Edge AI and Knowledge Graph Integration for Cross-Setting Special Population Rehabilitation

Abstract

Managing fragmented, highly heterogeneous data streams across home, school, and clinical environments for special population rehabilitation remains a persistent, systemic bottleneck in modern telemedicine. This paper introduces a decentralized digital therapeutic framework that attempts to integrate domain-specific knowledge graphs with wearable edge artificial intelligence inference to establish continuous, cross-setting tracking. In executing our initial field deployments, the anticipated linear alignment of multi-modal behavioral metrics collapsed due to irregular user adherence and severe device-side computational constraints, forcing an abrupt methodological pivot toward localized, task-affinity-aware scheduling algorithms that dynamically calibrate to ambient noise. While empirical observations indicate that these gamified closed-loop interventions enhance localized motor and cognitive expression to some extent, alternative interpretations suggest these observed gains might partially stem from short-term novelty effects rather than true neural plasticity or long-term clinical recovery, a potential interpretation bias heavily compounded by inherent data sparsity. Considering these confounding variables, our framework demonstrates how localized intelligence can bridge institutional boundaries without relying on flawless data flows. This structural shift moves the paradigm toward an adaptive, ecology-based model of digital care, though further longitudinal research is needed to fully uncover the macro-level socio-clinical mechanisms underlying sustained patient compliance.