Privacy-Preserving Federated Learning for Human Intention Modeling in Pediatric Cerebral Palsy Using Extended Reality

Accurately modeling human intentions in pediatric cerebral palsy (CP) rehabilitation is essential for providing successful, adaptive therapy that responds to each child's particular motor and cognitive characteristics. Conventional observation-based methods frequently fail to detect nuanced or unusual intention patterns, particularly in young children with intricate motor disorders. This study presents a theoretical framework that combines privacy-preserving federated learning (FL) with immersive extended reality (XR) technology to facilitate real-time, personalized intention recognition in therapeutic contexts. The system utilizes the immersive features of the Meta Quest Pro headset for interactive pediatric rehabilitation and the edge-processing capabilities of NVIDIA Jetson devices to do on-device inference and federated model updates without transferring sensitive patient information. The proposed architecture safeguards data privacy while facilitating decentralized model training in distant clinical settings. Our conceptual framework delineates multimodal data capture, federated aggregation procedures, adaptive XR feedback, and intention-aware therapeutic modifications - executed fully offline and under complete local control. This paper offers a scalable and ethically acceptable theoretical framework for revolutionizing pediatric rehabilitation using secure, intelligent, and immersive therapeutic technology, without necessitating implementation.