ORAMA: A Unified Computer Vision Framework for Real-Time Exercise Supervision, Functional Assessment and Remote Monitoring
Orestis N. Zestas, Dimitrios N. Soumis, Konstantinos I. Roumeliotis, Kyriakos D. Kyriakou, Stefania Tzanera, Konstantinos Laloudakis, Vasileios Sakellariou Kyrou, Theoni Moraitou, Sofia Kapellaki, Kyriaki Seklou, Nikolaos D. Tselikas · Applied Sciences · 2026
Remote exercise supervision and functional movement assessment require sensing pipelines that can capture body motion, interpret protocol progression, and provide meaningful feedback within the same runtime environment. This paper presents ORAMA, an integrated computer vision platform for the execution and remote monitoring of digital exercises and clinically oriented assessment protocols related to physical fitness, mobility, balance, and health. The system combines ZED 2i stereo capture and depth-aware body tracking with a protocol-driven software architecture that includes a computer-vision pipeline, an exercise and assessment engine, a real-time feedback layer, persistent session handling, structured output generation, and a chatbot-assisted interaction path. Unlike solutions that focus only on movement recognition, ORAMA organizes each task as an explicit executable protocol with calibration stages, state transitions, task-specific metrics, and live visual guidance. The paper analyzes the system architecture, reviews the surrounding literature on virtual coaching and rehabilitation-oriented computer vision, and demonstrates representative user-interface and runtime views for both assessment and exercise scenarios. The present work reports a prototype architecture and representative operational demonstrations, rather than a completed clinical validation or participant-based efficacy study. The resulting platform shows how markerless 3D body tracking can be embedded within a unified and interpretable environment for guided exercise, functional testing, and remote follow-up without requiring wearable sensors.