Alshammari et al. [9] replicated and modified schedules originally designed by
humans. The methods of modification include combining two samples of original
schedules and changing the start and end time of activities. The activity schedules
correspond to virtual binary sensor records, thus, a large number of records are generated simultaneously. This method is simple, but generated schedules may have high
similarity.
Mshali et al. [15] generated long-term activity schedules using a Markov model,
and five transition matrices associated to different periods of a day were designed. The
authors also proposed an adaptive and context-aware algorithm for monitoring the daily
activities of elderly and dependent persons, and generated schedules were used to test
the algorithm in simulations.
Lee et al. [16] generated activity schedules with a motivation-driven method.
A motivation value (MV) represents the desire of a virtual agent to perform a class of
activities with the agent performing an activity when its corresponding MV reaches its
threshold. Motivations are classified by levels; if two MVs reach their thresholds at the
same time, the agent will perform the activity that corresponds to the higher-level
motivation. This method has sufficient potential for improvement if the mechanism of
evolution of the MVs is designed carefully.
3 Activity Schedule Generation
3.1 Problem Statement
To build our activity schedule generator module, we need to improve upon the methods
mentioned in Sect. 2 by addressing the following issues. 1) The list of activities that
can be performed by a virtual resident is determined by the layout of simulated house,
e.g., the resident can only watch TV if a TV is in the house. The above methods are for
a determined layout with a fixed activity list. As our simulator contributes to provide
diverse simulation scenarios by producing diverse layouts, we need a method that can
process dynamic activity lists. 2) The above methods generate schedules whose
timelines include unspecified times between the end time of an activity and the start
time of the next one. Where the resident has been and what he/she has done during the
unspecified time are undetermined, thus, generated sensors records did not cover entire
days. 3) Most of the above methods generated schedules for one day or less, but longterm activity schedules are required for our simulation.
We developed a motivation-driven method on the basis of that presented in the
reviewed study [16] to build our activity schedule generator. An MV represent a
resident’s desire to perform an activity sequence (AS). While performing the activity is
dependent on its MV reaching its threshold in [16], in our method, the MVs are used to
determine the probability distribution (P) of sampling the next AS. The evolution of the
MVs is adaptive to the input indoor spatial data and resident’s profile. The input data
represent a layout that determines what AS can be performed, thus, this adaptive
evolution mechanism addresses issue 1). The profile represents a resident’s tendencies
to activities, which is quantified by durations (D), periods (T) and frequencies (f) of an
AS. We need to design an evolution mechanism and initialize the MVs carefully to
Automatic Daily Activity Schedule Planning for Simulating Smart House
173
humans. The methods of modification include combining two samples of original
schedules and changing the start and end time of activities. The activity schedules
correspond to virtual binary sensor records, thus, a large number of records are generated simultaneously. This method is simple, but generated schedules may have high
similarity.
Mshali et al. [15] generated long-term activity schedules using a Markov model,
and five transition matrices associated to different periods of a day were designed. The
authors also proposed an adaptive and context-aware algorithm for monitoring the daily
activities of elderly and dependent persons, and generated schedules were used to test
the algorithm in simulations.
Lee et al. [16] generated activity schedules with a motivation-driven method.
A motivation value (MV) represents the desire of a virtual agent to perform a class of
activities with the agent performing an activity when its corresponding MV reaches its
threshold. Motivations are classified by levels; if two MVs reach their thresholds at the
same time, the agent will perform the activity that corresponds to the higher-level
motivation. This method has sufficient potential for improvement if the mechanism of
evolution of the MVs is designed carefully.
3 Activity Schedule Generation
3.1 Problem Statement
To build our activity schedule generator module, we need to improve upon the methods
mentioned in Sect. 2 by addressing the following issues. 1) The list of activities that
can be performed by a virtual resident is determined by the layout of simulated house,
e.g., the resident can only watch TV if a TV is in the house. The above methods are for
a determined layout with a fixed activity list. As our simulator contributes to provide
diverse simulation scenarios by producing diverse layouts, we need a method that can
process dynamic activity lists. 2) The above methods generate schedules whose
timelines include unspecified times between the end time of an activity and the start
time of the next one. Where the resident has been and what he/she has done during the
unspecified time are undetermined, thus, generated sensors records did not cover entire
days. 3) Most of the above methods generated schedules for one day or less, but longterm activity schedules are required for our simulation.
We developed a motivation-driven method on the basis of that presented in the
reviewed study [16] to build our activity schedule generator. An MV represent a
resident’s desire to perform an activity sequence (AS). While performing the activity is
dependent on its MV reaching its threshold in [16], in our method, the MVs are used to
determine the probability distribution (P) of sampling the next AS. The evolution of the
MVs is adaptive to the input indoor spatial data and resident’s profile. The input data
represent a layout that determines what AS can be performed, thus, this adaptive
evolution mechanism addresses issue 1). The profile represents a resident’s tendencies
to activities, which is quantified by durations (D), periods (T) and frequencies (f) of an
AS. We need to design an evolution mechanism and initialize the MVs carefully to
Automatic Daily Activity Schedule Planning for Simulating Smart House
173