FRIES: First Response Interactive Emergency System for the Visually and Hearing Impaired

Wafa Elmannai, Yi Wang, Eltion Aliaj, Ahnaf Chowdhury, Rishamdeep Khehra, Dilara Yildi and Solange Soria

Abstract


It is very challenging for the visually and hearing- impaired people to react properly in case of a fire than the none- visually and hearing impaired. In addition, there is a lack of the current emergency response systems in the market that cater to the needs of visually and hearing-impaired people. Most of the current methods are expensive and unreliable while 90% of visually and hearing-impaired people live in developing countries. Therefore, we proposed a new emergency system called The First Response Interactive Emergency System (FRIES). This system provides all the emergency needs of hearing and visually impaired individuals when they are awake as well as asleep in case of a fire, carbon monoxide, and natural gas leak. It will also notify the emergency personnel and their caregivers. The cost of this system is very affordable. Our system consists of a microcontroller, which will be connected to the LED lights, gas sensors, smoke sensors, fire detector, speaker module, WIFI module, vibrating motors, and LCD display. All sensor data is simultaneous transmitted between the microcontroller and sensors. Our promising results showed that this system can be a complete emergency detection solution to provide safety for the visually and hearing impaired.

Keywords


Fire detection, visual impairment, hearing impairment, emergency response system, sensors & safety.

References


Kong B., Lim K., Kwon J. (2019) Fire Detection Using DCNN for Assisting Visually Impaired People in IoT Service Environment. In: De La Prieta F., Omatu S., Fernández-Caballero A. (eds) Distributed Computing and Artificial Intelligence, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 800. Springer, Cham

Milke, J. A. (2010). History of smoke detection: a profile of how the technology and role of smoke detection has changed. A report formulated for Siemens Industry, Inc, Building Technologies Division, Fire Life Safety Unit, By Department of Fire Protection Engineering, University of Maryland.

http://jerusalemhand.com/2014/06/19/fire-natural-disaster/, “Natural Disasters” Jerusalemhand, an international organization that for the rehabilitated [Online access: 10/28/2019].

K. Muhammad, J. Ahmad, I. Mehmood, S. Rho and S. W. Baik, "Convolutional Neural Networks Based Fire Detection in Surveillance Videos," in IEEE Access, vol. 6, pp. 18174-18183, 2018, doi: 10.1109/ACCESS.2018.2812835.

Elmannai, Wafa M., and Khaled M. Elleithy. "A highly accurate and reliable data fusion framework for guiding the visually impaired." IEEE Access 6 (2018): 33029-33054.

Furuhashi, Michihiko, et al. "Haptic communication robot for urgent notification of hearing-impaired people." 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2016, pp. 429-430.

Ciabattoni, Lucio, et al. "Hear to see-See to hear: a Smart Home System User Interface for visually or hearing-impaired people." 2018 IEEE 8th International Conference on Consumer Electronics-Berlin (ICCE-Berlin). IEEE, 2018.7.

Wright, John Richard. (2014) “Tactile and Visual Smoke Detector System.” U.S. Patent Application No. 14/259,679.

LIFETONE HLAC150 Alarm Clock Manual, https://www.manualslib.com/manual/765288/Lifetone- Hlac150.html?page=2#manual, [online access: 10/1/2019]

Bellman Visit Pager Receiver for the Hard of Hearing, https://www.amplifiedtelephones.co.uk/bellman-visit-pager-receiver-for- the-hard-of-hearing.html, [online access: 10/2/2019]

Aico Ei176RF Smoke Alarm User Manual, https://manualszoom.com/manuals/household-appliance/smoke- alarm/aico/aico-ei176rf-smoke-alarm.html [online access: 10/2/2019]

https://store.arduino.cc/usa/arduino-uno-rev3 [online access: 04/18/2020]

https://www.canakit.com/raspberry-pi-4- 4gb.html?cid=usd&src=raspberrypi [online access: 04/18/2020]

https://www.educba.com/raspberry-pi-vs-arduino/ [online access: 04/17/2020]

Czwajda L., Kosacka-Olejnik M., Kudelska I., Kostrzewski M., Sethanan K., Pitakaso R., 2019, Application of prediction markets phenomenon as decision support instrument in vehicle recycling sector, LogForum, Vol. 15, Issue 2, pp. 265-278. DOI: 10.17270/J.LOG.2019.329

Al-Muqbali, F., Al-Tourshi, N., Al-Kiyumi, K., & Hajmohideen, F. (2020, April). Smart Technologies for Visually Impaired: Assisting and conquering infirmity of blind people using AI Technologies. In 2020 12th Annual Undergraduate Research Conference on Applied Computing (URC) (pp. 1-4). IEEE.

Massaro, A., Maritati, V., Savino, N., Galiano, A., Convertini, D., De Fonte, E., & Di Muro, M. (2018). A Study of a health resources management platform integrating neural networks and DSS telemedicine for homecare assistance. Information, vol. 9, issue 7, 176.

Massaro, A., Maritati, V., Giannone, D., Convertini, D., & Galiano, A. (2019). LSTM DSS automatism and dataset optimization for diabetes prediction. Applied Sciences, vol. 9, issue 17, 3532.


Full Text: PDF

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

IT in Innovation IT in Business IT in Engineering IT in Health IT in Science IT in Design IT in Fashion

IT in Industry @ http://www.it-in-industry.com . ISSN (Online): 2203-1731; ISSN (Print): 2204-0595