Adaptive Framework for Ambient Intelligence in Rehabilitation Assistance
G\'abor Baranyi, Zsolt Csibi, Kristian Fenech, \'Aron F\'othi, Zs\'ofia Ga\'al, Joul Skaf, Andr\'as L\H{o}rincz

TL;DR
The AIRS framework leverages AI, 3D reconstruction, and vision-language models to enhance home rehabilitation, providing real-time feedback, privacy solutions, and accessibility for diverse users.
Contribution
This paper presents the novel AIRS framework integrating advanced AI technologies for personalized, privacy-aware, and accessible home rehabilitation support.
Findings
Demonstrated effectiveness in TKR rehabilitation scenarios
Enabled real-time 3D reconstruction and feedback
Supported users with visual and hearing impairments
Abstract
This paper introduces the Ambient Intelligence Rehabilitation Support (AIRS) framework, an advanced artificial intelligence-based solution tailored for home rehabilitation environments. AIRS integrates cutting-edge technologies, including Real-Time 3D Reconstruction (RT-3DR), intelligent navigation, and large Vision-Language Models (VLMs), to create a comprehensive system for machine-guided physical rehabilitation. The general AIRS framework is demonstrated in rehabilitation scenarios following total knee replacement (TKR), utilizing a database of 263 video recordings for evaluation. A smartphone is employed within AIRS to perform RT-3DR of living spaces and has a body-matched avatar to provide visual feedback about the excercise. This avatar is necessary in (a) optimizing exercise configurations, including camera placement, patient positioning, and initial poses, and (b) addressing…
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