International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | IoT-Based EOG Wheelchair with Fall Prevention Using ESP32 |
|---|---|
| ABSTRACT | We report the development of a gaze-driven, IoT-enabled smart wheelchair that addresses the mobility and safety needs of people living with severe motor impairments such as quadriplegia, ALS, spinal cord injury, and locked-in syndrome, by combining eye-movement control with real-time caregiver alerting and onboard fall prevention. The control architecture centres on an ESP32 microcontroller that acquires eye-movement signals through a minimal three-electrode arrangement (right, left, and reference), cleaned and amplified by the X-pill Bio Amplifier before analogue-to-digital conversion. Lateral gaze controls turning, a deliberate single blink initiates forward motion, and a rapid triple-blink sequence commands the wheelchair to halt. Forward obstacle sensing is handled by an HC-SR04 ultrasonic module paired with a local buzzer for immediate audio feedback, while an MPU-6050 six-axis IMU watches for dangerous tilt angles and free-fall signatures that indicate a patient fall. Propulsion is delivered by a pair of 12 V, 60 RPM gear motors controlled through an L298N H-Bridge driver. On detection of either hazard, the ESP32 pushes an instant alert to the caregiver’s smartphone via the Blynk cloud platform over the onboard Wi-Fi link. Bench evaluation on a scaled prototype demonstrated 93.75% command accuracy, complete obstacle and fall detection, and near-perfect alert delivery. Together, these capabilities form a low-cost, minimally invasive assistive device that gives severely paralysed individuals a realistic route to independent movement. |
| AUTHOR | OM ANAP, PRANAV SANAS, PROF. RUPALI A. GIRME Department of Electronics and Telecommunication, PVG's College of Engineering and Technology (PVGCOET), Pune, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406095 |
| pdf/95_IoT-Based EOG Wheelchair with Fall Prevention Using ESP32.pdf | |
| KEYWORDS | |
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