Automated Range of Motion (ROM) Measurement using Human Pose Estimation

We developed an automated Range of Motion (ROM) measurement web/mobile application for patient and healthcare providers with real-time monitoring and scalable deployment. The platform integrates over 200 active and passive ROM exercises, utilizing full-body pose estimation models to accurately analyze patient videos. Moreover, to boost model precision, we fine-tuned exercise-specific pose estimation models using custom in-house datasets. For scalability, we designed a distributed system with a master-slave architecture to efficiently manage high-volume API requests. We also leveraged Amazon EC2 and S3 to ensure a robust, cloud-based infrastructure, supporting seamless ROM assessments in both telehealth and clinical environments.

We developed an automated Range of Motion (ROM) measurement web/mobile application for patients and healthcare providers with real-time monitoring and scalable deployment. This innovative platform represents a significant advancement in telehealth and clinical assessment tools.

Key Features

Real-time Range of Motion measurement using pose estimation (placeholder image)

Comprehensive Exercise Library

The platform integrates over 200 active and passive ROM exercises, utilizing full-body pose estimation models to accurately analyze patient videos. This extensive library covers all major joint movements and therapeutic exercises commonly used in physical therapy.

Advanced 3D Pose Estimation

I contributed to developing a novel 3D pose estimation technique specifically designed to enhance the measurement of trunk rotation ROM. This innovation addresses one of the most challenging aspects of ROM assessment - accurately capturing rotational movements in three-dimensional space.

Machine Learning Optimization

To boost model precision, we fine-tuned exercise-specific pose estimation models using custom in-house datasets. This targeted approach ensures high accuracy across different exercise types and patient populations.

Technical Architecture

Left: Distributed system architecture. Right: Cloud infrastructure setup. (placeholder images)

Scalable System Design

For scalability, I designed a distributed system with a master-slave architecture to efficiently manage high-volume API requests. This design ensures consistent performance even under heavy load conditions.

Cloud Infrastructure

We leveraged Amazon EC2 and S3 to ensure a robust, cloud-based infrastructure, supporting seamless ROM assessments in both telehealth and clinical environments. The system can automatically scale based on demand and maintains high availability.

Clinical Applications

Telehealth Integration

The platform seamlessly integrates with existing telehealth workflows, allowing healthcare providers to:

  • Conduct remote ROM assessments
  • Monitor patient progress over time
  • Provide real-time feedback during exercises
  • Generate comprehensive reports for clinical documentation

Real-World Impact

The system has been deployed in clinical environments, demonstrating:

  • Improved patient engagement in physical therapy
  • Reduced need for in-person assessment visits
  • Enhanced accuracy in ROM measurements
  • Streamlined clinical workflows

Technical Specifications

  • Platform: Web and mobile application
  • Pose Estimation: Full-body and 3D trunk rotation models
  • Exercise Library: 200+ active and passive ROM exercises
  • Infrastructure: AWS EC2/S3 with auto-scaling
  • Architecture: Distributed master-slave system
  • Real-time Processing: Low-latency video analysis

This project was developed at MyMedicalHUB Corp., FL, USA, representing a significant advancement in digital health technology and remote patient monitoring.