compared. The 10% acceptable margin of error was established for assessment of frequency of QRS complexes by the application. Results. Six hundred ECG waveforms (12 leads in every ECG recording) were analysed for frequency of QRS complexes and 50 ECG recordings were analyzed for electrical axis of the heart. The application qualified as diagnostic 573 (95.5%) attempts of QRS frequency assessment and 26 (52%) attempts of electrical axis of the heart assessment. The assessment was accurate in 82% of attempts for QRS complexes frequency assessment and in 96% of attempts for electrical axis of the heart assessment. Significant correlation was proven between QT duration, T wave amplitude, ratio of amplitudes of QRS complexes and T waves and effectiveness of automatic interpretation of ECG waveform. Conclusions. Effective digitalisation and automatic interpretation of ECG recording in assessment of frequency of QRS complexes and electrical axis of the heart is possible with mobile application and smartphone type device.Wstęp. Obecnie brakuje narzędzi umożliwiających szybką digitalizację i wspierających interpretację papierowych zapisów elektrokardiograficznych (EKG). Powszechność urządzeń typu smartfonu sugeruje ich wykorzystanie w praktyce jako narzędzi wspomagających interpretację zapisów EKG. Celem pracy jest określenie możliwości wykorzystania darmowej aplikacji mobilnej i urządzenia typu smartfonu w ocenie częstotliwości zespołów QRS i osi elektrycznej serca jako podstawy do bardziej zaawansowanych analiz. Materiał i metody. Do analizy zakwalifikowano 50 zapisów 12-odprowadzeniowego EKG wykonanych z przesuwem25 mm/s aparatami różnych producentów. Każdy z zapisów oceniło jako diagnostyczny dwóch kardiologów, którzyponadto ocenili częstotliwość zespołów QRS, oś elektryczną serca, czas QT, amplitudę i czas trwania zespołów QRS i załamków T. Następnie przeprowadzono automatyczną interpretację zapisów EKG za pomocą aplikacji edukacyjnej eEKG (dostępna bez opłat w sklepach AppStore i Google Play) i telefonu iPhone 5s; zdjęcia krzywych EKG wykonano zgodnie z instrukcją aplikacji. Zestawiono wyniki oceny ekspertów i automatycznej interpretacji. Przyjęto 10-procentową granicę dopuszczalnego błędu dla oceny częstotliwości zespołów QRS przez aplikację. Wyniki. Łącznie zinterpretowano 600 krzywych EKG pod kątem częstotliwości zespołów QRS i 50 zapisów EKG podkątem osi elektrycznej serca. Jako diagnostyczne aplikacja zakwalifikowała 573 (95,5%) proby oceny częstotliwościzespołów QRS i 26 (52%) prób oceny osi elektrycznej serca. Odsetek zgodnych rozpoznań wynosił 82% w przypadkuczęstotliwości zespołów QRS i 96% w przypadku osi elektrycznej serca. Wykazano istotną korelację między czasem QT, amplitudą załamków T i stosunkiem amplitud zespołów QRS i załamków T a efektywnością automatycznej interpretacji krzywej EKG. Wnioski. Efektywna digitalizacja i automatyczna interpretacja zapisu EKG w zakresie oceny częstotliwości zespołów QRS i osi elektrycznej serca są możliwe za pomocą aplikacji mobilnej i urządzenia typu smartfon

AEVUM: Personalized Health Monitoring System

Advancement in the field of sensors and other portable technologies have resulted in a bevy of health monitoring devices such as blue-tooth and Wi-Fi enabled weighing scales and wearables which help individuals monitor their personal health. This collected information provides a plethora of data points over intervals of time that a primary care physician can utilize to gain a holistic understanding of an individual’s health and provide a more effective and personalized treatment. A drawback of the existing health monitoring devices is that they are not integrated with the professional medical infrastructure. With the wealth of information collected, it is also not feasible for a physician to look through all the data to obtain relevant information or patterns from multiple health monitoring systems. Therefore, it would be beneficial to have a single platform of hardware devices to monitor and collect data and a software application to securely store the collected information, identify patterns for analysis, and summarize the data for the physician and the patient. The aim of this study was to design and develop an unobtrusive, user friendly system, Aevum, which would integrate technology, adapt itself to changes in consumer behavior and integrate with the existing healthcare infrastructure to help an individual monitor their health in a customized manner. Aevum is a multi-device system consisting of a smart, puck-shaped hardware product, a wristband and a software application available to the patient as well as the physician. In addition to monitoring vitals such as heart rate, blood pressure, body temperature and weight, Aevum can monitor environmental factors that affect an individual’s health and uses personalized metrics such as precise calorie intake and medication management to monitor health. This allows the user to personalize Aevum based on their health condition. Finally, Aevum identifies patterns of anomalies in the collected data and compiles the information which can be accessed by the physician to assist in their treatment

Event-driven Middleware for Body and Ambient Sensor Applications

Continuing development of on-body and ambient sensors has led to a vast increase in sensor-based assistance and monitoring solutions. A growing range of modular sensors, and the necessity of running multiple applications on the sensor information, has led to an equally extensive increase in efforts for system development. In this work, we present an event-driven middleware for on-body and ambient sensor networks allowing multiple applications to define information types of their interest in a publish/subscribe manner. Incoming sensor data is hereby transformed into the required data representation which lifts the burden of adapting the application with respect to the connected sensors off the developer's shoulders. Furthermore, an unsupervised on-the-fly reloading of transformation rules from a remote server allows the system's adaptation to future applications and sensors at run-time as well as reducing the number of connected sensors. Open communication channels distribute sensor information to all interested applications. In addition to that, application-specific event channels are introduced that provide tailor-made information retrieval as well as control over the dissemination of critical information. The system is evaluated based on an Android implementation with transformation rules implemented as OSGi bundles that are retrieved from a remote web server. Evaluation shows a low impact of running the middleware and the transformation rules on a phone and highlights the reduced energy consumption by having fewer sensors serving multiple applications. It also points out the behavior and limits of the open and application-specific event channels with respect to CPU utilization, delivery ratio, and memory usage. In addition to the middleware approach, four (preventive) health care applications are presented. They take advantage of the mediation between sensors and applications and highlight the system's capabilities. By connecting body sensors for monitoring physical and physiological parameters as well as ambient sensors for retrieving information about user presence and interactions with the environment, full-fledged health monitoring examples for monitoring a user throughout the day are presented. Vital parameters are gathered from commercially available biosensors and the mediator device running both the middleware and the application is an off-the-shelf smart phone. For gaining information about a user's physical activity, custom-built body and ambient sensors are presented and deployed

Architecture and Applications of IoT Devices in Socially Relevant Fields

Number of IoT enabled devices are being tried and introduced every year and there is a healthy competition among researched and businesses to capitalize the space created by IoT, as these devices have a great market potential. Depending on the type of task involved and sensitive nature of data that the device handles, various IoT architectures, communication protocols and components are chosen and their performance is evaluated. This paper reviews such IoT enabled devices based on their architecture, communication protocols and functions in few key socially relevant fields like health care, farming, firefighting, women/individual safety/call for help/harm alert, home surveillance and mapping as these fields involve majority of the general public. It can be seen, to one's amazement, that already significant number of devices are being reported on these fields and their performance is promising. This paper also outlines the challenges involved in each of these fields that require solutions to make these devices reliableComment: 1