OrthoPredict

OrthoPredict: Harnessing AI to Measure Your Path to Recovery

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About the Project

OrthoPredict is designed to revolutionize the way patients with fractures recover by leveraging AI and IoT technologies. Frequent doctor visits are often impractical and inconvenient, especially for those living far from medical facilities. Healthcare providers also struggle to monitor each patient’s progress effectively. OrthoPredict addresses these issues by offering a remote and efficient solution that benefits both patients and doctors.

Key Aspects of OrthoPredict:

  • IoT Integration: Utilizes MPU 6050 sensors to capture real-time data on bone movement.
  • Machine Learning Algorithms: Analyzes the collected data to predict recovery progress.
  • Cloud Storage: Stores patient data securely on Google Cloud for easy access and analysis.
  • Remote Monitoring: Sends detailed recovery reports to doctors, enabling timely consultations.
  • Recovery Criteria: Provides a clear recovery percentage to gauge progress accurately.

Inspiration

The inspiration for OrthoPredict came from recognizing the immense inconvenience and challenges faced by patients with fractures in regularly visiting doctors. This inspired us to create a solution that enables remote monitoring and timely consultations, thus making the recovery process smoother and more efficient for both patients and healthcare providers.

What it does

  • Captures Real-Time Data: Uses MPU 6050 sensors to monitor bone rotation angles.
  • Predicts Recovery Progress: Employs machine learning algorithms to analyze data and predict recovery stages.
  • Sends Reports: Automatically generates and sends recovery reports to doctors.
  • Remote Consultations: Facilitates remote consultations, reducing the need for frequent hospital visits.
  • Secure Data Storage: Stores all patient data on Google Cloud for secure and easy access.

How we built it

OrthoPredict is built using a combination of modern technologies to ensure accuracy, reliability, and ease of use:

  • MERN Stack:
    • Frontend: Developed with React.
    • Backend: Managed with Node.js and Express.
    • Database: MongoDB for data storage.
  • IoT Devices:
    • Utilized MPU 6050 sensors to capture bone movement data.
  • Google Cloud Storage:
    • Ensures secure and scalable data storage.
  • Flask:
    • Bridges the IoT devices and machine learning algorithms.
  • Machine Learning Algorithms:
    • Processes sensor data to predict recovery status in real-time.

Challenges we ran into

Building OrthoPredict was a rewarding yet challenging experience. Some of the key challenges included:

  • Data Accuracy: Ensuring the MPU 6050 sensor provided accurate and consistent data was crucial.
  • Integration: Seamlessly integrating the IoT devices with the MERN stack and Flask.
  • Machine Learning: Developing robust algorithms that could provide reliable recovery predictions.
  • Cloud Storage: Managing and securing a large volume of patient data on Google Cloud.
  • User Experience: Creating an intuitive interface for both patients and doctors to easily access and understand recovery data.

Accomplishments that we're proud of

  • Successfully creating a working prototype that accurately captures and analyzes bone movement data.
  • Seamlessly integrating various technologies to provide a cohesive and user-friendly solution.
  • Developing machine learning algorithms that provide reliable recovery predictions.
  • Designing a user-friendly interface for both patients and doctors to easily access and understand recovery data.

What we learned

  • The importance of precise data collection for accurate predictions.
  • Effective integration of IoT, cloud storage, and machine learning technologies.
  • The challenges and solutions in developing user-friendly healthcare applications.
  • How to ensure data security and privacy in healthcare solutions.

What's next for OrthoPredict

  • Enhance Machine Learning Models: Improve accuracy of recovery predictions.
  • Expand Prototype: Include more types of injuries and recovery scenarios.
  • Advanced IoT Devices: Integrate more sophisticated devices for comprehensive data collection.
  • Healthcare Collaborations: Work with healthcare providers for broader adoption and feedback.
  • Large-Scale Implementation: Explore partnerships for widespread implementation and continuous improvement.

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