Adaptive Framework for Ambient Intelligence in Rehabilitation Assistance
Journal:
arXiv
Published Date:
Jul 11, 2025
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 privacy
concerns and promoting compliance with the AI Act. The system guides users
through the recording process to ensure the collection of properly recorded
videos. AIRS employs two feedback mechanisms: (i) visual 3D feedback, enabling
direct comparisons between prerecorded clinical exercises and patient home
recordings and (ii) VLM-generated feedback, providing detailed explanations and
corrections for exercise errors. The framework also supports people with visual
and hearing impairments. It also features a modular design that can be adapted
to broader rehabilitation contexts. AIRS software components are available for
further use and customization.
