PROTOTYPING SMART HOME FOR IMMOBILIZED PEOPLE: EEG/MQTT-BASED BRAIN-TO-THING COMMUNICATION
DOI:
https://doi.org/10.15588/1607-3274-2022-2-9Keywords:
brain-to-thing, immobilized people, EEG sensor, MQTTAbstract
Context. Immobilized people face additional barriers in almost all areas of life, including simple operations like turning the light on/off and controlling the air conditioner. The object of the study was to develop the brain-to-thin communication of affordable priceto control the smart home appliances by immobilized people from neck to toes.
Objective. The goal of the work is to manage smart home appliances via brain-to-thing communication with EEG non-invasive electrodes, edge IoT devices, and MQTT protocol if the brain and eye control of the disabled work normally.
Method. A non-invasive Sichiray TGAM brainwave EEG sensor kit captures signals and then transmit them via Bluetooth to the HC-05 module connected to the Arduino Mega microcontroller. Information about edge IoT devices is presented to the disabled on the LCD 1602 display wired to the same Arduino Mega. The disabled person chooses the option shown on display via the double blink that is detected if the quality of signal equals zero and low/mid gamma waves are less than ten in three consecutive Bluetooth packets. Control commands are sent from Arduino Mega (MQTT publisher) to the edge IoT devices (MQTT subscribers) that analyze them and start a specific operation like opening a door and turning the alarm on/off.
Results. Five females and five males of different ages from 8 to 59 years old examined the control of smart home appliances with the Sichiray TGAM brainwave sensor kit. Everyone successfully handled the Sichiray headset and showed satisfaction with the brain-to-thing system.
Conclusions. In this work, a smart home concept for immobilized people was developed using the brain-to-thing approach and the MQTT communication between the MQTT publisher, Sichiray TGAM brainwave EEG sensor kit connected via Bluetooth to the Arduino Mega microcontroller, and edge IoT devices total priced at USD 150. The most likely prospect of the presented work is to produce the sample that is ready to market.
References
World Health Organization, Disability and Health, 2020 [Electronic resource]. Access mode: https://www.who.int/news-room/fact-sheets/detail/disabilityand-health
COVID-19 and disabled people snapshot – March 2021 [Electronic resource]. Access mode: http://www.activityalliance.org.uk/how-wehelp/research/5854-covid19-and-disabled-people-snapshotseptember-2020
Ukraine: Authorities to tighten COVID-19 restrictions in Kyiv and Lviv March 20-April 9 /update 20, 2021 [Electronic resource]. Access mode: https://www.garda.com/crisis24/newsalerts/457186/ukraine-authorities-to-tighten-covid-19-restrictions-in-kyiv-and-lviv-march-20-april-9-update-20
Teles A., Cagy M., Silva F. et al.Using brain-computer interface and Internet of Things to improve healthcare for wheelchair users, Mobile Ubiquitous Computing, Systems, Services, and Technologies: 11th international conference, Barcelona, 12–16 November 2017, proceedings. Wilmington: IARIA XPS Press, 2017, pp. 92–94.
Zhang X. , Yao L. , Zhang S. et al.] Internet of Things meets brain-computer interface: A unified deep learning framework for enabling human-thing cognitive interactivity, IEEE Internet of Things, 2019, Vol. 6, pp. 2084–2092.
Jafri S.R.A., Hamid T., Mahmood R. et al. Wireless brain computer interface for smart home and medical system, Wireless Personal Communications, 2019, Vol. 106, pp. 2163–2177. DOI: 10.1007/s11277-018-5932-x
Lee W. T., Nisar H., Malik A. S. et al. A brain computer interface for smart home control, Consumer Electronics: 17th IEEE international symposium, Hsinchu, 3–6 June 2013, proceedings. Los Alamitos, IEEE, 2013, pp. 35–36. DOI: 10.1109/ISCE.2013.6570240
Qin L. Y., Nasir N. M. , Huq M. S. et al. Smart home control for disabled using brain computer interface, International Journal of Integrated Engineering, 2020, Vol. 12, No. 4, pp. 74–82.
Madoš B., Ádám N., Hurtuk J. et al. Brain-computer interface and Arduino microcontroller family software interconnection solution, Applied Machine Intelligence and Informatics, 14th IEEE International Symposium, Herlany, 21– 23 January 2016, proceedings. NY, Curran Associates, 2016, pp. 217–221. DOI: 10.1109/SAMI.2016.7423010
Sharma V., Sharma A. Review on: Smart home for disabled using brain-computer interfaces, Information Sciences and Computing Technologies, 2015, Vol. 2, No. 2, pp. 142-146.
Kim M., Kim M.-K., Hwang M. et al. Online home appliance control using EEG-based brain-computer interfaces, Electronics, 2019, Vol. 8, No. 10: 1101, 13 p. DOI: 10.3390/electronics8101101
Nanda P., Kamath A. Survey on home automation system using brain computer interface paradigm based on auditory selection attention, International Research Journal of Engineering and Technology, 2019, Vol. 6, pp. 3100–3111.
Belkacem A. N., Jamil N., Palmer J. A. et al. Brain computer interfaces for improving the quality of life of older adults and elderly patients, Frontiers in Neuroscience, 2020, Vol. 14, No. 692, pp. 1–11. DOI: 10.3389/fnins.2020.00692
Gu X., Cao Z., Jolfaei A. et al. EEG-based brain-computer interfaces (BCIs): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021, Vol. 18, No. 5, p. 1645–1666. DOI: 10.1109/TCBB.2021.3052811
Wu Z. P., Zhang W., Zhao J. et al. Optimized complex network method (OCNM) for improving accuracy of measuring human attention in single-electrode neurofeedback system, Computational Intelligence and Neuroscience, 2019, Vol. 2019, No. 2167871. 10 p. DOI: 10.1155/2019/2167871
Irfan G., Zubov D. EEG-based brain-computer interface: connecting Sichiray brainwave sensor kit and Arduino Uno with HC-05 Bluetooth module, Computer Intelligent Systems and Networks, 14th international conference, Kryvyi Rih, 23–25 March 2021, proceedings. Kryvyi Rih, Kryvyi Rih National University, 2021, pp. 21–28.
Dizdarevic J. , Carpio F. , Jukan A. et al. Survey of communication protocols for Internet-of-Things and related challenges of fog and cloud computing integration, ACM Computing Surveys, 2019,Vol. 51, No. 6, Article 116, pp. 1–29. DOI: 10.1145/3292674
Trent M., Abdelgawad A., Yelamarthi K. et al. Gaggi O., Manzoni P., Palazzi C. et al. (Eds.) A smart wearable navigation system for visually impaired, Smart objects and technologies for social good, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Cham, Springer, 2017, Vol. 195, pp. 333–341. DOI: 10.1007/978-3-319-61949-1_35
ScalAgent Distributed Technologies: Benchmark of MQTT servers, ActiveMQ 5.10.0, Apollo 1.7, JoramMQ 1.1.3 (based on Joram 5.9.1), Mosquitto 1.3.5, RabbitMQ 3.4.2. ScalAgent Distributed Technologies, Échirolles, 2015 [Electronic resource]. Access mode: http://www.scalagent.com/IMG/pdf/Benchmark_MQTT_servers-v1-1.pdf
Tijms H., Boucherie R., Van Dijk N. M. (Eds.). One-step improvement ideas and computational aspects, Markov decision processes in practice. Cham, Springer, 2017, pp. 3–32. DOI: 10.1007/978-3-319-47766-4.
Watkins C. J., Dayan P. Technical Note: Q-Learning, Machine Learning, 1992, Vol. 8, pp. 279–292. DOI: 10.1023/A:1022676722315
Getting Started w/ NodeMCU ESP8266 on Arduino IDE © GPL3+ [Electronic resource]. Access mode: https://create.arduino.cc/projecthub/electropeak/gettingstarted-w-nodemcu-esp8266-on-arduino-ide-28184f
Guide for Relay Module with Arduino [Electronic resource]. Access mode: https://randomnerdtutorials.com/guide-forrelay-module-with-arduino/
Dawaga M.E. Automatic detection of eye blinking using the generalized Ising model: thesis ... master of science. London: University of Western Ontario, 2016, 54 p.
Jia X., Kohn A. Gamma rhythms in the brain, PLOS Biology, 2011, Vol. 9, No. 4, 4 p. DOI: 10.1371/journal.pbio.1001045
Brain Wave Frequencies [Electronic resource]. Access mode: https://nhahealth.com/brainwaves-the-language/
Arduino library for reading Neurosky EEG brainwave data [Electronic resource]. Access mode: https://www.pantechsolutions.net/blog/arduino-library-for-reading-neurosky-eeg-brainwave-data/
Eclipse Mosquitto™: An open-source MQTT broker [Electronic resource]. Access mode: http://mosquitto.org
Brainwave Visualizer (Android&iOS) [Electronic resource]. – Access mode: https://store.neurosky.com/products/brainwave-visualizer-1
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