Powered Wheelchair Controller Using Hybrid Bio-Signals

Table of Contents

1 INTRODUCTION

1.1 History

1.2 Types of wheelchairs

Manual wheelchairs

Electric-powered wheelchairs

Limitation of the electric chairs

Smart or Intelligent wheelchair

1.3 Current research

2 GOALS AND OBJECTIVE

2.1 Project description

2.2 Hardware

Data acquisition box

Wheelchair

2.3 Software

Software description

Data collection

Pre-processing and data segmentation

Feature extraction

Classification

Algorithm

Training and Testing

Normalizing

Decision process

User interface

3 EVALUATION

Component testing

Sub-system testing

System testing

4 FUTURE DEVELOPMENT

4.1 Challenges faced and recommendations for future work

5 PROJECT PLAN

Work breakdown structure

Gantt chart

5.1 Conclusion

Project Goals and Themes

This project aims to design and implement a hybrid control algorithm for an intelligent wheelchair, specifically targeting the needs of quadriplegic individuals. By utilizing bio-signals (EEG, EOG, and EMG) captured via a headband, the system enables users to navigate a wheelchair with minimal human assistance, significantly improving their independence and social mobility.

• Integration of hybrid bio-signal processing (EEG, EOG, EMG)
• Development of pattern recognition using Artificial Neural Networks (ANN)
• Design of a human-machine interface for wheelchair control
• Real-time signal acquisition and noise-reduction strategies
• System evaluation through component, sub-system, and full system testing

Excerpt from the Book

Summary of Chapters

1 INTRODUCTION: Outlines the necessity of assistive technologies for the disabled and elderly, introducing the role of bio-signals in human-machine interaction.

2 GOALS AND OBJECTIVE: Defines the project's purpose, detailing the system architecture, hardware components, and the software methodology for signal processing and ANN classification.

3 EVALUATION: Discusses the three-tier testing process, covering individual component performance, sub-system functionality, and overall system reliability in real-time scenarios.

4 FUTURE DEVELOPMENT: Addresses observed limitations in signal precision and recommends enhancements, such as adding more sensor channels and utilizing advanced pattern matching algorithms.

5 PROJECT PLAN: Details the time management, work breakdown structure, and scheduling strategies employed to complete the project phases.

Keywords

Human Machine Interaction, Intelligent wheelchair, EEG, EMG, EOG, Artificial Neural Network, ANN, Bio-signals, Quadriplegic, Pattern Recognition, Signal Processing, Assistive Technology, Cyberlink, Control Algorithm, Rehabilitation.

Frequently Asked Questions

What is the primary focus of this project?

The project focuses on developing an intelligent control system for a powered wheelchair that allows individuals with mobility limitations, specifically quadriplegic patients, to navigate independently using bio-signals instead of manual controls.

Which specific bio-signals are utilized for control?

The system records and processes Electromyogram (EMG) signals from facial muscles, Electrooculogram (EOG) signals from eye movements, and Electroencephalogram (EEG) signals from the brain.

What is the core objective of the research?

The main goal is to create a robust and reliable hybrid bio-signal algorithm that translates specific user inputs—such as forehead movements and eye glances—into distinct navigation commands for the wheelchair.

How is the signal classification handled?

The system employs an Artificial Neural Network (ANN) as a classifier. It is trained to recognize specific movement patterns from the pre-processed bio-signal data and map these to motor commands.

What does the main body of the work cover?

The main body details the hardware setup (including the headband sensors and the wheelchair's internal processor), the software logic for data collection and segmentation, feature extraction, ANN training/testing, and the final decision-making process.

Which keywords best describe the work?

Key terms include Human-Machine Interaction (HMI), Intelligent Wheelchair, Bio-signals (EEG, EMG, EOG), Artificial Neural Networks (ANN), and assistive robotics.

Why were forehead-based bio-signals preferred for this project?

The forehead provides a non-invasive, accessible, and less intrusive location for capturing useful signals compared to traditional electrode caps, making it more socially acceptable and easier for paralyzed individuals to use.

What was a major challenge mentioned in the implementation?

A significant challenge was the real-time extraction of clean control signals from noisy face movements, requiring a trade-off between algorithmic complexity and system performance during execution.

What role does the fatigue indicator serve?

The fatigue indicator monitors signal strength and usage duration to prevent false command triggering, ensuring safe operation if the user is exhausted or if sensor contact quality degrades over time.