RealTime Compliant Stream Processing Agents for Physical Rehabilitation Digital rehabilitation is a novel concept that integrates stateoftheart technologies for motion sensing and monitoring, with personalized patientcentric 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 realtime constraints (e.g., scheduling, communication, and negotiation), (ii) plugandplay sensors must seamlessly manage data and functional heterogeneity, and finally (iii) multidevice coordination must enable flexible and scalable sensor interactions. Beyond traditional topdown and besteffort solutions, unsuitable for safetycritical scenarios, we propose a novel approach for decentralized realtime compliant semantic agents. In particular, these agents can autonomously coordinate with each other, schedule sensing and data delivery tasks (complying with strict realtime constraints), while relying on ontologybased 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 lowerlimb digital rehabilitation scenarios, simulated on the MAXIMGPRT environment for realtime compliance. Finally, we conduct an extensive evaluation of the implementation of the stream processing multiagent architecture, which relies on existing RDF stream processing engines.

A MultiSensors Wearable System for Remote Assessment of Physiotherapy Exercises during ACL Rehabilitation In this paper, the challenges associated with the design of a novel multisensor wearable system for the objective assessment of exercises during lowerlimbs rehabilitation are described. The overall system architecture is defined, and finally both the implemented hardware and software platforms are illustrated in detail. Multiple sensing technologies are adopted including motion data, electromyography measurements, and muscle electrostimulation. The software stack provides guidance to the users throughout the rehabilitation therapy sessions, and allows clinicians to access the data collected remotely in realtime thus supporting their clinical evaluation. Finally, preliminary results of the comparison between the knee joint angle estimated by the developed system against a goldstandard inertialbased system are provided showing promising results for future validation.