The Wearable Osteoarthritis Monitor (WOM) is an IoT solution developed as a final year project which is designed to facilitate the early diagnosis and post-total knee replacement (TKR) monitoring of osteoarthritis. With an aging population and increasing incidents among the youth due to injuries, osteoarthritis has become a prevalent condition, often requiring careful management and treatment. WOM aims to provide accurate, real-time data regarding knee flexion and patient pain levels, aiding healthcare professionals in making informed decisions.
Osteoarthritis is a degenerative disease primarily affecting the elderly but not uncommon in younger populations post-injury. It leads to the progressive erosion of joint cartilage, culminating in the painful grinding of bones against each other. WOM seeks to assist in the early detection of such conditions and offers a monitoring tool for the rehabilitation process post-surgery, particularly after TKR procedures.
- Knee Flexion Data: Measures and provides real-time knee flexion angles.
- Baseline Comparisons: Compares patient data against established baselines to determine the risk and progression of osteoarthritis.
- Visual Feedback: Utilizes color-coded numbers and charts for easy interpretation of the condition and rehabilitation progress.
- Secure Data Transmission: Ensures that patient data is securely shared with healthcare providers for further analysis.
- Rehabilitation Assistance: Offers recommendations for pain relief and tracks the rehabilitation process.
The WOM system comprises two main components:
- Mobile Application: For user interaction, visualization of data, and communication with the healthcare team.
- Arduino-based Monitoring Device: For collecting physical data from the patient.
Both components are designed to work in tandem to provide a seamless user experience and accurate data collection.
- Mobile App: Instructions on installation and usage for various platforms will be provided here.
- Arduino Code: Steps to set up the monitoring device with the necessary code will be detailed here.
For more insights and updates about the project, visit our WOM Blog.
This project is licensed under the MIT License - see the LICENSE file for details.