This document proposes the design of a machine-to-machine communication device for medical applications and disaster management. The device would take health data like pulse, heart rate, or temperature from a person and send it over a local area network to a monitoring device. The monitoring device would analyze the data and check if any thresholds are exceeded, and could warn of health hazards or regulate drunk driving. A video conferencing feature is included to allow direct communication between a patient and healthcare provider. The current work involves connecting sensors like temperature sensors to a Raspberry Pi, sending the data to a laptop over a network, and displaying results. Further work planned includes adding pulse sensors and refining the system for less human interference and faster operation.

1. MACHINE TO MACHINE
COMMUNICATION DEVICE
DEVELOPMENT FOR
MEDICAL APPLICATIONS
AND DISASTER
MANAGEMENT
DIKSHYA SHREE RATH
111EC0154
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
NATIONAL INSTITUTE OF TECHNOLOGY, ROURKELA

2. PROPOSAL
• To design a machine to machine device that will take data from the pulse/heart
rate or temperature of a human being measured and sent to one monitoring
device via LAN as digital data and the samples will be analysed for a threshold
level .
• if it exceeds it then person will not be allowed to drive the vehicle for these
results will be compared with the parameters of a human body after alcohol
consumption. This incorporates medical and disaster applications both.
• The data can be sent to medical professionals at the back end and the person
can be warned of the health hazards as well as incorporating this will regulate
and reduce the number of road accidents that take place due to reckless and
drunk driving .
• A video conferencing mode is added so as to provide a direct communication
between patient and health care professional.

3. WORK TILL NOW
• Extensive study on M2M architecture and applications.
• Installation of support packages for raspberry pi in Simulink.
• Configuring the raspberry pi to the electronics department internet network.
• Running the image inversion algorithm in raspberry pi. Captured the image on my laptop, connected USB
webcam on raspberry pi and sent the image to this camera via Simulink model and LAN.
• Took output from the GPIO (general purpose input output) pin and checked the voltage and glowed a led.
• Gave input to the GPIO pins via a button and tried to check the output of another pin.
• Measured the body temperature (hands and neck) using dht11 sensor and dumped Simulink model on
raspberry pi and logged the signals and drew the graph.
• The video capture mode was configured where the webcam being connected to raspberry pi sent me the
video as well as the temperature information.

6. BACKGROUND OF THE WORK
UNDERTAKEN
• The camera module and the temperature sensing module both work together
and establish a data sending and communication between the two people at end
without much human interference.
• The front end can be a patient and at the back end a security personnel or
health care specialist.
• The security personnel can analyse the body temperature data and detect a
drunk driving case or else the health care specialist can check for health
hazards while the person is explaining his/her problem via the video module.

8. PROCEDURE
• Connect the DHT11 with raspberry pi as shown
• Plug in the Ethernet and camera module in the raspberry pi.
• Make the necessary model in Simulink and set the Model Configuration
parameters like the Name of device, IP address, Sampling time etc.
• Deploy the model on the hardware in external mode.
• The Run button is starts the simulation, the running status and time spent can
be seen.
• The simulation stops after the time set gets expired.

9. HARDWARE SET UP

10. CIRCUIT DIAGRAM

11. RESULTS

15. FURTHER WORK INTENDED
• Infra red sensor module needs to be added to check for the heart
beat/pulse signals of the patient or suspect.
• Refinement for better modules to reduce time complexity.
• Less human interference.
• Replacing Simulink blocks by Python codes.
• Connecting the data logging to google spreadsheet and share it over the
network for better storage and access which can be further analysed.
• Accurate graphs, providing concrete analysis and strong inferences.

16. REFERENCES
• David J. Foran and Laura A. Goodell and Robert L. Trelstad,” A Distributed System for Medical consultation and
Education” Member, IEEE May 1, 2004
• Luca Bedogni, Angelo Trotta, Marco Di Felice, Luciano Bononi , ”Machine-to-Machine Communication over TV
White Spaces for Smart Metering Applications” , Department of Computer Science and Engineering, University of
Bologna
• Gil-Yong Lee,1 Nam-Yeol Yun,1 Sung-Chul Lee,2 and Soo-Hyun Park “A Smart Electronic Tagging System Based on
Context awareness and Machine-to-Machine Interworking“ Accepted 9 August 2013(Research article)
• Kwang-Cheng Chen , Shao-Yu Lien “Machine-to-machine communications: Technologies and challenges” 16th
march 2013
• White Paper on "Machine-to-Machine Communication (M2M)”,TEC

17. THANK YOU!!!