A Mobile Clinical DSS based on Augmented Reality and Deep Learning for the home cares of patients afflicted by bedsores

Abstract A bedsore, also known as pressure sore, pressure ulcer or decubitus ulcer, is the result of constant pressure on skin occurring in bedridden patients and paraplegics continuously sitting in chair. All patients who are immobile for a long time due to any cause are likely to get bedsores. Effective and efficient management of processes related to the treatment of bedsores is an important issue for healthcare organizations as it heavily affects the quality of life of patients and the costs for such organizations. Therefore organizations need and look for more and more to provide their field workforce with smart mobile tools able to support such processes. In such a context, this paper proposes a mobile app implementing a Clinical Decision Support System (CDSS) to help field operators to measure the bedsore, classify its status, trace its evolution along the timeline and making correct decisions about the course of actions to effectively treat it. The mobile app is mostly based on Augmented Reality supported by Deep Learning, thus it requires an adequate system architecture to be effectively deployed, adopted and used. From the conceptual viewpoint, the defined CDSS model lays on three important considerations: providing automatic support to classify the status of a bedsore does not do all the work but help operators to improve the quality of their decisions, augmented reality allows to build a situated environment for decision-making supporting the operators' cognitive processes, operators should use only one tool to execute all their tasks in order to be more focused on the real problem which is to improve the quality of life of their patients

adaptive goal selection for improving situation awareness the fleet management case study

Abstract: Lack of Situation Awareness (SA) when dealing with complex dynamic environments is recognized as one of the main causes of human errors, leading to serious and critical incidents. One of the main issues is the attentional tunneling manifested, for instance, by human operators (in Decision Support Systems) focusing their attention on a single goal and loosing the awareness of the global picture of the monitored environments. A further issue is represented by stimuli, coming from such environments, which may divert the attention of the operators from the most important aspects and cause erroneous decisions. Thus, the need to define systems helping human operators to improve SA with respect to the two aforementioned drawbacks emerges. These systems should help operators in focusing their attention on active goals and, when really needed, switching it on new goals, in a sort of continuous adaptation. In this work an adaptive goal selection approach exploiting both goal-driven and data-driven information processing is proposed. The approach has been defined and injected in an existing multi-agent framework for Situation Awareness and applied in a Fleet Management System. The approach has been evaluated by means of the SAGAT methodology

ITSEGO: An Ontology for Game-based Intelligent Tutoring Systems

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An AmI-Based Software Architecture Enabling Evolutionary Computation in Blended Commerce: The Shopping Plan Application

This work describes an approach to synergistically exploit ambient intelligence technologies, mobile devices, and evolutionary computation in order to support blended commerce or ubiquitous commerce scenarios. The work proposes a software architecture consisting of three main components: linked data for e-commerce, cloud-based services, and mobile apps. The three components implement a scenario where a shopping mall is presented as an intelligent environment in which customers use NFC capabilities of their smartphones in order to handle e-coupons produced, suggested, and consumed by the abovesaid environment. The main function of the intelligent environment is to help customers define shopping plans, which minimize the overall shopping cost by looking for best prices, discounts, and coupons. The paper proposes a genetic algorithm to find suboptimal solutions for the shopping plan problem in a highly dynamic context, where the final cost of a product for an individual customer is dependent on his previous purchases. In particular, the work provides details on the Shopping Plan software prototype and some experimentation results showing the overall performance of the genetic algorithm

Enabling technologies for future learning scenarios: the semantic grid for human learning

In this paper, starting from the limitations and constrains of traditional human learning approaches, we outline new suitable approaches to education and training in future knowledge based society. In our vision, learning and teaching are no longer standalone activities but complex, conversational and experiential-based processes implying collaboration, direct experience, mutual trust and shared interests. We identify characteristics of the environments suitable for these processes, and we compare different enabling technology infrastructures in order to justify why the Semantic Grid for Human Learning, that is a particular enhanced instance of the traditional Semantic Grid, is the most appropriate infrastructure to build our vision on. Finally, we present a realistic learning scenario as a case study, proving the effectiveness of our innovative learning approachesforfuture Education and Training

Building Ontology-Driven Tutoring Models for Intelligent Tutoring Systems Using Data Mining

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Supporting Seamless Learning with Semantic Technologies and Situation Awareness

The goal of this work is to propose and motivate the usage of Linked Data (realized by means of the Semantic Web Stack) and Situation Awareness techniques in order to support Seamless Learning scenarios. In particular, Linked Data and Semantic Web technologies and methodologies are considered very useful to model and support the continuity of the seamless experience across heterogeneous (in quality, time and space) learning activities. Moreover, Situation Awareness and, in particular, Situation Recognition techniques can be exploited to sustain enhanced forms of ubiquitous access to learning resources and services which enable the improvement of the learning environment by using context-specific elements

An Agent-based Framework for Indoor Navigation in Blended Shopping

In this paper we propose an advanced computational intelligence solution for an an Agent-based framework for implementing blended shopping scenarios. The proposed solution meets needs recently arose in Ubiquitous Computing and Pervasive Computing as well. The work focuses on the definition of an Indoor Navigation System that guides shoppers in a shopping mall, towards the shops providing the most suitable offerings for them, in a given time window. Such system is based on a distributed algorithm that runs on a network of intelligent cells sensing both shoppers and offerings by means of the sensor devices deployed in the mall

Building Pedagogical Models by Formal Concept Analysis

The Pedagogical Model is one of the main components of an Intelligent Tutoring System. It is exploited to select a suitable action (e.g., feedback, hint) that the intelligent tutor provides to the learner in order to react to her interaction with the system. Such selection depends on the implemented pedagogical strategy and, typically, takes care of several aspects such as correctness and delay of the learner’s response, learner’s profile, context and so on. The main idea of this paper is to exploit Formal Concept Analysis to automatically learn pedagogical models from data representing human tutoring behaviours. The paper describes the proposed approach by applying it to an early case study