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Sensors 2012, 12(1), 1072-1099; doi:10.3390/s120101072

Activity Inference for Ambient Intelligence Through Handling Artifacts in a Healthcare Environment

1
Facultad de Ingeniería, Universidad Autónoma de Baja California, Km 103 Carretera Tijuana-Ensenada, Ensenada, B.C. 022860, México
2
Facultad de Ciencias, Universidad Autónoma de Baja California, Km 103 Carretera Tijuana-Ensenada, Ensenada, B.C. 022860, México
3
Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí, Av. Manuel Nava No. 8, Zona Universitaria, San Luis Potosí, S.L.P. 78290, México
4
Facultad de Ingeniería, Universidad Autónoma de Baja California, Mexicali, B.C. 21280, México
*
Author to whom correspondence should be addressed.
Received: 29 November 2011 / Revised: 10 January 2012 / Accepted: 16 January 2012 / Published: 20 January 2012
(This article belongs to the Special Issue Smart Spaces and Ubiquitous Solutions)
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Abstract

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user. View Full-Text
Keywords: activity inference; activity representation; artifact recognition; ambient intelligence activity inference; activity representation; artifact recognition; ambient intelligence
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Martínez-Pérez, F.E.; González-Fraga, J.Á.; Cuevas-Tello, J.C.; Rodríguez, M.D. Activity Inference for Ambient Intelligence Through Handling Artifacts in a Healthcare Environment. Sensors 2012, 12, 1072-1099.

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