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.
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.
Authors: Daniel J Gottlieb; Susan Redline; F Javier Nieto; Carol M Baldwin; Anne B Newman; Helaine E Resnick; Naresh M Punjabi Journal: Sleep Date: 2006-08 Impact factor: 5.849
Authors: Alexandros N Vgontzas; Duanping Liao; Edward O Bixler; George P Chrousos; Antonio Vela-Bueno Journal: Sleep Date: 2009-04 Impact factor: 5.849
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