RFID for Health Care Tracking and Monitoring

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Presentation transcript:

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

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.

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.

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 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