shows a schedule for who does not sleep around noon, watches TV, and takes a bath
every day, but only goes out every four days.
We also tested the performance of our generator on PC with an Intel
(R) core
(TM) i78550U @1.80-GHz CPU. The generator ran 100 times in 3.987 s.
Additional generated schedules are available via this website [17].
5 Using Generated Activity Schedules for Simulation
Smart house are often equipped with passive infrared (PIR) sensors. When residents are
in the detection range of one, it turns on, otherwise, it remains off. Each PIR sensor has
a unique ID number which can be recorded when the sensor turns on or off. By placing
several PIR sensors in the house and analyzing their records, a resident’s movement
trajectories can be acquired, which can be used to determine whether they contain
wandering travel patterns associated with dementia [18].
In the simulation, the PIR sensor records were generated from simulated real-time
location data. We built a generator that could convert an activity schedule, a sample of
indoor spatial data, and several samples of travel pattern data into a sample of real-time
location data. We developed an interface to convert the real-time location data into
virtual PIR sensor records, which can be used to optimize the placement of the PIR
sensors in a smart house.
Examples of the performance of the real-time location data generator and the
interface are shown in figures and tables. Figure 2 shows a sample of spatial data.
Table 2 shows part of an activity schedule. The travel pattern data are shown in Fig. 4.
The above data are input into the generator to produce the real-time location data.
Figure 4 also shows the positions of the five PIR sensors located in the virtual house.
Their coordinates are [100, 0], [300, −150], [500, −200], [550, 50] and [850, 0]. The
interface converts the real-time location data into the records of PIR sensors, which is
shown in Table 3.
Fig. 2. Layout of input indoor spatial data.
Automatic Daily Activity Schedule Planning for Simulating Smart House
179
every day, but only goes out every four days.
We also tested the performance of our generator on PC with an Intel
(R) core
(TM) i78550U @1.80-GHz CPU. The generator ran 100 times in 3.987 s.
Additional generated schedules are available via this website [17].
5 Using Generated Activity Schedules for Simulation
Smart house are often equipped with passive infrared (PIR) sensors. When residents are
in the detection range of one, it turns on, otherwise, it remains off. Each PIR sensor has
a unique ID number which can be recorded when the sensor turns on or off. By placing
several PIR sensors in the house and analyzing their records, a resident’s movement
trajectories can be acquired, which can be used to determine whether they contain
wandering travel patterns associated with dementia [18].
In the simulation, the PIR sensor records were generated from simulated real-time
location data. We built a generator that could convert an activity schedule, a sample of
indoor spatial data, and several samples of travel pattern data into a sample of real-time
location data. We developed an interface to convert the real-time location data into
virtual PIR sensor records, which can be used to optimize the placement of the PIR
sensors in a smart house.
Examples of the performance of the real-time location data generator and the
interface are shown in figures and tables. Figure 2 shows a sample of spatial data.
Table 2 shows part of an activity schedule. The travel pattern data are shown in Fig. 4.
The above data are input into the generator to produce the real-time location data.
Figure 4 also shows the positions of the five PIR sensors located in the virtual house.
Their coordinates are [100, 0], [300, −150], [500, −200], [550, 50] and [850, 0]. The
interface converts the real-time location data into the records of PIR sensors, which is
shown in Table 3.
Fig. 2. Layout of input indoor spatial data.
Automatic Daily Activity Schedule Planning for Simulating Smart House
179