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CRAFFT: An Activity Prediction Model based on Bayesian Networks.

Ehsan Nazerfard¹, Diane J Cook²

¹School of Electrical Engineering and Computer Science, EME 206 Spokane Street, Washington State University, Pullman, WA USA, Tel.: +1 425 518-7974.

Journal of Ambient Intelligence and Humanized Computing

|May 5, 2015

Summary

This summary is machine-generated.

This study introduces a new Bayesian network model for predicting future activities in smart homes, enhancing health monitoring for independent living. The model accurately forecasts both activity type and start time using real-world data.

Keywords:

Activity Prediction Activity Recognition Bayesian Networks Clustering Prompting Smart Environments

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Area of Science:

Pervasive computing
Machine learning
Data mining
Artificial intelligence in healthcare

Background:

Smart home technology offers potential for independent living assistance.
Activity recognition and discovery are well-researched, but activity prediction is less explored.
Predicting user activities is crucial for proactive smart home interventions.

Purpose of the Study:

To develop an accurate activity prediction model for smart home residents.
To enhance health monitoring and assistance systems for independent living.
To improve context-aware prompting for important activities.

Main Methods:

Proposed a Bayesian network model for activity prediction.
Introduced a novel two-step inference process for predicting activity features and labels.
Developed an approach to predict activity start times using continuous normal distribution and outlier detection.

Main Results:

The proposed models demonstrated effectiveness in predicting future activities.
Validated the models using real-world data from physical smart environments.
Achieved accurate prediction of both activity type and start time.

Conclusions:

Activity prediction is a vital component for advanced smart home systems.
The developed Bayesian network model offers a robust solution for predicting resident activities.
This research contributes to the development of more intelligent and supportive smart home environments.