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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM54136
Volume:
9 |
11 |
Nov 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
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 rec
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Publication Date
11/24/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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