Download A wireless body area network of intelligent motion sensors

A wireless body area network of intelligent motion
sensors for computer assisted physical
rehabilitation
Emil Jovanov, Aleksandar Milenkovic, Chris Otto and Piet C de Groen
Presenter : Hyotaek Shim
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
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
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
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
Wireless Body Area Network
preprocessing & synchronization
Data flow in an WBAN
Sensor level
Personal Server Level
Medical Service Level
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
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
Bluetooth Disadvantages
 transfer raw data from sensors to the
monitoring station
 limitation for prolonged wearable
monitoring
 too complex
 power demanding
 prone to interference
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
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
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
ActiS : Activity Sensor
ISPM
ADXL202
Accelerometer
ADXL202
Accelerometer
ECG Signal
Conditioning
Telos
TI
MSP430F1232
CC2420
(ZigBee)
TI
MSP430F149
Flash
USB Interface
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
ActiS : Motion Sensor
 ActiS sensor as Motion Sensor
 Vertical Plane Θ =
 to detection of gait phases
ActiS : Signal Processing
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