NDLI: Activity modeling under uncertainty by trace of objects in smart homes

Content Provider	Springer Nature Link
Author	Amirjavid, Farzad Bouzouane, Abdenour Bouchard, Bruno
Copyright Year	2012
Abstract	A typical resident of a smart home can be an Alzheimer patient that forgets sometimes to complete the activities that he begins. The key point to assist the smart home resident is to model the activities and discover correct realization patterns of activities. To accomplish this task, we apply sensors to provide primary data about realization patterns of actions, operations, plans, goals and generally any objective that the smart home resident may desire to do. In the consequence, by applying fuzzy clustering techniques, we are able to mine sensor data to retrieve the realization patterns of activities, and so the prediction patterns of intentions are recognizable. Comparing the realization patterns with prediction patterns of activities, we would be able to predict the intention of the resident about the activity that the resident considers to realize. In this way, we would be able to provide hypotheses about the resident goals and his possible goal achievement’s defects. Spatiotemporal aspects of daily activities such as movement of objects are surveyed to discover the patterns of activities realized by the smart homes residents. In this research, uncertainty is considered as a property of activity recognition.
Starting Page	159
Ending Page	167
Page Count	9
File Format	PDF
ISSN	18685137
Journal	Journal of Ambient Intelligence and Humanized Computing
Volume Number	5
Issue Number	1
e-ISSN	18685145
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2012-08-29
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ambient environment Fuzzy logic Fuzzy subtractive clustering Activity recognition Temporal data mining Computational Intelligence Artificial Intelligence (incl. Robotics) Robotics and Automation User Interfaces and Human Computer Interaction
Content Type	Text
Resource Type	Article
Subject	Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A review of smart homes in healthcare

Data driven modeling of the simultaneous activities in ambient environments : Case study: automatic data driven anomaly recognition for interruptive activities in smart home

Recognition of fuzzy contexts from temporal data under uncertainty case study: Activity recognition in smart homes

The behavioral profiling based on times series forecasting for smart homes assistance

Providing relevant information in an ambient services using service requester’s “logical area”

Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments

CRAFFT: an activity prediction model based on Bayesian networks

A fuzzy ambient intelligent agents approach for monitoring disease progression of dementia patients

Biometrics in ambient intelligence

Activity modeling under uncertainty by trace of objects in smart homes

Similar Documents

A review of smart homes in healthcare

Data driven modeling of the simultaneous activities in ambient environments : Case study: automatic data driven anomaly recognition for interruptive activities in smart home

Recognition of fuzzy contexts from temporal data under uncertainty case study: Activity recognition in smart homes

The behavioral profiling based on times series forecasting for smart homes assistance

Providing relevant information in an ambient services using service requester’s “logical area”

Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments

CRAFFT: an activity prediction model based on Bayesian networks

A fuzzy ambient intelligent agents approach for monitoring disease progression of dementia patients

Biometrics in ambient intelligence

Activity modeling under uncertainty by trace of objects in smart homes