Non-monotonic Reasoning Supporting Wireless Sensor Networks for Intelligent Monitoring: The SINDI System

Authors:
Alessandra Mileo;Davide Merico;Roberto Bisiani
Affiliations:
Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan I---20126;Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan I---20126;Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milan I---20126
Venue:
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Year:
2009

Citing 7
Cited 2

Extending and implementing the stable model semantics

Artificial Intelligence
The DLV system for knowledge representation and reasoning

ACM Transactions on Computational Logic (TOCL)
Context-Aware Computing Applications

WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
Conflict-driven answer set solving

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
GrinGo: a new grounder for answer set programming

LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
A Logical Approach to Home Healthcare with Intelligent Sensor-Network Support

The Computer Journal
A survey on sensor networks

IEEE Communications Magazine

Answer set programming

A 25-year perspective on logic programming
Reactive answer set programming

LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

In recent years there has been growing interest in solutions for the delivery of clinical care for the elderly, due to the large increase in aging population. Monitoring a patient in his home environment is necessary to ensure continuity of care in home settings, but this activity must not be too invasive and a burden for clinicians. We prototyped a system called SINDI (Secure and INDependent lIving), focused on i) collecting data about the person and the environment through Wireless Sensor Networks (WSN), and ii) reasoning about these data both to contextualize them and to support clinicians in understanding patients' well being as well as in predicting possible evolutions of their health.