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Mesh Network of eHealth Intelligent Agents for Visually Impaired and Blind People: A Review Study on Arduino and Raspberry Pi Wearable Devices

Mesh Network of eHealth Intelligent Agents for Visually Impaired and Blind People: A Review Study on Arduino and Raspberry Pi Wearable Devices

Dmytro Zubov
Copyright: © 2022 |Pages: 32
ISBN13: 9781799841869|ISBN10: 1799841863|ISBN13 Softcover: 9781799857167|EISBN13: 9781799841876
DOI: 10.4018/978-1-7998-4186-9.ch013
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MLA

Zubov, Dmytro. "Mesh Network of eHealth Intelligent Agents for Visually Impaired and Blind People: A Review Study on Arduino and Raspberry Pi Wearable Devices." Emerging Trends in IoT and Integration with Data Science, Cloud Computing, and Big Data Analytics, edited by Pelin Yildirim Taser, IGI Global, 2022, pp. 240-271. https://doi.org/10.4018/978-1-7998-4186-9.ch013

APA

Zubov, D. (2022). Mesh Network of eHealth Intelligent Agents for Visually Impaired and Blind People: A Review Study on Arduino and Raspberry Pi Wearable Devices. In P. Taser (Ed.), Emerging Trends in IoT and Integration with Data Science, Cloud Computing, and Big Data Analytics (pp. 240-271). IGI Global. https://doi.org/10.4018/978-1-7998-4186-9.ch013

Chicago

Zubov, Dmytro. "Mesh Network of eHealth Intelligent Agents for Visually Impaired and Blind People: A Review Study on Arduino and Raspberry Pi Wearable Devices." In Emerging Trends in IoT and Integration with Data Science, Cloud Computing, and Big Data Analytics, edited by Pelin Yildirim Taser, 240-271. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-4186-9.ch013

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Abstract

Smart assistive devices for blind and visually impaired (B&VI) people are of high interest today since wearable IoT hardware became available for a wide range of users. In the first project, the Raspberry Pi 3 B board measures a distance to the nearest obstacle via ultrasonic sensor HC-SR04 and recognizes human faces by Pi camera, OpenCV library, and Adam Geitgey module. Objects are found by Bluetooth devices of classes 1-3 and iBeacons. Intelligent eHealth agents cooperate with one another in a smart city mesh network via MQTT and BLE protocols. In the second project, B&VIs are supported to play golf. Golf flagsticks have sound marking devices with a buzzer, NodeMcu Lua ESP8266 ESP-12 WiFi board, and WiFi remote control. In the third project, an assistive device supports the orientation of B&VIs by measuring the distance to obstacles via Arduino Uno and HC-SR04. The distance is pronounced through headphones. In the fourth project, the soft-/hardware complex uses Raspberry Pi 3 B and Bytereal iBeacon fingerprinting to uniquely identify the B&VI location at industrial facilities.

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