Deployment of assisted living technology using intelligent body sensors platform for elderly people health monitoring

Many of the Ambient Assisted Living Technologies (AALT) available to the end-users as off- shelf products have no common inter-operational protocol (Language). Each product has its own communication protocols, different interfaces and interoperation which limits their solution efficiency for long term health condition monitoring systems. This paper presents assisted living technology (ALT) solution for elderly people based on wireless sensors networking technology. The system includes biofeedback monitoring body sensors, such as: blood pressure, heart rate and body temperature. Each sensor has been integrated with the necessary real time embedded protocol and system to work in ad-hoc bases. The data will be send wirelessly and shared though cloud network. The collected data will be processed and relevant algorithms will be deployed to take certain actions when any changes occur or health warnings arise. These will be treated with high confidentiality to ensure end-users integrity and dignity have been maintained. The proposed solution system will provide the flexibility to analyse most of the health conditions based on near real time monitoring technology. It will also enable the population of elderly to manage their daily life activities within multiple environments i.e. from their comfort home, care centers and hospitals

Deployment of assisted living technology solution platform using smart body sensors for elderly people health monitoring.

Many of the Ambient Assisted Living Technologies (AALT) available in the market to the end-users with long term health condition have no common inter-operational protocol. Each product has its own communication protocols, different interfaces and interoperation which limits their solution reliability, flexibility and efficiency. This paper presents assisted living platform solution for elderly people with long term health condition based on wireless sensors networking technology. The system includes multi feedback sensor arrangements for monitoring, such as: blood pressure, heart rate and body temperature. Each sensor has been integrated with the necessary near real time embedded and wireless protocols that allow data collection, transfer and interoperate in ad-hoc bases. The data will be communicated wirelessly to central data base system and shared though cloud network. The collected data will be processed and relevant intelligent algorithms will be deployed to ensure certain actions taken place when health condition warnings arise. These warnings to be communicated to relevant carer, General Practitioner (GP) and health authority to take the necessary action and steps to handle such end user health condition warnings. The proposed solution system will provide the flexibility to analyse most of the health conditions based on near real time monitoring technology. It will enable the population of elderly with long term health condition to manage their daily life activities within multiple environments i.e. from their comfort home, care centres and hospitals. The data and information will be treated with high confidentiality to ensure end-users integrity and dignity have been maintained.N/

Delivering elder-care environments utilizing TV-channel based mechanisms

In this paper, we present a smart environment for elderly. What makes the development of such system challenging is that the concept of smartness for elderly brings to the extreme the idea of invisibility of the technology. In our experience, elders are well-disposed to new technologies, provided that those will not require significant changes - namely, they are invisible - to their habits. Starting from this consideration, 200 caregivers responses were collected by questionnaire, so as to better understand elders' needs and habits. A system was subsequently developed allowing elders to access a number of "modern web services" as standard TV channels: at channel 43 there is the health status, at channel 45 the photos of the family, at 46 the agenda of the week, just to mention few of the available services. The content of such services is automatically generated by the smart devices in the environment and is managed by the caregivers (e.g., family members) by simple web apps. Fourteen families were asked to install the system in their house. The results of these experiments confirm that the proposed system is considered effective and user-friendly by elders

The OCarePlatform : a context-aware system to support independent living

Background: Currently, healthcare services, such as institutional care facilities, are burdened with an increasing number of elderly people and individuals with chronic illnesses and a decreasing number of competent caregivers. Objectives: To relieve the burden on healthcare services, independent living at home could be facilitated, by offering individuals and their (in)formal caregivers support in their daily care and needs. With the rise of pervasive healthcare, new information technology solutions can assist elderly people ("residents") and their caregivers to allow residents to live independently for as long as possible. Methods: To this end, the OCarePlatform system was designed. This semantic, data-driven and cloud based back-end system facilitates independent living by offering information and knowledge-based services to the resident and his/her (in)formal caregivers. Data and context information are gathered to realize context-aware and personalized services and to support residents in meeting their daily needs. This body of data, originating from heterogeneous data and information sources, is sent to personalized services, where is fused, thus creating an overview of the resident's current situation. Results: The architecture of the OCarePlatform is proposed, which is based on a service-oriented approach, together with its different components and their interactions. The implementation details are presented, together with a running example. A scalability and performance study of the OCarePlatform was performed. The results indicate that the OCarePlatform is able to support a realistic working environment and respond to a trigger in less than 5 seconds. The system is highly dependent on the allocated memory. Conclusion: The data-driven character of the OCarePlatform facilitates easy plug-in of new functionality, enabling the design of personalized, context-aware services. The OCarePlatform leads to better support for elderly people and individuals with chronic illnesses, who live independently. (C) 2016 Elsevier Ireland Ltd. All rights reserved

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

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