A Personal Medical Digital Assistant Agent for Supporting Human Operators in Emergency Scenarios. In: Montagna S, Abreu PH, Giroux S, Schumacher MI, editors. Agents and Multi-Agent Systems for Health Care. Cham: Springer International Publishing; 2017. pp. 59-75. [DOI: 10.1007/978-3-319-70887-4_4]

Study on the Applicability of Digital Twins for Home Remote Motor Rehabilitation

The COVID-19 pandemic created the need for telerehabilitation development, while Industry 4.0 brought the key technology. As motor therapy often requires the physical support of a patient's motion, combining robot-aided workouts with remote control is a promising solution. This may be realised with the use of the device's digital twin, so as to give it an immersive operation. This paper presents an extensive overview of this technology's applications within the fields of industry and health. It is followed by the in-depth analysis of needs in rehabilitation based on questionnaire research and bibliography review. As a result of these sections, the original concept of controlling a rehabilitation exoskeleton via its digital twin in the virtual reality is presented. The idea is assessed in terms of benefits and significant challenges regarding its application in real life. The presented aspects prove that it may be potentially used for manual remote kinesiotherapy, combined with the safety systems predicting potentially harmful situations. The concept is universally applicable to rehabilitation robots.

Linguistic and semantic layers for emergency plans

Plans for emergency response are complex collaborations in which actors take roles and responsibilities. They are generally long textual documents containing practical instructions, in natural language, for hazard responses. A more rigorous structured-text would be useful for a twofold audience. From one side, it can be useful for quickly understanding the plan and on the other side it can be used to improve the modelling phase and delivering an automatic emergency-support system. This paper proposes an approach, conceived for humans, for converting a free-form plan document into a structured version of the same document. The approach is based on a linguistic and semantic analysis that are strictly correlated and materialize in a metamodel. It contains the essential elements of an emergency plan, and it aids in interpreting the input document also reducing inconsistencies, redundancies, and ambiguities.

Augmenting BDI Agency with a Cognitive Service: Architecture and Validation in Healthcare Domain

Autonomous intelligent systems are starting to influence clinical practice, as ways to both readily exploit experts' knowledge when contextual conditions demand so, and harness the overwhelming amount of patient related data currently at clinicians' disposal. However, these two approaches are rarely synergistically exploited, and tend to be used without integration. In this paper, we follow recent efforts reported in the literature regarding integration of BDI agency with machine learning based Cognitive Services, by proposing an integration architecture, and by validating such architecture in the complex domain of trauma management. In particular, we show that augmentation of a BDI agent, endowed with predefined plans encoding experts' knowledge, with a Cognitive Service, trained on past observed data, can enhance trauma management by reducing over triage episodes.

Real-time tracking and documentation in trauma management

In trauma resuscitation, an accurate documentation is crucial to improve the quality of trauma care. Hospital emergency departments typically adopt handwritten paper records and flow sheets for acquiring data, which are often inaccurate. In this article, we describe TraumaTracker, a computer-based system for trauma tracking and documentation. Results demonstrate that completeness and accuracy of trauma documentation significantly improved using TraumaTracker, since it enables to add data and information that were not recorded in paper documentation - especially precise times and locations of events.