CRAFFT: An Activity Prediction Model based on Bayesian Networks

Ehsan Nazerfard; Diane J Cook

doi:10.1007/s12652-014-0219-x

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CRAFFT: An Activity Prediction Model based on Bayesian Networks

Journal article

Open access

Peer reviewed

CRAFFT: An Activity Prediction Model based on Bayesian Networks

Ehsan Nazerfard and Diane J Cook

Journal of ambient intelligence and humanized computing, Vol.6(2), pp.193-205

04/01/2015

DOI: https://doi.org/10.1007/s12652-014-0219-x

Handle:

https://hdl.handle.net/2376/112018

PMCID: PMC4414055

PMID: 25937847

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https://doi.org/10.1007/s12652-014-0219-xView

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Abstract

Bayesian Networks

Activity Recognition

Activity Prediction

Prompting

Clustering

Smart Environments

Recent advances in the areas of pervasive computing, data mining, and machine learning offer unique opportunities to provide health monitoring and assistance for individuals facing difficulties to live independently in their homes. Several components have to work together to provide health monitoring for smart home residents including, but not limited to, activity recognition, activity discovery, activity prediction, and prompting system. Compared to the significant research done to discover and recognize activities, less attention has been given to predict the future activities that the resident is likely to perform. Activity prediction components can play a major role in design of a smart home. For instance, by taking advantage of an activity prediction module, a smart home can learn context-aware rules to prompt individuals to initiate important activities. In this paper, we propose an activity prediction model using Bayesian networks together with a novel two-step inference process to predict both the next activity features and the next activity label. We also propose an approach to predict the start time of the next activity which is based on modeling the relative start time of the predicted activity using the continuous normal distribution and outlier detection. To validate our proposed models, we used real data collected from physical smart environments.

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Title: CRAFFT: An Activity Prediction Model based on Bayesian Networks
Creators: Ehsan Nazerfard - School of Electrical Engineering and Computer Science, EME 206 Spokane Street, Washington State University, Pullman, WA USA, Tel.: +1 425 518-7974
Diane J Cook - School of Electrical Engineering and Computer Science, EME 121 Spokane Street, Washington State University, Pullman, WA USA, Tel.: +1 509 335-4985
Publication Details: Journal of ambient intelligence and humanized computing, Vol.6(2), pp.193-205
Academic Unit: Electrical Engineering and Computer Science, School of
Identifiers: 99900547647001842
Language: English
Resource Type: Journal article