Development and Evaluation of a Python Telecare System Based on a Bluetooth Body Area Network

This paper presents a prototype of a telemonitoring system, based on a BAN (Body Area Network) that is integrated by a Bluetooth (BT) pulse oximeter, a GPS (Global Positioning System) unit, and a smartphone. The smartphone is the hardware platformfor running a Python software thatmanages the Bluetooth piconet formed by the sensors. Thus the smartphone forwards the data received from the Bluetooth devices, encoded into JSON (JavaScript Object Notation), to a central server. This server provides universal access to the information of the patient’s location and health status through a web application based on AJAX (Asynchronous JavaScript and XML) technology. Additionally, for the described prototype, the study presents some performance analyses about several topics that are of great interest for the applicability of the prototype: (i) the technique used to forward the patient’s location and health status, (ii) the power consumption of the smartphone (which is compared with the measurements of an equivalent software developed for Java Micro Edition platform), and (iii) the web browser compatibility of the web application developed for the control and monitoring of the patients.Ministerio de Educación y Ciencia TEC2009-13763-C02-0

On the Capability of Smartphones to Perform as Communication Gateways in Medical Wireless Personal Area Networks

This paper evaluates and characterizes the technical performance of medical wireless personal area networks (WPANs) that are based on smartphones. For this purpose, a prototype of a health telemonitoring system is presented. The prototype incorporates a commercial Android smartphone, which acts as a relay point, or “gateway”, between a set of wireless medical sensors and a data server. Additionally, the paper investigates if the conventional capabilities of current commercial smartphones can be affected by their use as gateways or “Holters” in health monitoring applications. Specifically, the profiling has focused on the CPU and power consumption of the mobile devices. These metrics have been measured under several test conditions modifying the smartphone model, the type of sensors connected to the WPAN, the employed Bluetooth profile (SPP (serial port profile) or HDP (health device profile)), the use of other peripherals, such as a GPS receiver, the impact of the use of theWi-Fi interface or the employed method to encode and forward the data that are collected from the sensors.Ministerio de Educación y Ciencia TEC2009-13763-C02-0

WEARABLE MULTI-SENSOR SYSTEM FOR TELEMEDICINE APPLICATIONS

In this paper, we describe a technical design of wearable multi-sensor systems for physiological data measurement and wide medical applications, significantly impacted in telehealth. The monitors are composed of three analog front-end (AFE) devices, which assist with interfacing digital electronics to the noise-, time-sensitive physiological sensors for measuring ECG (heart-rate monitor), RR (respiration-rate monitor), SRL (skin resistivity monitor). These three types of sensors can be used separately or together and allow to determine a number of parameters for the assessment of mental and physical condition. The system is designed based on requirements for demanding environments even outside the realm of medical applications, and in accordance with Health and Safety at Work directives (89/391/CE and Seveso-II 96/82/EC) for occupational hygiene, medical, rehabilitation, sports and fitness applications

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

mHealth Engineering: A Technology Review

In this paper, we review the technological bases of mobile health (mHealth). First, we derive a component-based mHealth architecture prototype from an Institute of Electrical and Electronics Engineers (IEEE)-based multistage research and filter process. Second, we analyze medical databases with regard to these prototypic mhealth system components.. We show the current state of research literature concerning portable devices with standard and additional equipment, data transmission technology, interface, operating systems and software embedment, internal and external memory, and power-supply issues. We also focus on synergy effects by combining different mHealth technologies (e.g., BT-LE combined with RFID link technology). Finally, we also make suggestions for future improvements in mHealth technology (e.g., data-protection issues, energy supply, data processing and storage)

Design of a RESTful middleware to enable a web of medical things

In this paper, we consider the design methodology of a mobile patient hub for the remote self-management of COPD patients. The patient hub design forms a part of the WELCOME system. WELCOME is a current EU project that aims to design and develop a new mobile health system to provide integrated care for COPD patients with comorbidities. The approach adopted for this research is based on the Web of Things architecture with RESTful principles as the enabler of communications. The proposed patient hub architecture design is based on three layers: an application layer, a middleware layer and the sensors layer. This paper presents the detail of the initial design of the middleware and an analysis of the architecture in the context of the system's requirements

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

Towards a self-managed framework for orchestration and integration of devices in AAL

Ambrozas Diana. John B. Thompson (1995). The Media and Modernity : A Social Theory of the Media. In: Communication. Information Médias Théories, volume 18 n°1, décembre 1997. pp. 193-195

Fog Data: Enhancing Telehealth Big Data Through Fog Computing

The size of multi-modal, heterogeneous data collected through various sensors is growing exponentially. It demands intelligent data reduction, data mining and analytics at edge devices. Data compression can reduce the network bandwidth and transmission power consumed by edge devices. This paper proposes, validates and evaluates Fog Data, a service-oriented architecture for Fog computing. The center piece of the proposed architecture is a low power embedded computer that carries out data mining and data analytics on raw data collected from various wearable sensors used for telehealth applications. The embedded computer collects the sensed data as time series, analyzes it, and finds similar patterns present. Patterns are stored, and unique patterns are transmited. Also, the embedded computer extracts clinically relevant information that is sent to the cloud. A working prototype of the proposed architecture was built and used to carry out case studies on telehealth big data applications. Specifically, our case studies used the data from the sensors worn by patients with either speech motor disorders or cardiovascular problems. We implemented and evaluated both generic and application specific data mining techniques to show orders of magnitude data reduction and hence transmission power savings. Quantitative evaluations were conducted for comparing various data mining techniques and standard data compression techniques. The obtained results showed substantial improvement in system efficiency using the Fog Data architecture