Handicapped Supported Car by the Voice Commands
This paper presents the design, development, and implementation of a voice-command-controlled vehicle specifically engineered to support individuals with disabilities. The primary objective is to enhance mobility and independence for handicapped users by integrating advanced voice recognition technology with a robust microcontroller-based control system. The vehicle is equipped with an Arduino microcontroller interfaced with a sophisticated voice recognition module capable of interpreting and executing voice commands for essential driving functions, including acceleration, braking, steering, and auxiliary controls. The voice recognition system utilizes a high-precision microphone and noise-cancellation algorithms to ensure accurate command detection in various environmental conditions. The microcontroller processes these commands and controls the car’s actuators, accordingly, providing seamless and intuitive control over the vehicle. The mechanical design features a durable chassis optimized for stability and comfort, with an electric drive system powered by high-torque DC motors for smooth and responsive operation. Comprehensive testing was conducted to evaluate the system’s performance in real-world scenarios. Key metrics such as voice recognition accuracy, response time, and overall control reliability were assessed under different conditions. The results demonstrated that the system could accurately interpret voice commands with high reliability, offering a user-friendly driving experience for individuals with disabilities. The implementation of this voice-command-controlled vehicle represents a significant advancement in assistive technology, providing enhanced mobility and independence for handicapped individuals. The system's scalability and adaptability allow for future enhancements and customization to meet specific user needs. This study underscores the potential of integrating voice recognition technology into automotive applications, paving the way for more inclusive and accessible transportation solutions.
Authors
- Najlaa Nsrulaah Faris (ORCID: https://orcid.org/0000-0002-8677-9910)
- Ali Ihsan Alanssari (ORCID: https://orcid.org/0000-0003-0473-8419)
- Yaswanth Kumar
- B Kanaka Durga (ORCID: https://orcid.org/0000-0003-0172-4927)
- Pradeep Reddy
- Vanam Manasa
Institutions
- Thi Qar University (IQ)
- Al-Nisour University College (IQ)
Publication Details
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1109/ariia63345.2024.11051842
- Primary Topic
- Gaze Tracking and Assistive Technology
- Type
- article
- Field-Weighted Citation Impact
- 0.00