Journal of Electronics and Informatics
IRO Journals
Volume 8 — Issue 1 — March 2026

AI-Powered Real-Time Diagnosis of Neuromuscular Disorders Using EMG and Gait Analysis

https://doi.org/10.36548/jei.2026.1.004
Pages: 68-80
PDF
Gowtham M.
UG Student, Department of Biomedical Engineering, Kongunadu College of Engineering and Technology, Thottiam, Tamilnadu, India
Anbumuthu P.
UG Student, Department of Biomedical Engineering, Kongunadu College of Engineering and Technology, Thottiam, Tamilnadu, India
Harikrishnan M.
UG Student, Department of Biomedical Engineering, Kongunadu College of Engineering and Technology, Thottiam, Tamilnadu, India
Muthukumar T.
Assistant Professor, Department of Biomedical Engineering, Kongunadu College of Engineering and Technology, Thottiam, Tamilnadu, India
March 2026
Volume 8 — Issue 1
Journal of Electronics and Informatics
PDF

How to Cite

M., Gowtham, Anbumuthu P., Harikrishnan M., and Muthukumar T. 2026. “AI-Powered Real-Time Diagnosis of Neuromuscular Disorders Using EMG and Gait Analysis”. Journal of Electronics and Informatics 8 (1): 68-80. https://doi.org/10.36548/jei.2026.1.004.
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Keywords

— Electromyography (EMG)
— Gait Analysis
— Neuromuscular Disorders
— OpenCV
— WebSocket server
— Streamlit
— Artificial Intelligence (AI)
Published: 23-03-2026

Abstract

The proposed project aims to develop an online diagnostic system that can optimally integrate gait analysis and electromyography (EMG) signal processing to diagnose neuromuscular and neurodegenerative diseases in patients. The proposed system can utilize computer vision to detect abnormal gait patterns in patients. The abnormal gait patterns can include abnormal stride patterns, shuffling gait, freezing gait, and balance problems. The proposed system can utilize an EMG sensor to detect muscle activity in patients. The EMG sensor can be connected to an ESP8266 module to send muscle activity data wirelessly to a computer system using a WebSocket server. An online dashboard can be created to visualize muscle activity and gait patterns in patients. The proposed system can be an affordable and mobile AI-based system that can aid in early disease diagnosis in patients and provide better accessibility to healthcare services.

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