6,703 research outputs found
A Framework for Smart Distribution of Bio-signal Processing Units in M-Health
This paper introduces the Bio-Signal Processing Unit (BSPU) as a functional component that hosts (part of ) the bio-signal information processing algorithms that are needed for an m-health application. With our approach, the BSPUs can be dynamically assigned to available nodes between the bio-signal source and the application to optimize the use of computation and communication resources. The main contributions of this paper are: (1) it presents the supporting architecture (e.g. components and interfaces) and the mechanism (sequence of interactions) for BSPU distribution; (2) it proposes a coordination mechanism to ensure the correctness of the BSPU distribution; (3) it elaborates the design of smooth transition during BSPU distribution in order to minimize the disturbance to the m-health streaming application
FIT A Fog Computing Device for Speech TeleTreatments
There is an increasing demand for smart fogcomputing gateways as the size of
cloud data is growing. This paper presents a Fog computing interface (FIT) for
processing clinical speech data. FIT builds upon our previous work on EchoWear,
a wearable technology that validated the use of smartwatches for collecting
clinical speech data from patients with Parkinson's disease (PD). The fog
interface is a low-power embedded system that acts as a smart interface between
the smartwatch and the cloud. It collects, stores, and processes the speech
data before sending speech features to secure cloud storage. We developed and
validated a working prototype of FIT that enabled remote processing of clinical
speech data to get speech clinical features such as loudness, short-time
energy, zero-crossing rate, and spectral centroid. We used speech data from six
patients with PD in their homes for validating FIT. Our results showed the
efficacy of FIT as a Fog interface to translate the clinical speech processing
chain (CLIP) from a cloud-based backend to a fog-based smart gateway.Comment: 3 pages, 5 figures, 1 table, 2nd IEEE International Conference on
Smart Computing SMARTCOMP 2016, Missouri, USA, 201
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
Impact of Mobile and Wireless Technology on Healthcare Delivery services
Modern healthcare delivery services embrace the use of leading edge technologies and new
scientific discoveries to enable better cures for diseases and better means to enable early
detection of most life-threatening diseases. The healthcare industry is finding itself in a
state of turbulence and flux. The major innovations lie with the use of information
technologies and particularly, the adoption of mobile and wireless applications in
healthcare delivery [1]. Wireless devices are becoming increasingly popular across the
healthcare field, enabling caregivers to review patient records and test results, enter
diagnosis information during patient visits and consult drug formularies, all without the
need for a wired network connection [2]. A pioneering medical-grade, wireless
infrastructure supports complete mobility throughout the full continuum of healthcare
delivery. It facilitates the accurate collection and the immediate dissemination of patient
information to physicians and other healthcare care professionals at the time of clinical
decision-making, thereby ensuring timely, safe, and effective patient care. This paper
investigates the wireless technologies that can be used for medical applications, and the
effectiveness of such wireless solutions in a healthcare environment. It discusses challenges
encountered; and concludes by providing recommendations on policies and standards for
the use of such technologies within hospitals
An Advanced Home ElderCare Service
With the increase of welfare cost all over the developed world, there is a need to resort to new technologies
that could help reduce this enormous cost and provide some quality eldercare services. This paper presents a
middleware-level solution that integrates monitoring and emergency detection solutions with networking solutions. The proposed system enables efficient integration between a variety of sensors and actuators deployed
at home for emergency detection and provides a framework for creating and managing rescue teams willing
to assist elders in case of emergency situations. A prototype of the proposed system was designed and implemented. Results were obtained from both computer simulations and a real-network testbed. These results show that the proposed system can help overcome some of the current problems and help reduce the enormous cost of eldercare service
Emergency TeleOrthoPaedics m-health system for wireless communication links
For the first time, a complete wireless and mobile emergency TeleOrthoPaedics system with field trials and expert opinion is presented. The system enables doctors in a remote area to obtain a second opinion from doctors in the hospital using secured wireless telecommunication networks. Doctors can exchange securely medical images and video as well as other important data, and thus perform remote consultations, fast and accurately using a user friendly interface, via a reliable and secure telemedicine system of low cost. The quality of the transmitted compressed (JPEG2000) images was measured using different metrics and doctors opinions. The results have shown that all metrics were within acceptable limits. The performance of the system was evaluated successfully under different wireless communication links based on real data
- …