Smart Wheel chair using Raspberry pi
Ms. Srilakshmi Nayaka S R, Amrutha V, Balaji M V, Druvan M, Laxmi S N
Ms. Srilakshmi Nayaka S R, ECE & PES Institute of Technology and Management Ms. Amrutha V, ECE & PES Institute of Technology and Management
Mr. Balaji M V, ECE & PES Institute of Technology and Management Mr. Druvan M, ECE & PES Institute of Technology and Management Ms. Laxmi S N, ECE & PES Institute of Technology and Management
Abstract – The integration of assistive technologies in healthcare has greatly enhanced the quality of life for individuals with mobility impairments. This project presents the design and development of a smart wheelchair controlled using a Raspberry Pi. The system incorporates sensors such as an accelerometer, ultrasonic sensors, and a camera module to enable intelligent navigation, obstacle detection, and safety monitoring. User commands can be provided through voice recognition, joystick, or gesture control, ensuring flexible operation. The Raspberry Pi acts as the central processing unit, interfacing with motor drivers to control the wheelchair’s movement. Additionally, IoT connectivity can be implemented for remote monitoring, emergency alerts, and location tracking. The proposed smart wheelchair offers a cost-effective, customizable, and efficient solution that enhances user independence, safety, and accessibility compared to conventional wheelchairs.
Mobility assistance plays a vital role in improving the quality of life for people with physical disabilities. This project proposes the design and development of a smart wheelchair controlled using a Raspberry Pi with gesture recognition and voice commands. Gesture control is implemented using an accelerometer/gyroscope-based sensor (such as ADXL345), which interprets hand movements to direct the wheelchair stop, forward, backward, left, or right. Voice control is enabled through a microphone module and speech recognition system, allowing users to operate the wheelchair with simple spoken commands. The Raspberry Pi acts as the central controller, processing sensor data and controlling the DC motors via a motor driver. For safety, ultrasonic sensors are integrated to detect obstacles and prevent collisions. The combination of gesture and voice-based control provides flexibility, convenience, and independence for users
In the future, the system will be enhanced with machine learning techniques for speech and speaker recognition, enabling more accurate command interpretation, personalized voice profiles, and improved reliability in noisy environments, making the wheelchair smarter and more adaptive to individual users.
