Detecting Room-to-Room Movement by Passive
Infrared Sensors in Home Environments
Kazuya Murao1 , Tsutomu Terada1 , Ai Yano2 , and Ryuichi Matsukura3
1

Graduate School of Engineering, Kobe University
1-1 Rokkodai, Nada, Kobe, Hyogo 657-8501, Japan{murao,tsutomu}@eedept.kobe-u.ac.jp
2
Fujitsu Laboratories Ltd.
4-1-1 Kamikodanaka, Nakahara, Kawasaki, Kanagawa 211-8588, Japanyano.ai@jp.fujitsu.com
3
Fujitsu Limited
4-1-1 Kamikodanaka, Nakahara, Kawasaki, Kanagawa 211-8588, Japanr.matsukura@jp.fujitsu.com

Abstract. We discuss in this paper the problem of simultaneous tracking, which exploits the synergy between location and movement to provide the information necessary for intelligent home appliance control.
Our goal is to carry out accurate movement estimation for multiple people in a home environment. We propose an approach that uses information gathered using only passive infrared sensors commonly found inlighting control systems. No special devices or video cameras are needed.
Moreover, it is not necessary to carry out data collection for training.
We evaluated our approach by conducting experiments in a real home
ﬁtted with sensors and we conﬁrmed that room-to-room movement was
detected with an accuracy of 0.82 for two inhabitants who moved freely
through the house.

1

Introduction

The Kyoto Protocol was initially adopted in Kyoto in 1997 and it was enteredinto force in 2005. Under the protocol, 37 countries commit themselves to a
reduction of greenhouse gases, and all the member countries give general commitments. In the negotiations, 37 countries including the US collectively agreed
to reduce their greenhouse gas emissions by 5.2% on average for the period
2008-2012. Reduction of green eﬀect gas emissions has been recognized as oneof the social goals of the world. Moreover, the 2011 earthquake oﬀ the Paciﬁccoast of Tohoku, also known as the 2011 Tohoku earthquake, or the Great East
Japan Earthquake, occurred on 11 March 2011. The earthquake triggered powerful tsunami waves that reached heights of up to 40.5 m (133 ft). The tsunami
struck and severely damaged nuclear power plants, which caused a number ofnuclear accidents. This shattered the myth that nuclear power is a safe energy
resource. As of April 2012, 53 of the 54 nuclear power plants had been disabled
or taken oﬄine, so Japan is facing some serious power shortages4 .
4http://www.gengikyo.jp/english/status/ChartOfPowerPlant.html

2

K. Murao, T. Terada, A. Yano, and R. MatsukuraHome Energy Management System (HEMS) and Building Energy Management System (BEMS) have attracted a great deal of attention due to the growinginterest in energy conservation around the world and the development of related
technologies. These systems manage home appliances and optimize power consumption by using sensor hardware. For example, an infrared sensor in a TV orlighting system detects the absence of humans and reduces power consumption
by turning these devices oﬀ when no one is there. However, simple management
such as “turn on when someone comes” and “turn oﬀ when no one is there” may
not be appropriate for other types of appliances, such as for air-conditioners andbroadcast satellite tuners. For example, if an air-conditioner is simply turnedon and oﬀ, the aironditioning will stop even when a user temporarily leaves
the room to go to the lavatory. This will lead to an increase in temperature,and the user will have to turn on the air-conditioner again, which will consume
excess power. A TV can be turned oﬀ, but a broadcast satellite tuner cannot
be turned oﬀ as easily since it takes longer to activate itself. Appliances thattake longer to activate should be managed before the user arrives. Smart management without taking human movement into consideration just deteriorates
quality of life (QoL). Intelligent management of appliances requires information
on human movement; this has been acquired through systems using cameras orradio frequency identiﬁcation (RFID) tags in conventional studies. Such systems,
however, do not consider privacy issues, and they involve additional tasks for theusers such as carrying around a device all day long. A system for general use
should not require users to carry around or operate any devices, and the systems
should not cause stress to the users when monitoring them.
We propose a method for detecting room-to-room movements of inhabitants
in a home environment by using infrared sensors. Our method is appealing because of the following points;– Users do not have to carry or operate any devices.
– The number of infrared sensors needed is relatively small (≈ 5 m2 /sensor).
– It is not necessary to collect training data.
We adopted the infrared sensor since it has certain advantages, as follows:– Infrared sensors are relatively cheap (≈ 3 USD).
– Infrared sensors are often already installed in homes to control lighting.
– Infrared sensors do not invade our privacy since only in/out information is
captured
Our goal in this study is to detect room-to-room movements of multiple inhabitants even when they moved at the same time. In addition, we investigated theimportance of sensor position by changing sensor combinations.
The paper is organized as follows. Section 2 introduces related work. Section 3
presents the assumed environment. We describe the proposed method in Section
4 and discuss its performance in Section 5. Finally, Section 6 concludes the paper.

Detecting Room-to-Room Movement by PIR Sensors in Home Environments

2

3

Related Work

Much research on human movement detection using sensors has been done. Many
methods use cameras, which can track humans and detect invaders, because theyobtain a great deal of information through images, video, and sound. Two of themain purposes of systems using cameras are to detect invaders from outside andto monitor workers in factories. Therefore, these systems create a feeling of being
kept under surveillance for inhabitants, which is not appropriate for home usefrom the viewpoint of privacy[1].
A human tracking system using RFID has been proposed[2]. Users with an
RFID reader are traced by touching RFID tags attached to objects as they move.
The advantage of the system is that transitions are correctly detected unless theuser forgets to touch the RFID reader. Although the system is easily applicable
to oﬃce environments where ID cards with RFID tags are used as keys to enterrooms, it is a diﬃcult constraint for home users to have to carry a card andtouch a reader whenever they move around.
A position estimation method using dead reckoning with inertia sensors has
also been proposed[3]. The system is complementarily used with a global positioning system (GPS) to obtain location information when the GPS does notwork well in the shade of buildings and trees or when underground. The system
is assumed to be used in a broad area, as errors accumulate as time elapses innarrow areas where the user performs fragmentary movements and pivot turns(changing direction on the spot). Moreover, users have to attach sensors on theﬁxed position whenever they move, which is unrealistic and unacceptable in ahome environment.
Position and transition detection systems using ubiquitous sensors without
any portable devices have also been proposed. One example is a door-level movement detection method using pressure sensors attached to central heating, ventilation, and air conditioning (HVAC) management systems[4]. Sensors attached
to an air ﬁlter detect room transitions throughout the house from the changes in
air pressure that occur when people pass through doorways and open and close
doors. This kind of system is easy to install and maintain since sensors are attached to only one ﬁlter. However, the detection accuracy is not more than 65%,
and much depends on the ﬂoor plan. As of 1997, approximately 66% of housesin the USA and Canada and 55% in Europe and Australia had HVAC, but thediﬀusion rate of HVAC in Japan and Korea is low since houses are small. It
may be that HVAC systems are not as eﬀective as distributing sensors for small
house.
Wren et al. proposed a method of detecting human movement that involves
placing a lot of infrared sensors on the ceiling[5]. Sensors are placed in a lattice
at intervals of a few meters, and events such as passing through rooms, changing
direction, and passing each other are detected by analyzing the order of outputs
from adjacent sensors. Hundreds of sensors are used, which leads to a high costof installation and maintenance. Wilson et al. proposed a method to detectmovements of multiple users by using infrared sensors and touch sensors on
doors[6]. This system is realistic since relatively few sensors are required: one

4

K. Murao, T. Terada, A. Yano, and R. Matsukura

infrared sensor per room and one touch sensor per door. However, ordinary
houses are not equipped with touch sensors, so these sensors would need to
be installed for the purpose of movement detection. We aim to utilize sensors
secondarily that are already installed in houses for other purposes. Therefore,
we use only infrared sensors which are already set for lighting control purposes.
In addition, conventional studies employ probability-based methods such as
those using Bayesian networks and particle ﬁlters[7]. These methods require alarge amount of sensor data and ground truth, which must be collected throughreal living activity. Models of other houses do not works since the ﬂoor plans
are diﬀerent. In order to collect data for training models, one subject has tolive alone for several days since it is hard to separate data if multiple subjects
are living in the same house[6]. Collecting data after the house is constructed
and before the owner moves in is possible, but it is not realistic to have a third
party live in the home for a few days before the owner of the house moves in.
Therefore, we aim to detect movements in houses without having to collect data
for training.

3

Environment

This section describes the environment from which we collected data for theexperiment and evaluation. Data collection took place in an experimental house
located in the campus of the Japan Advanced Institute of Science and Technology(JAIST). The ﬂoor plan of the house is shown in Figure 1. The house is a twostory detached house consisting of 12 rooms and spaces: an entrance, living &dining room, lavatory, bathroom (in Japan, a room only for bathing), Japanesestyle room, and WC (toilet) on the ﬁrst ﬂoor, and two Western-style rooms,
a bedroom, a spare room, and a WC on the second ﬂoor. In this paper, thelavatory and two WCs are treated as rooms. At least one sensor is installed ineach room except for the bathroom since infrared sensors are not water-proof
and are sensitive to heat. Apart from the rooms, sensors are placed in the hall,corridor, and stairway on the ﬁrst ﬂoor, and in the corridor and stairway on thesecond ﬂoor. A total of 25 sensors are installed in the house as shown in Figure2(a).
The infrared sensor used is model SN-MP13 manufactured by NTT Advanced
Technology Corporation (Figure 2(b)); Table 1 lists its speciﬁcations. All sensors
are connected to a server in the house via a sensor controller, as shown in Figure
3. The sensors capture infrared-rays every 500 msec and transmit the data to thesensor controller. “Found” is transmitted with time and sensor ID if a moving
object emitting infrared-rays, in other words, radiating heat, is in the range ofthe sensor; otherwise, “Not Found” is transmitted. Then, the sensor controller
carries the message from the sensor to the server if the new state (Found or
Not Found) is diﬀerent from the last state and continues for one second (twoconsective samples). Please note that states of “Found” and “Not Found” do
not indicate the existence of a human, but a human moving under the sensor,
and “Not Found” might be produced while an inhabitant stays under the sensor.

Detecting Room-to-Room Movement by PIR Sensors in Home Environments

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6WDLUV:HVWHUQVW\OHURRP

6SDUHURRP+DOO&RUULGRU/LYLQJDQGGLQLQJURRP+DOO%DWKURRP-DSDQHVH
VW\OHURRP:HVWHUQVW\OH
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Fig. 1. Floor plan of experimental house and sensor arrangement. The shaded areas
are closets and storage.

Infrared-ray receptor
USB connector(b) Infrared sensor.
(a) Snapshot of sensor on the ceiling.
Fig. 2. Passive IR sensor.

Table 1. Sensor speciﬁcations.
Item
Value
Detection range<5 m
Horizontal
38◦
Detection angle
Vertical22◦
Price5,900 JPY (tax included)

Therefore, we do not use “Not Found” information since “Not Found” does notalways mean that no one is there.
It might seem that tracking inhabitants is easy just by connecting sensors in
the order of reaction time, but it is diﬃcult for the following reasons. Infrared
sensors exhibit characteristics in which they do not always react when the inhabitant walks along the edge of the detection area (false negative), and dosometimes react when the inhabitant walks near the doorway of the room (false

6

K. Murao, T. Terada, A. Yano, and R. Matsukura
SerialLAN

Sensor controller

ServerCeiling
Passive IR sensor

SerialSensor controller

Time, sensorID, Value
10:20:02, Kitchen, Found
10:20:10, Kitchen, NotFound
10:20:11, Living room, Found・・・

InhabitantFig. 3. Sensor conﬁguration.

positive). Moreover, infrared sensors detect the existence of moving objects only
and are not able to identify the person. “Found” information is mixed in thetime-series when multiple inhabitants moved at the same time. For example, if asequence of sensor IDs (s1, s2, s3, s4, s5) is produced by inhabitant A, and (s6,s7, s8, s9) is produced by inhabitant B, the server receives (s1, s2, s6, s7, s3, s6,
s4, s9, s5). It is not clear who produced each ID. The distribution of sensors is
not dense, and the distance between two sensors is long, which makes it diﬃcult
to trace a particular inhabitant by connecting sensor IDs on the ﬂoor map.
We propose here a method to detect movements from room to room even if
multiple people move at the same time. For the sake of simplicity, we assume
the inhabitants move between two diﬀerent rooms, do not stop in the corridor,
and do not stroll without a destination after leaving a room.

4

Movement detection method

This section describes the method to detect transition information from sensor
sequences. In contrast to sensors that detect physical values such as hygrometers
and accelerometers, infrared sensors output binary values. Physical values change
according to the surrounding conditions, and changes in the environment or
malfunctions are detected by analyzing the change in values. The physical value
itself is preprocessed and then fed into a kind of classiﬁer, such as a support
vector machine (SVM)[8] and K-nearest neighbor (K-NN). On the contrary, areacting sensor ID changes according to human movement. Therefore, we focus
on the sensor ID only.
The procedure of the proposed method is described as follows.
1. Input ID sequence S.
2. Replace sensor IDs in S with a unique letter, i.e., sensor 1 is ‘a’, sensor 2 is‘b’.
3. Calculate accordance ratio A(i, t) of ST with all templates, where ST is asubset of S over a certain length of a window starting from time t = T , i isa sequential number of templates (i = 1, · · · , N ), and accordance ratio is aDetecting Room-to-Room Movement by PIR Sensors in Home Environments
Ground truth

Mother Living->W.C. 1FSon

ID sequence

WC(1F)->Western 2 Western 2->Lavatory

Living->W.C. 1F

7

WC(1F)->Western 2 Western 2->Lavatory

WC(2F)->Bedroom

WC(2F)->Bedroom

ABCDEDEFKFLMNMOFDPQEGHIHGHIHSTUTV

Living->WC(1F)[ABCDEF]

time

ABCDEDEFKFLMNMOFDPQEGHIHGHIHSTUTV

time

Living->WC(1F) has thehighest accordance ratio
and will be output.

WC(2F)->Bedroom[KLMNOPQ]
Templates WC(1F)->Western 2[FEGHI]
Western 2->Lavatory[IGHSTUV]
Living->Japanese[ABCXYZ]・・・

Accordance ratio

The accordance ratios
are calculated again
after corresponding IDs
are deleted.

Accordance ratio

Fig. 4. Movement detection.

proportion of a template included in ST . Iterate this calculation from t=0
to Tend , where Tend is the time of the last sample in S.
4. Search a pair (i′ , t′ ) with the highest accordance ratio from A as follows;(i′ , t′ ) = arg max A(i, t).
i,t

(1)

If multiple pairs meet the above condition, the one with the longest templateis selected. Moreover, if multiple pairs are still meeting the condition, theone with the earliest t is selected.
5. Quit if A(i′ , t′ ) < T hreshold, otherwise go to step 6.
6. Output the label of template #i′ , ﬁnd the matching letters in ST with template #i′ , and delete the matching letters from S. Then, go back to step3.
The detailed ﬂow in steps 3 and 4 is illustrated in Figure 4. Given the sequenceof IDs produced by the mother’s movements of living room to WC(1F), WC(1F)
to Western-style room 2, and Western-style room 2 to lavatory, and the son’s
movement WC(2F) to bedroom, the accordance ratio rises at the correspondingtime for each template. THe transition living to WC(1F) is output since its
accordance ratio is the highest, and the matching letters (A, B, C, D, E, F)
are deleted from the original ID sequence, and the accordance ratio is calculated
again. Deleting the corresponding letters will prevent the same output from being
detected twice. Even when multiple inhabitants move, IDs derived from diﬀerent
movements would not be deleted, and all the transitions can be extracted.
A detailed algorithm to calculate the accordance ratio leverages a dynamic
programming (DP) matching algorithm. By comparing m-length ST = (s1 , · · · , sm )and n-length template Y = (y1 , · · · , yn ), we can deﬁne a m × n matrix dij , where
dij = 0 if xi = yi , and dij = 1 if xi ̸= yi . Subsequently, path P = (p1 , · · · , pk ) is
found, which is a pair of indices of ST and Y . At this time, path P meets thefollowing three conditions.
– Boundary condition
p1 = (1, 1), pk = (m, n)8

K. Murao, T. Terada, A. Yano, and R. Matsukura– Seriality
pk = (a, b), pk−1 = (a′ , b′ ) ⇒ a − a′ ≤ 1 ∧ b − b′ ≤ 1– Monotony
pk = (a, b), pk−1 = (a′ , b′ ) ⇒ a − a′ ≥ 0 ∧ b − b′ ≥ 0
To ﬁnd the path with the lowest cost that meets the above conditions, thefollowing steps are applied.
1. Initialization:
Cost(0, 0) = 0
Cost(i, 0) = ∞ for i = 1, · · · , m
Cost(0, j) = ∞ for j = 1, · · · , n
2. Cost calculation:
Do for i = 1, 2, · · · , m
Do for j = 1, 2, · · · , n Cost(i − 1, j − 1))
Cost(i, j) = min Cost(i − 1, j
Cost(i, j − 1) + d(xi , yj )3. Lowest-cost path search:k = 0, i = m, j = n, pk = (i, j)
While i ̸= 1&&j ̸= 1:
if Cost(i − 1, j − 1) < Cost(i − 1, j)&&Cost(i − 1, j − 1) < Cost(i, j − 1)i − −, j − −, k + +, pk = (i, j)else if Cost(i − 1, j) < Cost(i, j − 1)i − −, k + +, pk = (i, j)else
j − −, k + +, pk = (i, j)
4. Output:
Return P
Basically, Y is a subset of ST when a transition is ﬁnished. Therefore, d(xi , yj )is added to the second equation in order not to allow Y to grow even though theletters do not match. The number of matching letters c is counted from P , andthe accordance ratio is obtained by c/n. For example, Y = (a, b, c, d, e), and a,c, d, and e are matched to ST in this order, and the accordance ratio becomes
0.8.

5

Evaluation

5.1

Data collection

The data used for the evaluation were collected from the scenarios from one andtwo subjects in the two-story house introduced in Section 3. In the one-subject
scenario, the subject moved along a route that includes all possible transitions

Detecting Room-to-Room Movement by PIR Sensors in Home Environments

9among 11 rooms excluding the bathroom. The transition from/to the living room
was done in two ways since it has two routes: direct and via the kitchen. In the
two-subject scenario, the subjects freely moved around the house for 10 minutes.
Their transitions are listed in Table 2, which includes passing each other andstaying together in the room.
Templates for training are made automatically by outputting all sensor IDs
on a route with a given origin and destination. On routes that include a roomwith more than two sensors, all the combinations are included for the room.
For an example of a transition from the living room to the bedroom, since theliving room has four sensors that react in 24 − 1 = 15 patterns and the bedroom
has two sensors that react in 22 − 1 = 3 patterns, the number of templates
for the transition is 15 × 3 = 45. The size of window W is set to 25 seconds.
This is because one transition takes up to 25 seconds in the data collection. We
discuss the performance of our proposed method based on detection accuracy.
The accuracy is measured on the basis of:– If either the origin or destination of a detected transition is false, it is false
positive.
– If both the origin and destination of a detected transition are correct, and• if the transition is detected in 25 seconds after the subject leaved theorigin room, it is true positive.
• Otherwise, it is false positive.
The departure and arrival times of the transition are recorded.
5.2

Result

The recall, precision, and F-measure of the results of movement detection for one
and two subjects are plotted in Figures 5 and 6. Recall, precision, and F-measuretrue positive# of true positive
are measured by Recall = ## ofof test sample , P recision = # of detected sample ,recisionand F − measure = 2·Recall·P
Recall+P recision . The results indicate that recall increases
as the threshold decreases since the number of outputs increases, while precision
decreases also since uncertain results are output. On the contrary, precision increases as threshold increases since only reliable results are output, while recall
decreases since the number of false negatives increases. The F-measure results
show that the highest F-measures for one subject, 0.953, and for two subjects,
0.821, are obtained when the threshold is set to 0.8. The reason incorrect detections occur is that the sensor in the living room which is nearest to the kitchen
incorrectly reacts when the subject goes to the kitchen. This is because the sensor is so close to the kitchen that the movement is detected as “to living room”.
In addition, some sensors did not work well because their position and direction
were not very good. Therefore, we investigated which sensors had an importantrole.
Firstly, the F-measures for the cases when all 25 sensors were used and when
one of the sensors in the non-room places was not used are shown in Figure 7.
The bar chart of “w/o hall”, for example, indicates the result when 24 sensors

10

K. Murao, T. Terada, A. Yano, and R. Matsukura
Table 2. Scenario for two subjects

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

RecallPrecision
F-measure0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1

Threshold of accordance ratio

Fig. 5. Result of one subject.

Evaluation value

Evaluation value

Departure time
OriginArrival time
Destination
15:46:54
Western-style room 2 15:47:05
Bedroom
15:47:54
Bedroom
15:48:09
WC(2F)
15:49:54
WC(2F)
15:50:13
WC(1F)
15:50:54
WC(1F)
15:51:04
Lavatory
15:51:34
Lavatory
15:51:46 Japanese-style room
15:52:14
Japanese-style room 15:52:31
Entrance
Subject A
15:52:54
Entrance
15:53:07
Kitchen
15:53:34
Kitchen
15:53:54 Western-style room 2
15:54:29
Western-style room 2 15:54:36 Western-style room 1
15:54:54
Western-style room 1 15:55:05
Spare room
15:55:29
Spare room
15:55:52
Living room
15:56:29
Living room
15:56:51
Bedroom
15:47:54
Living room
15:48:05
Lavatory
15:48:54
Lavatory
15:49:15
Bedroom
15:50:24
Bedroom
15:50:34
Spare room
15:51:24
Spare room
15:51:36 Western-style room 2
Subject B
15:52:24
Western-style room 2 15:52:34
WC(2F)
15:53:24
WC(2F)
15:53:36 Western-style room 2
15:54:24
Western-style room 2 15:54:39
Living room
15:55:24
Living room
15:55:31
Kitchen
15:56:24
Kitchen
15:56:36 Japanese-style room

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

RecallPrecision
F-measure0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1

Threshold of accordance ratio

Fig. 6. Result of two subjects.

were used, excluding the sensor in the hall on the ﬁrst ﬂoor. The use of all sensors
resulted in an F-measure of 0.95, while the F-measure is dropped to 0.92 when asensor in the hall on the ﬁrst ﬂoor was excluded. This is the lowest performanceof the ﬁve places. The same trend was conﬁrmed for two subjects. This meansthat the sensor in the hall on the ﬁrst ﬂoor plays an important role. This isbecause this sensor is involved in many patterns of transitions.
On the contrary, the F-measure rose up to 0.98 when the sensor in the hall on
the second ﬂoor for one subject. This is because the sensor setting was not adequate, and it seemed to be a malfunctioning sensor. The results of the stairways
indicate that the performance does not drop when the sensors in the stairways
were excluded. This is because the sensors complementarily react to each other.

Detecting Room-to-Room Movement by PIR Sensors in Home Environments
10.98

F-measure

0.95
0.9

11

0.94

0.95

0.95

0.92

0.90

0.90
0.88
0.84
0.80

0.8

one
two

0.78

0.7
0.6
All sensors ON

w/o hall (1F)

w/o corridor (1F) w/o stairway (1F) w/o stairway (2F)

w/o hall (2F)

Fig. 7. Results for two subjects.

However, the use of both sensors would be important when the two subjects
passing each other on the stairs. These results indicate that it is important toplace sensors at points where inhabitants pass each other.
Finally, the F-measures for the case when only one of multiple sensors installed in each room was used is shown in Figure 8. The bar chart of “Living
East/Far,” for example, is the result when one out of four sensors on the eastside and far from the doorway, and 21 sensors outside the living room wereused. “Far” and “Near” denote the sides far from the doorway and near the
doorway. The results of the living room show that the performance of the sensor at West/Near was the lowest of the four sensors in the living room. This isbecause the West/Near sensor is close to the kitchen, which incorrectly reacted
just when the subject entered the kitchen. However, the F-measure of living
West/Far dropped to 0.76 for two subjects. This is because the subjects did not
go to the far side of the living room. The performance of sensors at the far sidein the kitchen, Japanese-style room, bedroom, Western-style room 1, Westernstyle room 2, and spare room was also lower than that of sensors at the nearside. Accordingly, a sensor placed at the near side performed better than that at
the far side since the inhabitant does not always go to the far side of the room.
These results indicate that it is more eﬀective to place sensors at the near sideof the doorway. However, sensors placed very close to the doorway may output
a false positive just when the inhabitant walks through the front of the room.
Therefore, sensors should be placed taking their detection range and angle into
consideration.

6

Conclusion

We proposed a method to detect room-to-room transitions in a home environment by using only some infrared sensors. An evaluation was carried out withthe data collected in an experimental two-story house for one and two test subjects, and an F-measure of 0.82 was conﬁrmed, which is a relatively high degreeof performance even though relatively few sensors were used, and our proposed
method does not require data collection for training. In addition, we investigated the performance when changing the combination of sensors and clariﬁedthat sensors place where transitions cross contribute to improving accuracy and12

K. Murao, T. Terada, A. Yano, and R. Matsukura
1

0.95

0.94

0.95

0.95

0.93

0.93

0.93

0.93

0.94

0.94

0.91
0.90

0.9
F-measure

0.86

0.86

0.86

0.85
0.82

0.82

0.85
0.82

0.85

0.85

0.92

0.93

0.90
0.84

0.83

0.84

0.82

0.8

0.84
0.82

one

0.79
0.76

0.75

two
0.70

0.7
0.65
0.6

LivingLivingEast/Far

East/Near West/Far West/Near

LivingLivingKitchen

KitchenFar

Near

Japanese- Japanese- Bedroom Bedroom
style
style
Far

Near

Far

Near

Western- Western- Westernstyle 1style 1style 2
Far

Near

Far

Westernstyle 2

Spare
room

Spare
room

Near

Far

Near

Fig. 8. Results for two subjects.

that removing malfunctioning sensors also improves accuracy. Sensors in rooms
should be placed near the doorway taking into consideration their detection
range and angle.
We are working on a method to identify people and to connect transitions,and will conduct further investigations on the sensor arrangement considering
their detection area, angle, and placement.

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