Brain And Gesture Controlled Assistive System For Physically Challenged Individuals

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 13 | 01 | Page :
    By

    Ashmin Sabu,

  • Abhijith T.R.,

  • Abhijith K.B.,

  • Aswin K.S.,

  • Sonima M.P.,

  • Reshma S.,

  1. Student, Department of Electrical and Electronics Engineering, College of Engineering Kidangoor, Kerala, India
  2. Student, Department of Electrical and Electronics Engineering, College of Engineering Kidangoor, Kerala, India
  3. Student, Department of Electrical and Electronics Engineering, College of Engineering Kidangoor, Kerala, India
  4. Student, Department of Electrical and Electronics Engineering, College of Engineering Kidangoor, Kerala, India
  5. Assistant Professor, Department of Electrical and Electronics Engineering, College of Engineering Kidangoor, Kerala, India
  6. Assistant Professor, Department of Electrical and Electronics Engineering, College of Engineering Kidangoor, Kerala, India

Abstract

Assistive communication technologies are essential for improving the independence of individuals with physical and sensory disabilities. This paper presents the design and implementation of a multimodal assistive system that integrates brain signal acquisition and gesture recognition for real- time communication. The system utilizes an Electroencephalography (EEG) sensor to capture neural activity and a PAJ7620 gesture sensor along with an ADXL335 accelerometer to detect hand movements. The acquired signals are processed using an Arduino Nano microcontroller, where predefined logic and threshold detection are applied to generate assistive commands. These commands are displayed on a 16×2 LCD and transmitted wirelessly using a 433 MHz RF module. A DF Mini MP3 module provides voice output for emergency communication. The system demonstrates an average response time below one second, gesture recognition accuracy above 90%, and reliable wireless communication up to 20 meters. The proposed system offers a low-cost, portable, and efficient solution for assistive communication. Designed for ease of use and requiring little training, the system is accessible to people with different levels of disability. Its modular design permits easy expansion with extra sensors or advanced AI-based processing to enhance accuracy and adaptability. By diminishing reliance on one communication method, the incorporation of multimodal inputs improves reliability. This strategy helps to boost users’ confidence and feelings of safety, as well as their quality of life. It also offers a scalable framework for future developments in assistive technology.

Keywords: Assistive Technology, Brain–Computer Interface (BCI), Electroencephalography (EEG), Gesture Recognition, Arduino Nano, Embedded Systems, RF Communication, Multimodal Control

How to cite this article:
Ashmin Sabu, Abhijith T.R., Abhijith K.B., Aswin K.S., Sonima M.P., Reshma S.. Brain And Gesture Controlled Assistive System For Physically Challenged Individuals. Recent Trends in Sensor Research & Technology. 2026; 13(01):-.
How to cite this URL:
Ashmin Sabu, Abhijith T.R., Abhijith K.B., Aswin K.S., Sonima M.P., Reshma S.. Brain And Gesture Controlled Assistive System For Physically Challenged Individuals. Recent Trends in Sensor Research & Technology. 2026; 13(01):-. Available from: https://journals.stmjournals.com/rtsrt/article=2026/view=241490


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Ahead of Print Subscription Review Article
Volume 13
01
Received 23/04/2026
Accepted 25/04/2026
Published 29/04/2026
Publication Time 6 Days
7

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