A Wireless Body Area Network of Intelligent Motion Sensors
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Transcript of A Wireless Body Area Network of Intelligent Motion Sensors
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A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation
Presenter : Hyotaek Shim
Emil Jovanov, Aleksandar Milenkovic, Chris Otto and Piet C de Groen
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Telemedicine System
Wearable health monitoring systems integrated into a telemedicine system continuous monitoring as a part of a
diagnostic procedure to support early detection of
abnormal conditions and prevention of its serious consequences
during supervised recovery from an acute event or surgical procedure
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Holter monitors
Traditional personal medical monitoring systems only to collect data for off-line
processing Wires may limit the patient’s
activity and level of comfort negatively influence the measured
results
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Continuous monitoring
Important limitation for wider acceptance of the existing systems for continuous monitoring unwieldy wires between sensors and
a processing unit lack of system integration of
individual sensors interference on a wireless
communication channel shared by multiple devices
nonexistent support for massive data collection and knowledge discovery
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Integrated research databases
Records from individual monitoring sessions are rarely integrated into research databases support for data mining and
knowledge discovery relevant to specific conditions and
patient categories
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Wireless Body Area Network
preprocessing & synchronization
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Data flow in an WBAN
Sensor level Personal Server Level Medical Service Level
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Sensor Level (1/2)
ECG(electrocardiogram) sensor for monitoring heart activity
EMB(electromyography) sensor for monitoring muscle activity
EEG(electroencephalography) sensor for monitoring brain electrical activity
A blood pressure sensor A tilt sensor for monitoring trunk
position movement sensors used to estimate
user’s activity A “smart sock” sensor or a sensor
equipped shoe insole • to delineate phases of individual steps
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Sensor Level (2/2)
Minimal weight
Low-power operation to permit prolonged continuous monitoring
Seamless integration into a WBAN standard-based interface protocols
Patient-specific calibration, tuning and customization
continuously collect and process raw information, store them locally, and send them to the personal server
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Bluetooth Disadvantages
transfer raw data from sensors to the monitoring station
limitation for prolonged wearable monitoring too complex power demanding prone to interference
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Zigbee wireless protocol
High level communication protocols using small, low-power digital radios based IEEE 802.15.4 standard for wireless
personal area networks (WPANs) targeted at RF applications that
require a low data rate, long battery life, and secure networking
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Personal server level
Initialization, configuration and synchronization of WBAN nodes
Control and monitor operation of WBAN nodes
Collection of sensor readings from physiological sensors
An audio and graphical user-interface early warnings or guidance
Secure communication with remote healthcare provider servers Internet-enabled PDA 3G cell phone A home personal computer
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Medical Services
An emergency service If the received data are out of range
or indicate an imminent medical condition
The exact location of the patient If the personal server is equipped
with GPS sensor monitoring the activity of the
patient By medical professionals Issue altered guidance based on the
new information
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ActiS : Activity Sensor
ADXL202Accelerometer
ADXL202Accelerometer
ECG SignalConditioning
TI MSP430F1232
CC2420(ZigBee)
Flash
USB Interface
TI MSP430F149
TelosISPM
ECG electrodes
The Telos platform 8MHz MSP430F1611 microcontroller 10KB RAM and 48KB Flash Memory UART(Universal Asynchronous Receiver
Transmitter) ISPM
MSP430F1232 microcontroller 10-bit ADC and UART
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ActiS : Motion Sensor
ActiS sensor as Motion Sensor Vertical Plane Θ =
to detection of gait phases
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ActiS : Signal Processing
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Conclusion
Continuous monitoring in the ambulatory setting early detection of abnormal conditions
• increased level of confidence• improve quality of life
supervised rehabilitation potential knowledge discovery
• through data mining of all gathered information