Abstract

This project proposes the development of an IoT-based posture detection and battery management system for a multimode wheelchair, enhanced with machine learning capabilities. The system aims to monitor the user’s posture in real-time, providing alerts and adjustments to prevent discomfort and health issues. Additionally, it will optimize battery usage and management to ensure the wheelchair’s operational efficiency and longevity. By integrating IoT sensors, machine learning algorithms, and smart battery management, the project seeks to improve the overall usability and safety of multimode wheelchairs.

Introduction

Multimode wheelchairs, which offer various modes of operation such as manual, electric, and assisted, are crucial for enhancing mobility and independence for individuals with disabilities. However, improper posture can lead to discomfort and long-term health problems, while inefficient battery management can limit the wheelchair’s usability. This project aims to address these issues by developing an IoT-based system for real-time posture detection and intelligent battery management. By leveraging machine learning, the system can provide personalized and adaptive solutions to enhance user comfort and wheelchair performance.

Problem

Users of multimode wheelchairs often face challenges related to maintaining proper posture and managing battery life. Prolonged incorrect posture can result in discomfort, pressure sores, and other health complications. Meanwhile, inefficient battery usage can lead to unexpected power depletion, reducing the reliability and independence provided by the wheelchair. Current solutions lack real-time monitoring and adaptive management capabilities, necessitating the development of a more intelligent and responsive system.

Aim

The primary aim of this project is to develop an IoT-based posture detection and battery management system for multimode wheelchairs, incorporating machine learning to provide real-time monitoring and adaptive control. The system will enhance user comfort, prevent health issues related to poor posture, and optimize battery usage to ensure reliable wheelchair operation.

Objectives

1. Research and Analysis Study existing posture detection techniques, battery management systems, and machine learning algorithms to identify the best approaches for integration.

2. System Design Design the architecture of the IoT-based system, including sensors for posture detection, battery monitoring modules, and communication protocols.

3. Sensor Integration Develop and integrate IoT sensors for real-time monitoring of the user’s posture and battery status.

4. Machine Learning Algorithms Implement machine learning algorithms to analyze posture data and predict optimal battery management strategies.

5. Prototyping Build a prototype of the system and integrate it with a multimode wheelchair, ensuring seamless communication and operation.

6. Testing and Validation Conduct extensive testing and validation of the system in various scenarios to evaluate its accuracy, reliability, and user acceptance.

7. User Interface Develop a user-friendly interface for caregivers and users to monitor posture and battery status, receive alerts, and adjust settings.

8. Optimization Optimize the system for real-time performance, low power consumption, and minimal maintenance requirements.

Research

The project involves comprehensive research in IoT technology, biomedical engineering, machine learning, and power management. Initial research will focus on reviewing existing literature on posture detection methods, battery management techniques, and machine learning models suitable for real-time applications. The design phase will involve creating detailed system architecture, selecting appropriate sensors and hardware components, and developing algorithms for data processing and analysis. The development phase will include programming the IoT devices, training machine learning models, and integrating the components into a functional prototype. Testing and validation will involve both simulated environments and real-world trials to ensure the system meets the required standards of accuracy, reliability, and user satisfaction.

Ethical Considerations

The project will address ethical considerations such as user privacy, data security, and compliance with healthcare regulations. Ensuring that user data is protected and that the system operates safely and effectively will be paramount. The design and development process will adhere to relevant standards and guidelines to ensure that the technology is both ethical and beneficial to users.

By developing an IoT-based posture detection and battery management system enhanced with machine learning, this project aims to significantly improve the functionality, comfort, and reliability of multimode wheelchairs. The result will be a smarter, more responsive wheelchair that better meets the needs of its users.