Abstract:
This project focuses on the development of an EMG-based muscle-controlled electric vehicle integrating an Arduino Uno, EMG sensors, RF communication, and machine learning techniques. The system captures the user’s muscle activity signals through EMG sensors, which are processed and transmitted wirelessly using RF transmitter and receiver modules. The vehicle, equipped with DC motors controlled through a motor driver, responds to these muscular inputs, enabling intuitive and user-friendly navigation. Machine learning algorithms are applied to enhance EMG signal interpretation, improving the accuracy and responsiveness of vehicle control. This innovative approach combines biomedical signal processing, embedded systems, and robotics to create a smart and efficient electric vehicle control system.
INTRODUCTION
With rapid advancements in embedded systems and biomedical signal processing, human–machine interaction has become an important area of research. Conventional vehicle control methods such as steering wheels, joysticks, or remote controllers may not be suitable for individuals with physical disabilities or limited motor control. This limitation has motivated the development of alternative control techniques that allow users to operate vehicles using biological signals generated by the human body.
Electromyography (EMG) is a technique used to measure electrical signals produced by muscle activity. These signals reflect muscle contractions and can be effectively utilized as control inputs for electronic systems. EMG-based control provides a natural and intuitive interface, enabling users to command machines through simple muscle movements. Due to its reliability and ease of implementation, EMG has gained significant attention in assistive technologies, robotics, and biomedical applications.
In this project, an EMG-based muscle-controlled electric vehicle is developed using an Arduino Uno platform. EMG sensors are used to acquire muscle signals from the user, which are processed and transmitted wirelessly using RF communication modules. At the receiver side, the control signals are decoded and used to drive DC motors through a motor driver circuit. Based on the detected muscle activity, the vehicle can perform basic movements such as forward, backward, left, and right.
To improve the accuracy and responsiveness of the system, machine learning techniques are incorporated for effective interpretation of EMG signals. These algorithms help distinguish between different muscle activation patterns, reducing noise and improving command reliability. The proposed system demonstrates a simple, low-cost, and efficient solution for muscle-based vehicle control.
Overall, this project highlights the integration of biomedical signal processing, embedded systems, wireless communication, and robotics. The developed EMG-controlled electric vehicle offers a promising approach for assistive mobility and future intelligent transportation systems.


OBJECTIVES
1. To develop an EMG-based electric vehicle control system.
2. To use EMG sensors to detect muscle movements.
3. To transmit EMG signals wirelessly using RF modules.
4. To control the vehicle movement using DC motors and a motor driver.
5. To improve control accuracy using machine learning techniques.
6. To design a simple, low-cost, and user-friendly system.