Real-Time Compliant Stream Processing Agents for Physical Rehabilitation

Abstract

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.

Potential of digitalization within physiotherapy: a comparative survey

“…With the current digital transformation of the healthcare system, also physiotherapy is increasingly affected by new developments [ 3 , 4 , 30 ]. Various elements like electronic documentation, telehealth and mHealth can make physiotherapists’ daily work easier and increase their efficiency [ 5 , 6 , 14 ].…”

“…While some medical disciplines, such as radiology, have already been highly influenced by digital innovations, others with need for a physical examination, such as orthopedics, are rather in an early stage of digital adaptation [ 2 ]. As a healthcare discipline with close and intense patient contact, but also with many aspects of transportable digital knowledge, physiotherapy is also increasingly affected by digital influences [ 3 , 4 ].…”

Real-time multi-agent systems: rationality, formal model, and empirical results

“…Exploiting such a simulator, we have tested three rehabilitation scenarios. In particular, we have designed and tested (simulated) several task-sets and agent distribution over wearable inertial sensors [19]. Such a study aims to show how RT-MAS can perform over distributed wearable-based scenarios when dealing with real-time data-stream processing.…”

In-Time Explainability in Multi-Agent Systems: Challenges, Opportunities, and Roadmap

“…Nevertheless, depending on the joint(s) to rehabilitate, the therapy might be more complex (from both physical and cognitive viewpoints). A first step to enable telerehabilitation for more demanding therapies is presented in [13], where the authors developed a semantic model for RT-MAS enabling more elaborated -yet real-time compliant-interactions among the wearable sensors. Along this path, we envision that RTX-BDI-MAS can empower the telerehabilitation systems by providing not only real-time monitoring and feedback but also providing in-time explanations.…”