15-01-2013, 03:32 PM
Design and Application of Mobile Embedded Systems for Home Care Applications
Design and Application of Mobile Embedded Systems.pdf (Size: 1.36 MB / Downloads: 33)
Abstract
— Applied biotelemetry is of growing importance in
today's world. Specificity of biotelemetric data put special
requirements on real biotelemetric system. This article
describes some of conclusions acquired in development of real
biotelemetric system using off the shelf embedded hardware
technology, namely ARM microcontrollers, FRAM memory
and dedicated ZigBee chipsets. Described biotelemetric system
is partitioned into logical parts that communicate using custom
data protocols. Devices participating in biotelemetric system
use ZigBee and Ethernet networks as underlying structure for
data communication.
INTRODUCTION
Described model biotelemetric system is embedded
system of distributed nature aimed at monitoring of patient's
vital functions, among others ECG, heart rate, blood pressure
and oxygen saturation. There is need to track other values as
well - body and ambient temperature, patient's posture, body
weight and patient's location in monitored space. To
accomplish these goals, various kinds of data need to be
acquired and processed.
CONCEPT OF BIOTELEMETRIC SYSTEM
Model biotelemetric system can be partitioned into two
basic parts. Inner part located in patient's home and outer
part located in monitoring centre. Both parts are
subpartitioned into participating elements.
A. Inner Part of Model Biotelemetric System
Inner part of model biotelemetric system is located in
space where patient spends most of his time. Main purpose
of this subsystem is to acquire biotelemetric data and to hand
them over to outer part of model biotelemetric system.
Communication in inner part of model biotelemetric system
is implemented using ZigBee technology. There are three
main hardware elements in inner part of model biotelemetric
system, all marked with symbolic names:
DATA FLOWS IN MODEL BIOTELEMETRIC SYSTEM
Data flows in model biotelemetric system can be
divisioned into several correlating categories. These data
flows are biometric data flow from CERBERUS element,
biometric data flow from SENSORs elements, management
data flow and provisioning data flow. There is one to one
mapping between data transported by different network
technologies.
CONCLUSIONS
In these time, model biotelemetric system is being
implemented into working solution. Nevertheless, there is
space for improvements in both concept and implementation
details of this system. Model biotelemetric system is
currently designed for indoor use by one patient only. More
nearby instances of inner part of model biotelemetric system
managed by single outer part of system are possible, but
there exists one to one mapping between patient and ZigBee
network. Future improvements may include support for
outdoor operation with communication implemented using
3G mobile technology and patient's tracking by GPS system.
With advancements in low-power high-density FPGA
solutions, FPGA programmable system on chip technology
seems to be promising for purpose of this biotelemetric
system.