AI Assisted Electric Tricycle for Paraplegic Individuals

Abstract

In today’s rapidly developing world, transportation has become an essential requirement for individuals to travel independently and perform daily activities efficiently. Mobility plays a vital role in education, employment, healthcare, and social interaction. However, paraplegic individuals often face significant challenges in commuting due to limited accessibility, dependence on caregivers, and the high cost of advanced assistive mobility devices. Difficulties such as maneuvering vehicles, reversing, parking, travelling during unfavourable conditions, and maintaining physical stability reduce their independence and confidence. To address these issues, this project introduces an “AI Assisted Electric Tricycle for Paraplegic Individuals”, an intelligent, eco-friendly, and cost-effective mobility solution designed to enhance independent transportation for differently abled users. The proposed tricycle is powered by an electric drive system integrated with rechargeable battery technology, providing smooth and efficient transportation with minimal operational cost. The system incorporates Artificial Intelligence-assisted features for safer and smarter mobility assistance. The tricycle is designed with advanced functionalities such as motorized reverse driving assistance, obstacle awareness support, intelligent control assistance, lighting system, horn, and user safety features to ensure reliable operation under different travelling conditions. The reverse driving mechanism eliminates the need for manual vehicle movement, which is often difficult for paraplegic users. In addition, the vehicle structure is designed to provide comfort, stability, and ease of access for long-distance travel. The proposed vehicle also promotes sustainable transportation by reducing dependence on conventional fossil fuels and minimizing environmental pollution. Compared to conventional mobility aids and modified scooters, the proposed tricycle offers lower maintenance cost, improved user independence, and enhanced accessibility at an affordable price. A working prototype of the AI Assisted Electric Tricycle was developed to demonstrate the practical implementation of the system. The project mainly aims to empower paraplegic individuals by providing a reliable, intelligent, and self-sustaining mobility solution that improves their quality of life while encouraging inclusive and environmentally responsible transportation technology.

Introduction

The text presents the development of an AI-assisted electric tricycle for paraplegic individuals, aimed at improving independent mobility, safety, and affordability for users with lower-limb disabilities. It highlights the limitations of conventional mobility aids, which often require external assistance and lack intelligent control features. With advancements in electric vehicles and AI, the proposed system integrates smart assistance such as obstacle detection, stability monitoring, IoT connectivity, and user alerts to enhance autonomy and safety.

The literature review shows that while previous research has improved electric tricycles through better ergonomics, BLDC motors, batteries, IoT monitoring, and AI-based safety systems, most solutions address only individual aspects rather than combining them into a unified platform. This gap motivates the proposed integrated design.

The methodology describes a fully integrated system combining mechanical, electrical, electronic, and IoT subsystems. The tricycle uses a BLDC motor powered by a lithium-ion battery with a Battery Management System (BMS) and protection via an MCB. Sensors like the ultrasonic sensor and MPU6050 provide obstacle detection and tilt/stability monitoring. The ESP8266 microcontroller acts as the central controller, processing sensor data and enabling communication with the Blynk IoT platform. An AI-assisted mobile application (built using MIT App Inventor) supports user interaction and monitoring.

The system operates by continuously monitoring surroundings and vehicle stability. It provides automatic responses such as slowing or stopping when obstacles are detected or when unsafe tilt conditions occur. Real-time alerts are delivered through buzzers and mobile notifications.

Experimental results show that the prototype performs well under different road and traffic conditions. It achieves a maximum speed of about 41 km/h with stable braking and good seating comfort. The ultrasonic system successfully detects obstacles and reduces speed accordingly, while the MPU6050 ensures safety by immediately stopping the vehicle when excessive tilt is detected.

Conclusion

The development of a personalized electric tricycle for individuals with disabilities enhances inclusive and accessible mobility by combining user-centred design with advanced technologies. Features such as a flat platform, secure locking mechanisms, and ramps ensure easy wheelchair access and safe travel. Systems like obstacle detection, along with components such as LiDAR, ToF sensors, GPS, and lithium-ion batteries, improve performance, control, and navigation. The project can be further enhanced with smart control options like voice, mobile app, and joystick operation, along with health monitoring sensors for emergency alerts. Additional safety features such as automatic braking, tilt detection, and lighting systems, combined with intelligent driving capabilities, improve overall reliability. Power efficiency can be increased through solar support and battery management, while GPS tracking, geo-fencing, and anti-theft systems strengthen navigation and security. User comfort is improved with adjustable seating and protection features, and communication systems enable emergency messaging. Overall, the project promotes independence, safety, and equality, redefining mobility through innovation and accessibility

References

[1] Kumar R & Anand, “ Assistive mobility technologies for differently abled individuals”, International Journal of Rehabilitation Research, vol. 41, no. 2, pp. 95–104,2018 [2] Priya M, Jayanthi, K & Dhivya S ,“Design and development of electric tricycles for rehabilitation applications”, International Journal of Therapy and Rehabilitation, vol. 27, no. 8, pp. 12–25,2020 [3] Sharma A & Pant D ,”Applications of artificial intelligence in assistive transportation”, AI & Society, vol. 36, no. 4, pp. 1125–1139,2021 [4] Deori S & Rajesh R. “A Smart sensor integration in electric assistive vehicles. Journal of Assistive Technologies”, vol. 16, no. 3, pp. 145–158 ,2022 [5] Shamsini R & Iskak W,” Intelligent safety systems for electric mobility devices”, Transportation Research Part C: Emerging Technologies, vol. 149, pp. 104–118,2023 [6] Lee J & Karthik M, “Energy-efficient electric drive systems: Energy management and efficiency optimization in electric tricycles”, Journal of Power Sources, vol. 580, pp. 233–242,2023 [7] Kumar, T. A & Gupta R,” Human–machine interaction in assistive electric vehicles”, IEEE Transactions on Human-Machine Systems, vol. 54, no. 1, pp. 34–45,2024

Copyright

Copyright © 2026 Jayasree D, Adarsh A. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.