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

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

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

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