1. RFID for Health Care Tracking and Monitoring
Jack Jean
Dept. of Computer Science and Engineering
Wright State University

2. Outline FastFind Zigbee-Based Body Temperature Sensor Node
Bluetooth-Based Fall Detection Sensor Node

3. FastFind: RFID Asset/People Tracking
for hospital/nursing home use
track, manage, and optimize the usage of assets
track personnel and patients
monitor events that need special care.

4. RF Code M200 RFID Reader and TRENDnet Wireless Camera

5. RF Code A750 Room Locator
Uses 36 KHz IR to transmit a 3 digit octal ID for room location
Uses a form of bi-phase encoding
Take the first 9 of 15 bits for room ID

6. Healthcare Monitoring Devices
Hospitals/Nursing Homes/Assisted Living Communities/Home Care
Zigbee-Based Body Temperature Sensor Node with IR Capability
Bluetooth-Based Fall Detection Sensor Node with IR Capability

7. Wireless Protocol Choice
Zigbee
Bluetooth
802.11(Wi-Fi)
Data Rates
Up to 250kbits/s
1Mbits/s
11/54Mbits/s
Range
10-100m
10m*
50-100m
Network Type
Ad-hoc, peer to peer, star or mesh
Ad-hoc and very small networks
Point to Hub
Current Consumption
Low mA Range
(Sleep Low uA)
Mid to High mA
Join Time
Under 30ms
Up to 10sec.
3-5sec.
* : can be much longer with different Bluetooth class.

8. Temperature Sensor Node

9. Temperature Sensor:
Microchip TC77 with an SPI interface
Meets ASTM Standard for electronic thermometer
Microcontroller:
Microchip PIC24F16KA102
sleep mode at 500 nA
1.8 to 3.6 V

10. Right-hand side: a failed attempt to further improve the device.

11. Fall Detection Sensor-Phase 1
The sensors were made as a shield mounted on top of the Arduino Uno development board.
The shield has the following components:
Bluesmirf gold Bluetooth module (class 1).
ADXL335 3-axes accelerometer.
36KHZ Infrared Receiver.

12. Fall Detection Sensor-Phase 2
PCB and a new panic button
Operating Environment:
Typical input voltage 9.0 Volts.
Typical transmission range for the Bluetooth module : 200 feet.

15. Fit the board to an enclosure that has a 9-volt battery compartment and a belt clip to easily attach the device on the waist
An ON/OFF switch, and a panic button

16. Accelerometer data analysis
Matlab was used to interface with the fall detection device to analyze the accelerometer values in the x,y,z directions.
Future work will include human subject testing to find the thresholds values for daily life activates, to be able to have a concise algorithm that can differentiate between falls and daily life activities .

17. Assisted Living

18. Independent Living for the Elderly
The device communicates through Bluetooth to an Android phone in case of a fall or if the panic button is pressed. The phone can call 911 directly for medical help, or call a family member.

19. Android Phone Interface
pairing the Android app with the fall detection device.
Received command “P” for panic, calling a family member or call 911
Received command “F” for fall, dial a number to request help