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Real-Time Compliant Stream Processing Agents for Physical Rehabilitation

Institute of Information Systems HES-SO Valais-Wallis, University of Applied Sciences and Arts Western Switzerland HES-SO, TechnoPole 3, CH-3960 Sierre, Switzerland
Author to whom correspondence should be addressed.
Sensors 2020, 20(3), 746;
Received: 15 December 2019 / Revised: 27 January 2020 / Accepted: 28 January 2020 / Published: 29 January 2020
(This article belongs to the Special Issue Semantics for Sensors, Networks and Things)
Digital rehabilitation is a novel concept that integrates state-of-the-art technologies for motion sensing and monitoring, with personalized patient-centric methodologies emerging from the field of physiotherapy. Thanks to the advances in wearable and portable sensing technologies, it is possible to provide patients with accurate monitoring devices, which simplifies the tracking of performance and effectiveness of physical exercises and treatments. Employing these approaches in everyday practice has enormous potential. Besides facilitating and improving the quality of care provided by physiotherapists, the usage of these technologies also promotes the personalization of treatments, thanks to data analytics and patient profiling (e.g., performance and behavior). However, achieving such goals implies tackling both technical and methodological challenges. In particular, (i) the capability of undertaking autonomous behaviors must comply with strict real-time constraints (e.g., scheduling, communication, and negotiation), (ii) plug-and-play sensors must seamlessly manage data and functional heterogeneity, and finally (iii) multi-device coordination must enable flexible and scalable sensor interactions. Beyond traditional top-down and best-effort solutions, unsuitable for safety-critical scenarios, we propose a novel approach for decentralized real-time compliant semantic agents. In particular, these agents can autonomously coordinate with each other, schedule sensing and data delivery tasks (complying with strict real-time constraints), while relying on ontology-based models to cope with data heterogeneity. Moreover, we present a model that represents sensors as autonomous agents able to schedule tasks and ensure interactions and negotiations compliant with strict timing constraints. Furthermore, to show the feasibility of the proposal, we present a practical study on upper and lower-limb digital rehabilitation scenarios, simulated on the MAXIM-GPRT environment for real-time compliance. Finally, we conduct an extensive evaluation of the implementation of the stream processing multi-agent architecture, which relies on existing RDF stream processing engines. View Full-Text
Keywords: stream reasoning; real-time multi-agents; RDF stream processing; stream processing agents; digital rehabilitation; real-time sensors stream reasoning; real-time multi-agents; RDF stream processing; stream processing agents; digital rehabilitation; real-time sensors
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MDPI and ACS Style

Calvaresi, D.; Calbimonte, J.-P. Real-Time Compliant Stream Processing Agents for Physical Rehabilitation. Sensors 2020, 20, 746.

AMA Style

Calvaresi D, Calbimonte J-P. Real-Time Compliant Stream Processing Agents for Physical Rehabilitation. Sensors. 2020; 20(3):746.

Chicago/Turabian Style

Calvaresi, Davide, and Jean-Paul Calbimonte. 2020. "Real-Time Compliant Stream Processing Agents for Physical Rehabilitation" Sensors 20, no. 3: 746.

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