Projects
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. I also contributed developing a novel 3D pose estimation technique to enhance the measurement of trunk rotation ROM. Moreover, to boost model precision, we fine-tuned exercise-specific pose estimation models using custom in-house datasets.
For scalability, I 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.
Abnormal Gait Analysis using Video-based Signal Processing
This project focuses on the development of an advanced gait event and abnormality detection system using pose estimation techniques. The system incorporates a signal-based approach to improve the accuracy of gait event detection, outperforming traditional angle-based methods, resulted in a patented solution. The project involved analyzing a diverse range of abnormal gait patterns, including Antalgic, Ataxic, Hemiplegic, Parkinsonian, and Trendelenburg gaits, using an in-house database of human participants. Collaborating closely with MyMedicalHUB's (MMH) clinical team of physicians and physical therapists, the project ensured clinical relevance and accuracy.
Balance and Fall Risk Assessment for Elderly Patients
This project focuses on developing a video-based system for balance monitoring and fall risk assessment for elderly patients. Using joint distortion metrics from a reference axis (white line shown in the image), the system predicts balance scores and fall risks incorporating Timed Up and Go Test (TUG) and Berg Balance Scale (BBS) tests. Designed for real-world clinical environments, the solution integrates computer vision techniques to enhance safety and improve elderly care outcomes.
Safegaze API for Kahf Browser
We developed a lightweight computer vision pipeline for Kahf Android Browser (10K+ downloads), ensuring safe browsing experience aligned with Islamic values (Featured in The Daily Star).