RFID for Health CareTracking and Monitoring

Jack JeanDept. of Computer Science and

EngineeringWright State Universityjack.jean@wright.edu

Outline FastFind Zigbee-Based Body Temperature Sensor Node

Bluetooth-Based Fall Detection Sensor Node

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.

RF Code M200 RFID Reader and TRENDnet Wireless Camera

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

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

Wireless Protocol ChoiceZigbee 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 mARange Low mA Range

Join Time Under 30ms Up to 10sec. 3-5sec.

* : can be much longer with different Bluetooth class.

Temperature Sensor Node

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

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

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.

PCB and a new panic buttonOperating Environment:

Typical input voltage 9.0 Volts.Typical transmission range for the Bluetooth module : 200

feet.

Fall Detection Sensor-Phase 2

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

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 .

Assisted Living

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.

Android Phone Interfacepairing the Android app with the fall detection device.

Received command “F” for fall, dial a number to request help

Received command “P” for panic, calling a family member or call 911