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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 05 | May 2025

p-ISSN: 2395-0072

www.irjet.net

ADVANCED NEUROPROSTHETIC WHEELCHAIR WITH EMG CONTROL Dr. K M Mahesh Kumar1, Rakshitha M N2, Rahul G R3, Santhosha K S4, Shivaprasad M5 1Associate Professor Dept. of Electrical and Electronics Engineering, PES COLLEGE OF ENGINEERING- Mandya ,

Karnataka, India

2,3,4,5 U.G. Student, Dept. of Electrical and Electronics Engineering, PES COLLEGE OF ENGINEERING- Mandya ,

Karnataka, India ---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - Designing assistive mobility devices for patients

concentration, making them impractical for widespread reallife application.[3]

with severe locomotor disorders like motor neuron disease or post-polio paralysis demands intelligent systems that minimize or eliminate limb use. While EEG-based systems have been explored, they rely heavily on interpreting noisy brain signals, making real-time control difficult and prone to false positives.

In contrast, Electromyography (EMG) offers a more intuitive and reliable method for mobility control. EMG captures the electrical activity generated by muscle contractions. This technology is especially suitable for individuals who may retain partial muscle control—such as in their forearms, biceps, or facial muscles—even if they cannot perform gross motor tasks. By detecting these muscle signals and processing them through a microcontroller (like Arduino), a smart wheelchair can be controlled using natural, low-effort movements.

In this project, we propose an EMG-controlled smart wheelchair, which leverages electromyography (EMG) signals generated by voluntary muscle contractions to control motion. This approach enables individuals with partial motor function (e.g., ability to flex a specific muscle) to navigate without complex cognitive interfaces.

This project proposes the development of a low-cost EMGcontrolled smart wheelchair system that can effectively interpret muscle activity to control movement. The primary objective is to design a safe, portable, and user-friendly wheelchair interface that does not depend on complex brainwave readings or speech input but instead uses accessible EMG signals. The system includes EMG sensors to capture muscle signals, amplifiers to process them, and an Arduino-based controller to translate them into directional commands for the wheelchair's motors.

By placing EMG sensors on active muscle zones (such as the biceps or forearm), the system captures muscle contractions, filters and amplifies them, and translates them into directional commands using an Arduino microcontroller. The processed signals then drive the wheelchair's motors, allowing for intuitive and responsive control. Compared to EEG systems, EMG-based control offers a more stable, user-friendly, and cost-effective alternative. Key Words: Electromyography, Arduino, HC-50, EEG , BCI

Our approach specifically targets users with upper-limb muscle activity but who lack full limb mobility, offering a balance between affordability, reliability, and ease of use. The system also integrates safety mechanisms to prevent unintended movement and aims to significantly reduce the physical and cognitive burden on users.

1.INTRODUCTION Mobility is a fundamental aspect of human independence. However, individuals suffering from severe motor disabilities—such as those with muscular dystrophy, postpolio paralysis, or partial spinal cord injuries—often face significant challenges in operating traditional electric wheelchairs that require hand-operated joysticks or touch interfaces.[1] While several alternative input systems like EEG-based control, head tracking, speech recognition, and eye-gaze tracking have emerged, each of these has inherent limitations, especially for users with high degrees of paralysis or inconsistent signal accuracy.[2]

1.1 PROBLEM STATEMENT Mobility remains a major challenge for individuals with physical impairments, especially in hospitals and at home. Despite having full cognitive abilities, many disabled individuals struggle with conventional wheelchair controls. Current mobility solutions fail to consider the user's mental capacity as a control source. This project addresses the need for a smarter solution by enabling wheelchair control using brain or muscle signals, allowing users to move independently through thought-driven commands.

Recent innovations in assistive technologies aim to empower individuals with limited motor control to regain independence without relying on third-party assistance. Brain-Computer Interface (BCI) systems using EEG (electroencephalography) have shown promise in translating brainwave patterns into wheelchair commands. However, such systems are often expensive, highly sensitive to noise, require extensive training, and demand continuous

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