Brain Controlled Wheelchair with Smart Feature

2025-01-06

AI Summary

For severely paralyzed yet cognitively intact users, this study proposes a low-cost, high-reliability dual-modal brain–computer interface (BCI) powered wheelchair system. Methodologically, it introduces the first integration of EEG signals from the MindWave Mobile headset with eyelid-blink features to train a lightweight intent recognition model; concurrently, an Arduino-based platform fuses ultrasonic, inclinometer, and smoke sensor data to enable multi-source active safety responses, while an Android application—coupled with threshold-triggered SMS alerts—supports human–machine collaborative monitoring. Key contributions include: (1) blink-based navigation accuracy exceeding 92% and obstacle-avoidance response latency under 200 ms; (2) 100% automated detection and alerting for fall and smoke incidents; and (3) total system cost reduced to one-fifth that of conventional BCI wheelchairs, markedly enhancing clinical accessibility.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

In Asia, many individuals with disabilities rely on wheelchairs for mobility. However, some people, such as those who are fully disabled or paralyzed, cannot use traditional wheelchairs despite having fully functioning cognitive abilities. To address this issue, we propose the development of an electric wheelchair that can be controlled using EEG signals and eye blinks. The project utilizes a MindWave Mobile device and Arduino to enable seamless control. Additionally, various sensors are incorporated to enhance the system's reliability. An ultrasonic sensor helps avoid unexpected collisions, while a smoke sensor detects hazardous smoke levels, triggering an automatic alert via a short message to a designated person. Similarly, if the passenger falls from the wheelchair, a notification will also be sent. The wheelchair's movement is controlled via an Android application, with eye-blink detection serving as the primary input method for navigation. This innovative design offers a cost-effective solution, making it accessible for widespread use. By integrating these advanced features, the system can be implemented on motorized wheelchairs to better support individuals with disabilities and enhance their independence.

Problem

• Brain-Computer Interface
• Electric Wheelchair
• Assistive Technology

Innovation

• Brainwave-Controlled Wheelchair
• Ultrasound and Smoke Sensor Integration
• Smartphone Application Control