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Real-Time Compliant Stream Processing Agents for Physical Rehabilitation.

Davide Calvaresi1, Jean-Paul Calbimonte1

  • 1University of Applied Sciences and Arts Western Switzerland HES-SO, Institute of Information Systems HES-SO Valais-Wallis, TechnoPole 3, CH-3960 Sierre, Switzerland.

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Summary
This summary is machine-generated.

Digital rehabilitation uses advanced sensors for personalized physiotherapy. This study introduces decentralized agents for real-time coordination, addressing technical challenges in wearable technology for improved patient care.

Keywords:
RDF stream processingdigital rehabilitationreal-time multi-agentsreal-time sensorsstream processing agentsstream reasoning

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Rehabilitation Medicine

Background:

  • Digital rehabilitation integrates advanced sensing technologies with physiotherapy for personalized patient care.
  • Wearable and portable sensors enable accurate monitoring of exercise performance and treatment effectiveness.
  • Personalized treatments can be enhanced through data analytics and patient profiling.

Purpose of the Study:

  • To address technical and methodological challenges in digital rehabilitation, specifically real-time constraints, sensor data heterogeneity, and multi-device coordination.
  • To propose a novel approach using decentralized real-time compliant semantic agents for autonomous coordination and task scheduling.
  • To present a model where sensors act as autonomous agents capable of real-time interaction and negotiation.

Main Methods:

  • Development of decentralized semantic agents capable of autonomous coordination and real-time task scheduling.
  • Utilization of ontology-based models to manage data heterogeneity from heterogeneous sensors.
  • Simulation of upper and lower-limb digital rehabilitation scenarios in the MAXIM-GPRT environment for real-time compliance testing.

Main Results:

  • Demonstrated the feasibility of the proposed agent-based model in digital rehabilitation scenarios.
  • Validated the real-time compliance of autonomous agent coordination and task scheduling.
  • Evaluated the performance of the stream processing multi-agent architecture using RDF stream processing engines.

Conclusions:

  • The proposed decentralized real-time compliant semantic agent approach effectively addresses key challenges in digital rehabilitation.
  • This framework enables flexible, scalable, and reliable sensor interactions for personalized and effective patient care.
  • The study highlights the potential of autonomous agent systems in advancing digital rehabilitation technologies.