His paper, titled "A Framework for Assessing Physical Rehabilitation Exercises," introduces an innovative framework for evaluating rehabilitation exercises and patient progress. Inspired by the WHO's "Rehabilitation 2030" initiative and aligned with SDG 3 on Health and Wellbeing, his goal is to reduce rehabilitation costs while enabling personalized treatment plans through automated exercise assessment.

Using an RGB camera to capture patient movements and extract skeletal components for classification, his framework demonstrated impressive results as it achieved a remarkable 99.64% accuracy rate on a benchmarking dataset and a solid 90% accuracy rate on datasets from university clinics.

You can read the paper through the following link:

Congratulations Moamen for your outstanding work