Affiliations: School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA
Correspondence:
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Corresponding author: Jennifer A. Williams, School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA. E-mail:jen_williams@wsu.edu
Abstract: BACKGROUND: The goal of this research is to use smart home
technology to assist people who are recovering from injuries or coping with
disabilities to live independently. OBJECTIVE: We introduce an algorithm to model and forecast wake
and sleep behaviors that are exhibited by the participant. Furthermore, we
propose that sleep behavior is impacted by and can be modeled from wake
behavior, and vice versa. METHODS: This paper describes the Behavior Forecasting (BF)
algorithm. BF consists of 1) defining numeric values that reflect sleep and
wake behavior, 2) forecasting wake and sleep values from past behavior, 3)
analyzing the effect of wake behavior on sleep and vice versa, and 4)
improving prediction performance by using both wake and sleep scores. RESULTS: The BF method was evaluated with data collected from 20
smart homes. We found that regardless of the forecasting method utilized,
wake behavior and sleep behavior can be modeled with a minimum accuracy of
84%. Additionally, normalizing the wake and sleep scores drastically
improves the accuracy to 99%. CONCLUSIONS: The results show that we can effectively model wake
and sleep behaviors in a smart environment. Furthermore, wake behaviors can
be predicted from sleep behaviors and vice versa.
