Roberto Bisiani

Support for context-aware monitoring in home healthcare

This paper tackles the problem of supporting independent living and well-being for people that live in their homes and have no critical chronic condition. The paper assumes the presence of a monitoring system equipped with a pervasive sensor network and a non-monotonic reasoning engine. The rich set of sensors that can be used for monitoring in home environments and their sheer number make it quite complex to provide a correct interpretation of collected data for a particular patient. For this reason, we introduce a logic-based context model for situation assessment combined with high level declarative feedback policy specification, and we use logic programming techniques to reason about different pieces of knowledge for prevention.

A Logical Approach to Home Healthcare with Intelligent Sensor-Network Support

This paper describes an intelligent home healthcare system characterized by a wireless sensor network (WSN) and a reasoning component. The aim of the system is to allow constant and unobtrusive monitoring of a patient in order to enhance autonomy and increase quality of life. Data collected by the sensor network are used to support a reasoning component, which is based on answer set programming (ASP), in performing three main reasoning tasks: (i) continuous contextualization of the physical, mental and social state of a patient, (ii) prediction of possibly risky situations and (iii) identification of plausible causes for the worsening of a patients health. Starting from different data sources (sensor data, test results, inference results) the reasoning component applies expressive logic rules aimed at correct interpretation of incomplete or inconsistent contextual information, and evaluates correlation rules expressed by clinicians. The expressive power of ASP allows efficient enough reasoning to support prevention, while declarativity simplifies rule-specification and allows automatic encoding of knowledge. Preliminary evaluations show that the combination of an ASP-based reasoning component and a WSN is a good solution for creating a home-based healthcare system.

Fostering social interaction of home-bound elderly people: the easyreach system

This paper presents the EasyReach system, a tool that aims at getting the elderly and pre-digital divide population closer to new technologies by creating a simplified social enviroment that facilitates interaction, trying to allow them to (i) easily keep in contact with friends and relatives, (ii) share their lifetime expertise, and (iii) avoid isolation. The EasyReach tool creates for the elderly a special social TV channel accessed by means of their own TV set and a specialized remote control endowed with gesture recognition, video and audio capture capabilities. A hidden personal assistant reasons on user preferences in the background allowing better focalization on his/her social interests.

Knowledge-based multi-criteria optimization to support indoor positioning

Indoor position estimation constitutes a central task in home-based assisted living environments. Such environments often rely on a heterogeneous collection of low-cost sensors whose diversity and lack of precision has to be compensated by advanced techniques for localization and tracking. Although there are well established quantitative methods in robotics and neighboring fields for addressing these problems, they lack advanced knowledge representation and reasoning capacities. Such capabilities are not only useful in dealing with heterogeneous and incomplete information but moreover they allow for a better inclusion of semantic information and more general homecare and patient-related knowledge. We address this problem and investigate how state-of-the-art localization and tracking methods can be combined with Answer Set Programming, as a popular knowledge representation and reasoning formalism. We report upon a case-study and provide a first experimental evaluation of knowledge-based position estimation both in a simulated as well as in a real setting.

An Agent-Based Data-Generation Tool for Situation-Aware Systems

This paper describes the design, implementation and evaluation of a simulator that generates plausible data for the debugging and testing of situation-aware monitoring systems. The use of simulated data is necessary because there is very little sensor-level behavioural-data available in the literature and, moreover, these data are very specific of the set-up used to collect them, i.e. the kind of sensors, the people behavior and age, the environment characteristics, and so on. Moreover, data collection requires a long time since the phenomena we are interested in last months or years. The use of a tool like Repast has substantially sped up the design and implementation phase while still achieving the necessary time performance and quality of the data generated.