Morse Code-Based Assistive Communication System for Individuals with Partial Paralysis

Abstract

Individuals with partial paralysis often face challenges in communication due to limited mobility. This project presents a Morse code-based assistive communication system that enables users to convey messages using a six-button interface. The system processes Morse inputs in real-time, providing predictive text suggestions on an OLED display, which can be selected with minimal effort. Once a word or phrase is confirmed, the text is converted into speech using a Bluetooth-enabled audio device such as a speaker or headset. By offering an intuitive and efficient communication aid, this system enhances accessibility and independence for individuals with mobility impairments, ensuring smoother and more effective interaction in daily life.

Introduction

1. Introduction

Communication is essential for human interaction, but individuals with partial paralysis (due to strokes, spinal cord injuries, or neurological disorders) face serious communication barriers. Current assistive technologies are often:

• Expensive

• Complex

• Difficult to learn

To address these issues, this project proposes a low-cost, Morse code-based communication system that enables users to communicate independently using simple physical inputs (e.g., button presses or eye blinks). These inputs are translated into Morse code, which is then converted into text or speech for effective communication.

2. Literature Review

Existing research in assistive gesture recognition systems includes:

• Static/dynamic gesture recognition using tools like OpenCV, TensorFlow, and MediaPipe (Bera, Negi, Patil, Mary)

• Real-time ASL recognition using neural networks (Sankar)

Limitations:

• Focus on specific languages or gestures

• Need for advanced hardware

• Susceptibility to lighting conditions and gesture misinterpretation

• Often not suitable for users with very limited mobility

3. Proposed Methodology

The system is built around the Raspberry Pi Zero 2W, using:

• A six-button interface:

  • 2 buttons for Morse input (dot and dash)

  • 4 buttons for navigation, backspace, and confirmation

• An OLED display:

  • Shows real-time text

  • Provides predictive text suggestions for common words

• A Bluetooth audio system:

  • Uses Google Text-to-Speech (TTS) for vocal output

  • Supports devices like speakers, AirPods, or headbands

• Customizable interface:

  • Adapts to varying motor abilities

  • Includes easy setup and calibration

The system aims for high accessibility, efficiency, and independence, reducing reliance on caregivers or complex systems.

4. System Architecture (Block Diagram + Flowchart)

Key Components:

1. Raspberry Pi Zero 2W

   • Central processing unit

   • Handles Morse code conversion, predictive suggestions, and Bluetooth TTS output

   • Also supports real-time health monitoring and alerts

2. Input Buttons

   • Ergonomic, tactile buttons suitable for users with limited mobility

3. OLED Display

   • Visual feedback for inputs

   • Shows suggested words/phrases based on Morse inputs

4. Bluetooth Audio Output

   • Converts selected text to speech using Google TTS

   • Enables verbal communication

5. Predictive Text

   • Suggests common words (e.g., typing “W” suggests “water” or “washroom”)

   • Learns user habits over time for personalized suggestions

6. Health Monitoring (mentioned but inconsistent)

   • Though details were unclear or mixed with farming systems, the idea is to integrate user health tracking for safety

7. Adaptive Interface

   • Customizable layout and input options for different user needs

8. 5V Power Supply

   • Ensures reliable operation without frequent charging

9. Wireless Connectivity

   • Supports future upgrades like cloud data, remote monitoring, and app integration

5. Results

• A working prototype has been developed using the above components

• OLED display successfully shows Morse-translated text and predictive suggestions

• Google TTS effectively converts text to speech for verbal output

• The system offers a low-cost, reliable, and accessible communication tool for users with mobility impairments

Conclusion

By integrating Morse code communication, predictive text, and speech synthesis, the proposed assistive communication system significantly enhances the quality of life for individuals with partial paralysis. The use of the Raspberry Pi Zero 2W as the central controller ensures efficient processing, while the six-button interface offers a simple and intuitive way for users to input Morse code. The OLED screen provides real-time visual feedback, allowing users to easily navigate and select words, reducing the effort required for communication. The Bluetooth-enabled audio output system ensures that the text is converted into speech, enabling users to interact vocally with caregivers and others in their environment. The system’s predictive text feature improves communication efficiency by suggesting commonly used words and phrases, further minimizing the time spent on input. Additionally, real-time health monitoring ensures that users’ well-being is continuously tracked, triggering alerts for timely intervention in case of any health anomalies. Customizability is a key feature of the system, allowing users to adjust the interface and settings according to their needs. The 5V adapter power supply ensures continuous operation, and the low-power consumption of the Raspberry Pi Zero 2W extends the system\'s usability. Future scalability through wireless connectivity and potential integration of AI and machine learning could enhance predictive capabilities, making the system adaptable to evolving assistive technology needs. This assistive communication system combines accessibility, efficiency, and sustainability, offering individuals with severe mobility impairments an improved way to interact with the world around them. Through its integration of advanced technologies and ease of use, the system not only provides an effective solution for communication but also empowers users with greater independence, ultimately contributing to the advancement of assistive technologies in healthcare and personal care.

References

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Copyright

Copyright © 2025 Pravin Kumar S K, Prathap A, Geetha M, Boomish G, Dev Prakash S A. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.