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@article{189843,
        author = {Arjun P and Anjo K A and Albin Babu and Arun Mohandas and Dr. Mary P Varghese and Ms. Akhila R},
        title = {Voice controlled wheel chair},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {1818-1823},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189843},
        abstract = {Mobility is a fundamental necessity for individuals with physical disabilities, and as- sistive technologies play a crucial role in enhancing their independence. This project focuses on developing a Voice-Controlled Wheelchair that enables users to navigate using simple voice commands. The system utilizes a Raspberry Pi 4 for voice recognition and command processing, and an Arduino Nano as the main microcontroller to control the movement of the wheelchair through a BTS7960 motor driver. The Raspberry Pi 4 processes the user’s voice commands such as “Forward,” “Reverse,” “Left,” “Right,” and “Stop” using advanced speech recognition algorithms. These interpreted commands are transmitted to the Arduino Nano, which in turn controls the BTS7960 motor driver to drive the 24V wiper motors, ensuring smooth and reliable motion. Experimental testing under various environmental conditions showed high command recognition accuracy (around 96 percentage in quiet environments and 85 percentage in noisy environments), with a quick response time of approximately 0.8 seconds and efficient battery operation lasting about 3.5 hours per charge. The system offers a cost-effective, user-friendly, and practical solution compared to commercially available smart wheelchairs. Future developments include integrating AI-based adaptive control, IoT connectivity for remote monitoring, and enhanced power management systems. This project con- tributes to advancing assistive mobility solutions, aiming to improve the independence and quality of life of individuals with physical disabilities},
        keywords = {Voice-controlled wheelchair, Raspberry Pi 4, Arduino Nano, BTS7960 motor driver, speech recognition, as- sistive technology, embedded systems, mobility aid, automation, rehabilitation engineering.},
        month = {January},
        }