Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

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

Towards a Smarter organization for a Self-servicing Society

Traditional social organizations such as those for the management of healthcare are the result of designs that matched well with an operational context considerably different from the one we are experiencing today. The new context reveals all the fragility of our societies. In this paper, a platform is introduced by combining social-oriented communities and complex-event processing concepts: SELFSERV. Its aim is to complement the "old recipes" with smarter forms of social organization based on the self-service paradigm and by exploring culture-specific aspects and technological challenges.Comment: Final version of a paper published in the Proceedings of International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI'16), special track on Emergent Technologies for Ambient Assisted Living (ETAAL

Service oriented centered e-health solution for monitoring and preventing chronic diseases

The modern and continuously changing lifestyles in almost all parts of the world resulted in an increase in the incidence of chronic diseases (CDs). To reduce risks associated with chronic diseases, health professionals are studying various clinical solutions. As a result of recent advances in sensing technology, wireless communications, and distributed communication, the monitoring of patients\u27 health condition and the elaboration of prevention plans are considered the most promising solutions for the treatment of chronic diseases. In this paper, we propose a novel framework for monitoring chronic diseases and tracking their vital signs. The framework relies on the service orientation concepts and standards to integrate various subsystems. Monitoring of subjects\u27 health condition, using various sensors and wireless devices, aims to proactively detect any risk of chronic diseases. The system will allow generating and customizing preventive plans dynamically according to the subject\u27s health profile and context while considering many impelling parameters. As a proof of concept of our monitoring and tracking schemes, we have considered a case study for which we have collected and analyzed preliminary data

Ubiquitous Knowledge-based Framework for Personalized Home Healthcare Systems

[[abstract]]In this paper, a personalized home healthcare system with the ubiquitous knowledge-based framework is presented. This system aims to facilitate the in-person service of healthcare and mobility support. To this end, an efficient rule-based reasoning model and flexible knowledge rules are constructed for providing the necessary physiological support and medication treatment procedures. Among the utilized sensing and control technology, software modules, video camera sensors, communication devices, physiological sensors, and a robotic walking support platform are integrated in our system. The proposed system can offer high flexibility and further meet the demands in the practical healthcare support for different patients and caregivers by updating the knowledge rules in the inference mechanism. The various experimentations can demonstrate the system feasibility and interaction with users under numerous healthcare tasks, intelligent personalized services, remote healthcare and walking support and monitoring.[[conferencetype]]國際[[conferencedate]]20140407~20140411[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Miami, US

A Mobile Healthcare Solution for Ambient Assisted Living Environments

Elderly people need regular healthcare services and, several times, are dependent of physicians’ personal attendance. This dependence raises several issues to elders, such as, the need to travel and mobility support. Ambient Assisted Living (AAL) and Mobile Health (m-Health) services and applications offer good healthcare solutions that can be used both on indoor and in mobility environments. This dissertation presents an ambient assisted living (AAL) solution for mobile environments. It includes elderly biofeedback monitoring using body sensors for data collection offering support for remote monitoring. The used sensors are attached to the human body (such as the electrocardiogram, blood pressure, and temperature). They collect data providing comfort, mobility, and guaranteeing efficiency and data confidentiality. Periodic collection of patients’ data is important to gather more accurate measurements and to avoid common risky situations, like a physical fall may be considered something natural in life span and it is more dangerous for senior people. One fall can out a life in extreme cases or cause fractures, injuries, but when it is early detected through an accelerometer, for example, it can avoid a tragic outcome. The presented proposal monitors elderly people, storing collected data in a personal computer, tablet, or smartphone through Bluetooth. This application allows an analysis of possible health condition warnings based on the input of supporting charts, and real-time bio-signals monitoring and is able to warn users and the caretakers. These mobile devices are also used to collect data, which allow data storage and its possible consultation in the future. The proposed system is evaluated, demonstrated and validated through a prototype and it is ready for use. The watch Texas ez430-Chronos, which is capable to store information for later analysis and the sensors Shimmer who allow the creation of a personalized application that it is capable of measuring biosignals of the patient in real time is described throughout this dissertation

Smart system and mobile interface for healthcare: stress and diabetes

In this thesis, a system with multi-channel measurement capabilities was designed and implemented, associated with the monitoring of stress levels, through a proposed algorithm that correlates heart rate, respiratory rate, and galvanic skin response. Experimental validation tests were carried out, as well as experiments with patients suffering from diabetes. To this end, measurements were made not only of stress-related parameters, but also of parameters such as blood glucose levels and blood pressure levels, seeking to extract correlations between stress and diabetes status. In addition, body temperature was another parameter acquired, in order to assess its importance and relation to stress and diabetes. The proposed multichannel system also features RFID technology for authentication purposes, as well as Wi-Fi access for internet connection and storage of the acquired data in a database structured for that purpose, thus enabling remote access. To allow the assessment of stress levels and diabetes progress, a mobile application was also developed, which also allows the visualisation of the analysed data.In this thesis, a system with multi-channel measurement capabilities was designed and implemented, associated with the monitoring of stress levels, through a proposed algorithm that correlates heart rate, respiratory rate, and galvanic skin response. Experimental validation tests were carried out, as well as experiments with patients suffering from diabetes. To this end, measurements were made not only of stress-related parameters, but also of parameters such as blood glucose levels and blood pressure levels, seeking to extract correlations between stress and diabetes status. In addition, body temperature was another parameter acquired, in order to assess its importance and relation to stress and diabetes. The proposed multichannel system also features RFID technology for authentication purposes, as well as Wi-Fi access for internet connection and storage of the acquired data in a database structured for that purpose, thus enabling remote access. To allow the assessment of stress levels and diabetes progress, a mobile application was also developed, which also allows the visualisation of the analysed data

Smart hospital emergency system via mobile-based requesting services

In recent years, the UK’s emergency call and response has shown elements of great strain as of today. The strain on emergency call systems estimated by a 9 million calls (including both landline and mobile) made in 2014 alone. Coupled with an increasing population and cuts in government funding, this has resulted in lower percentages of emergency response vehicles at hand and longer response times. In this paper, we highlight the main challenges of emergency services and overview of previous solutions. In addition, we propose a new system call Smart Hospital Emergency System (SHES). The main aim of SHES is to save lives through improving communications between patient and emergency services. Utilising the latest of technologies and algorithms within SHES is aiming to increase emergency communication throughput, while reducing emergency call systems issues and making the process of emergency response more efficient. Utilising health data held within a personal smartphone, and internal tracked data (GPU, Accelerometer, Gyroscope etc.), SHES aims to process the mentioned data efficiently, and securely, through automatic communications with emergency services, ultimately reducing communication bottlenecks. Live video-streaming through real-time video communication protocols is also a focus of SHES to improve initial communications between emergency services and patients. A prototype of this system has been developed. The system has been evaluated by a preliminary usability, reliability, and communication performance study

Wireless Medical Sensor Networks: Design Requirements and Enabling Technologies

This article analyzes wireless communication protocols that could be used in healthcare environments (e.g., hospitals and small clinics) to transfer real-time medical information obtained from noninvasive sensors. For this purpose the features of the three currently most widely used protocols—namely, Bluetooth® (IEEE 802.15.1), ZigBee (IEEE 802.15.4), and Wi-Fi (IEEE 802.11)—are evaluated and compared. The important features under consideration include data bandwidth, frequency band, maximum transmission distance, encryption and authentication methods, power consumption, and current applications. In addition, an overview of network requirements with respect to medical sensor features, patient safety and patient data privacy, quality of service, and interoperability between other sensors is briefly presented. Sensor power consumption is also discussed because it is considered one of the main obstacles for wider adoption of wireless networks in medical applications. The outcome of this assessment will be a useful tool in the hands of biomedical engineering researchers. It will provide parameters to select the most effective combination of protocols to implement a specific wireless network of noninvasive medical sensors to monitor patients remotely in the hospital or at home