Design and Development of a
Wireless Remote
Point-of-Care Patient Monitoring
System
Ashwin K. Whitchurch, Member, IEEE, Jose K. Abraham, Senior Member, IEEE and
Vijay K.
Varadan, Member, IEEE
High Density Electronics Center, University of Arkansas, Fayetteville AR 72701
Advisor: Chao-Huang Wei
Student: Syue-Ming Lin
PPT Production rate: 100%
Data: 2011 / 11 / 30
Outline
 Abstract
 Introduction
 DESIGN CONSIDERATIONS
 SYSTEM DESIGN AND DEVELOPMENT
A. Monitored Parameters
B. Easy reconfiguration
C. Accuracy and noise isolation
 Central Monitoring System
 REFERENCES
Abstract(1/2)
 Remote patient monitoring is an alternative to regular home check-ups of patients
with certain special medical conditions or the elderly who are unable to regularly visit
a healthcare facility. This technology reduces the number of home visits which are
now only required when special attention is needed.
 This paper presents the design and development of a remote point-of-care patient
monitoring system which allows the patient to be monitored remotely while remaining
in the comfort of their home.
 The system described here allows wireless data acquisition from eight patient-worn
sensors. The number and type of sensors are configurable according to the subject's
specific condition.
Abstract(2/2)
 The system uses the standard Bluetooth technology for communication with a home
based monitor which in turn relays this data to the remote healthcare facility using the
internet.
 This data can be used for real-time evaluation of the patient's conditions as well as
data logging for later analysis.
 Since this is a configurable system, a few selected sensors are connected to
demonstrate the concept of remote patient monitoring; these include
Electrocardiogram (ECG), Electroencephalogram (EEG), Airflow, respiration, patient
movement and body temperature.
Introduction(1/3)
 Point-of-care (POC) patient monitoring refers to near-patient testing, usually outside
the central hospital or primary care facility. Sometimes, Point-of-care testing is
performed by a hospital employee by regular visits to the patient's home to monitor
vital parameters or the state of a patient in the recovery process of rehabilitation.
 The use of wireless patient monitoring systems is usually limited to use within the
hospital environment or independent monitoring in the home which can raise alerts in
case of an emergency, but are not connected to the healthcare facility.
 Remote patient monitoring (RPM)systems are usually off-line and record data for
extended periods which are then read by the hospital at regular intervals, usually at
least a week.
Introduction(2/3)
 In this work, this functionality is extended for remote POC monitoring where
the patient's vital signs can be monitored in real-time by a remote
healthcare facility while the patient remains at home.
 In addition to adding convenience and comfort to the patient's life, it also
allows a more realistic recording of the patient's health while performing
normal everyday activities.
 The system uses a configurable model for the addition of only the required
sensors for the specific applications .It provides eight data acquisition
channels, each with adjustable gain so that it can be adapted to various
sensors.
Introduction(3/3)
 A standard broadband Ethernet connection is used for remote communication with
the care facility, thus eliminating any need for special hardware or services.
 The system consists of a wearable patient monitoring unit; a home-based internet
connected wireless receiver unit and a central monitoring system at the healthcare
facility which retrieves data through the internet.
 Figure 1 shows the basic data flow between components in the patient monitoring
system.
Figure 1: System data flow schematic
Monitored Parameters
 A few parameters have been selected here which are considered to be vital
parameters for a patient monitoring system.
 The parameters monitored by this system and the sensors used for measuring them
are described in this section.
Electroencephalogram (EEG)
 EEG is the measurement of electrical activity of the brain using surface bio-potential
electrodes attached to the subject's scalp.
 EEG requires an amplifier and signal conditioning module because the signals
obtained from the electrodes are only in the order of micro volts, which is too weak to
be digitized without any noise.
 EEG can be used to detect conditions related to the central nervous system such as
Epilepsy and Parkinson's disease.
Electrocardiogram (ECG)
 ECG is a bio potential recorded as a result of the electrical activity of the heart. The
same module used to record EEG as described in the previous section, but with a
lower gain setting, is used to amplify the ECG signals.

ECG can be used to detect various cardiac abnormalities including some forms of
arrhythmia and cardiac damage.
Airflow and respiration
 Respiratory data is a vital parameter in the patient's health monitoring, especially for
respiratory conditions such as COPD.
 A combination of ECG, strain gauges measuring chest expansion and acceleration
data can be used to estimate respiration rate.
 To demonstrate this, an airflow pressure sensor and a resistive strain gauge sensor
is connected to this system and the results are obtained.
Movement sensing
 Some events such as abnormal patient movement or falls could be caused by
medical conditions, so the monitoring of patient movement is useful in detecting any
such events and taking appropriate action.
 Here, a MEMS tri-axis accelerometer and a MEMS gyroscope are used to detect
patient movement.
Body Temperature
 Symptoms of several abnormal medical conditions begin by a rise in the body
temperature causing a fever.

Hence, a temperature sensor device is integrated into the system to relay any sharp
changes in the subject's body temperature.
Easy reconfiguration
 The hardware and software design of the system has to allow the addition or removal
of various sensors with varying levels of input.
 The scaling, offset and gain aspects of the sensor input channels should be
configurable in software.
 Miniature connectors are provided for connection of external sensors to the unit.
Regulated power for the external sensors is also provided through the same interface.
Accuracy and noise isolation
 Accuracy is an important consideration for the design of any data acquisition system.
In this case, it is decided to have 24-bit maximum precision for the sensor inputs.
 Some sensor inputs such as those for EEG and ECG are very sensitive to noise and
thus need a good noise isolation and filtering system.
 The analog sections of the system need to be completely isolated from the digital
sections in order to reduce the coupling of noise induced by clocks in the digital circuit
into the analog sections.
Central Monitoring System
 The data from the patients can be centrally monitored in a healthcare facility using
the central monitoring system.
 It is a Windows based program which runs on a PC which is connected through the
Internet or any other network to the remote home-based unit.
 This program was written using Microsoft Visual C# for the . net framework. It uses
the TCP/IP protocol for communication with the home-based receiver unit.
RESULTS AND CONCLUSION (1/2)
 An experiment was conducted to evaluate the real-time performance of the system
when attached to a real test subject. Figure 2 shows the setup of the system being
tested on a volunteer.
 EEG is recorded by the cap worn by the subject, which contains all the electrodes in
the proper positions according to the EEG electrode placement system (10-20
system).
 The rest of the sensors mentioned earlier are enclosed in a watch-like enclosure for
ease of use. Figure 3 shows the results of the experiment and the recording done in
real-time using the central monitoring system.
Figure 2: The monitoring system being tested
Figure 3: Screenshot showing real-time data display in the central monitoring system
software
RESULTS AND CONCLUSION(2/2)
 The sensor data presented hasn't been calibrated yet, although it has to be
calibrated the various channels correlated with each other to obtain any
useful data out the system.
 The system described in this paper was successfully built and tested with
various sensors. The results showed a strong correlation with the original
sensor data with minimum transmission error and delay.
 This is intended to be improved in future versions by the use of high speed
communication and/or compression and DSP techniques.
REFERENCES
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[2] T. Bratan, M. Clarke, "Towards the Design of a Generic Systems Architecture for
Remote Patient Monitoring," Proc. 27th Annual IEEE EMBSIntl. Conf, 2005, pp. 106109.
[3] A. K. Whitchurch, B. H. Ashok, R. V. Kumaar, K. Sarukesi, V. K. Varadan, "Wireless
system for long-term EEG monitoring of absence epilepsy," Proc. SPIE Vol. 4937, pp.
343-349, 2002.
[4] Daoming Zhang; Celler, B., "Monitoring physiological signals during running exercise,"
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