Proceeding of the IEEE
International Conference on Robotics and Biomimetics (ROBIO)
Shenzhen, China, December 2013

Measurement and Estimation of Indoor Human Behavior of Everyday
Life Based on Floor Sensing with Minimal Invasion of Privacy
Yoonseok Pyo, Tsutomu Hasegawa, Masahide Tanaka,
Tokuo Tsuji, Ken’ichi Morooka and Ryo Kurazume
Abstract— This paper describes a method of measurement
and estimation of human behaviors in a room together with thelayout of objects on the floor. The information obtained by themethod is essential for a service robot working in a human daily
life environment. The method uses only one laser range finder(LRF) installed in the room and a strip of mirror attached toa side wall close to a floor. The area of sensing is limited toa plane parallel to and just a few centimeters above the floor,
thus covering the whole room with minimal invasion of privacyof a resident while reducing occlusion. Processing both distanceand reflectance acquired by the LRF from the surface of theexisting objects allows us to exclude immediately distinguishable
clusters and to focus on the analysis of remaining clusters. The
human behavior models that we propose are effectively used toestimate human behavior based on the limited LRF data. Our
experimental results validate the effectiveness of the proposed
method.

I. INTRODUCTION
Daily life assistance is one of the most important applications of service robots in the near future. A service
robot must have a function for recognizing its surroundings.
However, it is very difficult to recognize an environmentof human daily life since it is a dynamically changing real
space: there exist human beings walking and working around
while the space is cluttered with furniture and everyday
objects. It is quite difficult to recognize its surroundings
for a robot by only using sensors mounted on its limited
body. An alternative and promising approach would be toconstruct an informationally structured environment embedded with distributed sensors combined with a data-base of
the environment[1], [2]. In this paper, we propose a method
to measure and estimate human behavior and location ofvarious everyday objects in a room, a private indoor space,
while protecting privacy of residents.
An ordinary room for everyday life is equipped with
furniture. Also everyday objects of various size and shapeexist there. The layout of small furniture like a chair oftenchanges and small objects are moved due to the daily
human activity. It is very difficult to directly measure all
Y. Pyo and M. Tanaka are with Graduate School ofInformation
ScienceandElectricalEngineering,
KyushuUniversity, 744 Motooka, Nishi-Ku, Fukuoka 819-0395, JAPAN.

{pyo,tanaka}@irvs.ait.kyushu-u.ac.jp
T. Hasegawa iswithTechnology,
Koshi-Shi,

Kumamoto
Kumamoto,

National
College of
861-1102,
JAPAN.

hasegawa@kumamoto-nct.ac.jp
T. Tsuji, K. Morooka and R. Kurazume are with Facultyof Information Science and Electrical Engineering, Kyushu
University, 744 Motooka, Nishi-Ku, Fukuoka 819-0395, JAPAN.

{tsuji,morooka,kurazume}@ait.kyushu-u.ac.jp

978-1-4799-2744-9/13/$31.00 ©2013 IEEE2170

the human behavior and displacement of objects by using
sensors. Vision sensors suffer from illumination change andocclusion. In addition, deployment of multiple cameras isneeded to complement the incompatibility of range of viewand the resolution of sensing. However, embedding so manycameras in our daily life environment so that almost all the
space is within the visible space would be expensive andtedious due to initial setups and later maintenance of wiring,
fixture, calibration, lighting control and so on. Furthermore
it is not acceptable for residents because of possible invasionof their privacy.
This paper proposes a novel method of measurement
and estimation of daily human behavior in a room together
with the layout of objects on the floor. The method uses
only one LRF fixed on the floor very close to the wall,
therefore the sensor implementation is very simple andlow cost. Since the range of view is limited to a scanningplane parallel to and a little above the floor by a couple ofcentimeters, the system causes minimal invasion of privacy
while enabling acquisition of position data of human feet
together with objects on the floor including small everyday
objects. However, it is difficult to directly recognize thesituation of surroundings since the available sensor data
is limited. To overcome the difficulties, we introduce thefollowing ideas.
1) Reduction of occlusion by using LRF and a mirror
The scanning plane of the LRF is carefully selected tobe parallel and just a few centimeters above the floor sothat there exist relatively few objects that cross the plane in
our western style environment which contains a bed, chairs,
tables and so on. This means that the sensing plane is lessinfluenced by occlusion. In addition, a strip of mirror is
attached to the side wall of the room so that the reflected
laser rescans the floor. Thus the floor is scanned by thelaser projected from two different positions. Therefore thefurniture having legs with small cross section will not cause
severe occlusion. In the case of the furniture having large
support on the floor, we can lift it using small pillars tocreate space below for the scanned laser to pass throughthus reducing occlusion.
2) Distance value and reflectance value of objects
The LRF provides both distance value and reflectancevalue when the laser beam is projected on the object
surface. The reflectance value is a function of distance,
angle of incidence of the laser to the surface, and opticalproperty of the surface of the object. Objects can be
distinguished by this normalized reflectance values if
the difference is sufficiently large above the noise level.
However, some objects have similar reflectance value. To
expand the normalized reflectance value, a retroreflective
tape is attached to the surface of the object of interest. The
retroreflective material has a transparent surface in whichmicro glass balls are embedded so that the incident lightis reflected back to the same direction. So it gives very
high reflection and it is often used, for example, for traffic
symbol panels. Attaching the retroreflective tape on the
supporting part of stable furniture and mobile objects likewheeled robots makes them distinguishable by its reflection
value. Only a small tape is enough for the previous
object, since their vertical pose with respect to the floor isalways kept. Furthermore, we have developed a method for
identifying the pose of objects by attaching a tag coded byreflection value. This method has been successfully applied
to pose measurement of a cylindrical robot which has nogeometrical features useful for pose identification.
3) Human behavior model related to the usage of furniturefor daily life
Human behavior in a room can be classified into fourmodes: walking, stopping, sitting down on a chair, andresting on a couch or bed. Human feet move differently
depending on each mode. The floor sensing by LRF willlose sight of feet when the human is resting on a bed,
however, walking feet can be observed approaching to andleaving from the bed before and after the rest. So we canestablish models of human behavior corresponding to eachmode in terms of how human feet are observed by the floor
sensing system. Then the human behavior can be measured
and estimated from sensor data based on these models.
II. RELATED WORKLRF has been widely used to measure human motion[3],[4], [5]. In most of the previous works, the laser scanningplane is set to be horizontal at the height of the waist of an
adult. However, this configuration has some disadvantages:
the position measurement is influenced by the motion ofarms and hands; small children cannot be measured; and tall
tables and chairs located at the central area of the room maycause occlusion. Therefore, instantaneous measurement may
be less accurate and less reliable, even though the long term
trend of motion is available. Pedestrian tracking is reported
in [6] by setting the scanning plane at the height of humanleg. Though it has successfully tracked pedestrians at therailway station in Tokyo, it cannot distinguish other objects
existing within the measurement area.
Elastic Pressure sensor has been used to measure humanfoot print [7], [8]. The position and the direction of thehuman foot can be measured if the resolution of the pressure
sensor is 1cm or less. However such sensor device itself isexpensive. Furthermore covering all the surface of the room
is very expensive including wiring and later maintenance.
Moreover the position measurement of light weight objects

2171

Fig. 1.

Top view of floor sensing system using LRF and mirror

is difficult, although such objects made of tissue or paper
exist everywhere in our daily environment.
Using cameras to track everyday objects in a room isreported in [9]. Measurements are influenced by changeof illumination and occlusion. Furthermore it may not bedesirable due to possible invasion of privacy. Everyday
objects can be tracked once RFID or ultrasonic tags areattached to them [10], [11]. However active scanning of large
directional antennas is required for tracking RFID tags. Thisis time consuming and resolution is rather low. In addition,
the ultrasonic tag is rather large and expensive to be used on
numerous everyday objects.
In our daily life, we often leave or drop everyday objects
on the floor. Also, various objects, such as persons, robots,
furniture and movable furniture may exist on the floor. Therefore, detection and position measurement of these objects
on the floor is an important issue in the informationallystructured environment.
III. INDOOR FLOOR SENSING FOR HUMAN FEETAND OBJECTSA. Floor Sensing by a Laser Range Finder with a strip ofMirror on the Wall
We installed a LRF on a floor at one end of a room next
to a wall so that the scanning plane is parallel to the floor
at a height about 25mm above the floor. A strip of mirror is
attached to a side wall to reflect the laser beam from the LRF.
The measureable area is covered by the direct beams fromthe LRF and/or the indirect beams reflected by the mirror(Fig.1).
If no object is placed on the floor, the LRF measures the
distance to the opposite wall. If an object is placed on thefloor, the LRF measures the distance to the object. Even asmall object is detected if it has a height of more than 25mm.
However, the LRF may not obtain any distance data due tothe reflection property of the object. In this case, our systemstill obtains the distance value thanks to the combination of
the LRF and the mirror as follows.
1) Object That Reflects Laser Beam: If an object reflects asufficient laser beam, the LRF obtains the distance not to thewall but to the object. Then the system detects the existenceof the object from this difference, and calculates the position

using the distance and the angle of the laser beam. Moreover,
two types of the measurements can be obtained. One results
is obtained using the direct laser beam from the object, andthe other is obtained from the indirect laser beam via themirror (Fig.1).
2) Object That Diffuses Laser Beam: If the placed object
does not reflect the sufficient laser beam, e.g. a transparent
plastic bottle, the LRF is unable to obtain any distance data.
This implies that some object is placed somewhere on theline from the LRF to the wall. Also if the LRF fails to obtain
indirect measurement, then some object is placed on the linefrom the LRF to the wall via the mirror. By integrating thesetwo pieces of information, we can calculate the position of
the object as the intersection of the two lines (Fig.1).

Fig. 2.

Objects in daily environment

B. Laser Reflection Intensity of Everyday Objects
The LRF(Hokuyo URG-30LX) measures not only distancevalues but also intensity values of laser reflections. We use
geometric information and material information to classify
persons, robots, furniture, and everyday objects made ofwood, paper, plastic and rubber in daily environment (Fig.2).
Figures 3 show the reflection feature for each object material.
The reflection intensity varies depending not only on the
optical property of objects but also on the distance and angle
of incidence of the laser beam (Fig.3). We obtain the intrinsic
intensity of reflectance by normalizing the obtained reflection
value with respect to distance and angle of incidence.
In our model, we exclude the first 800mm from ourmeasurement due to the strong discontinuity of reflection
intensity curve with other part (Fig.3). Then, we obtainedEq.(1) by curve fitting using experimental data. Finally, theapproximate intrinsic intensity (2) was obtained using Eq.
(1) with measured intensity, r and α from LRF.
Intensity
IntrinsicIntensity

cos(α)0.196
r0.287
r0.287= Intensity
cos(α)0.196= Kd Iq

Fig. 3. Experiment results of reflection intensity vs. distance and angle ofincidence

Fig. 4.

Experiment setups

(1)
(2)

Kd

:

diffuse reflection coefficient

Iq
α

:
:

the power of the light source
angle of incidence on the surfacer:distance from the light source

Next, we evaluate the effect of the normalization using Eq.
(1), (2). A piece of wood, a red bucket, a green plastic bucket,
and a cardboard box were placed at 2-3 m in front of the LRF(Fig.4). The reflection intensity data from the surface of eachobject is shown in Fig.5. Moreover the normalized reflection
intensity of each object is obtained in Fig.6. If there is adifference in the reflection intensity among the objects, thenthey can be classified immediately.

Fig. 5.

Reflection intensity value of each object

C. Expanding Reflection by Attaching the Retro-reflective
Material
If there is no enough difference in the reflection valueof each object, we cannot classify the object using only

2172

Fig. 6.

Normalized reflection intensity value of each object

Fig. 8.
Fig. 7.

Position of retroreflective material

Reflection Intensity value of retroreflective materialreflection intensity. To solve this problem, we attached atape made of retroreflective material to the object. Thus, thedifference of the reflection features becomes large (Fig.7).
Using this simple method, we will be able to classify objects
based on the distance and the reflection intensity.
IV. OBJECT DETECTION INSIDE INDOORENVIRONMENT
A. Measuring Human Activity and Object Layout by the
Floor Sensing System
The main goal of our system is to measure the trajectoryof human walking and mobile robots. Moreover we obtainthe layout of furniture such as tables, chairs and a mobile
dining wagon. And finally we obtain the location of everyday
objects on the floor. Clusters of points are obtained by the
LRF. By analyzing a sequence of these clusters, motion
tracking of objects is achieved.
The measurable data correspond only to partial profile
of object from the LRF. Moreover some objects may beinvisible due to the occlusion by other objects. In addition, it
may be often difficult to separate the multiple clusters when
they are closely located. This happens for example when aperson approaches a table or sits down on a chair. Therefore,
it is not easy to accurately classify and track objects in realtime.
To solve these problems, we use the reflection intensityand position information obtained from the target surface.
Although it is not possible to classify all objects by additionally using reflection values, it would be easier to classify
unknown clusters if we can eliminate easily classifiableobjects by reflectance.
B. Improvement of Reflectance Detection by Attaching
Retroreflective Materials
Our daily life environments (Fig.1) contains some furniture that can be moved by a person like for example, achair and a dining cart (Fig.8). We attach the retroreflective
material on the surface of legs of the chair and wheels of
the dining cart where the laser scans. Furthermore, we attachsemi-transparent sheet over the retroreflective material tocontrol the reflection intensity. As a result, they will be easilyclassified only by the reflection intensity as shown in Fig.9.
Figure 8 shows how the retroreflective material is attachedto the legs of movable furniture. Since the tags are small, they2173

Fig. 9.

Distinctly different intensity compared to the other objects

will not affect general appearance of these objects. This isgood for keeping our daily environment as it used to be even
if service robots are working around.
C. Measurement of Robot Pose by Coded Reflection
It is not possible to determine the orientation of object
based on the distance value obtained by LRF if the horizontal
cross-section of the object is rotationally symmetric withrespect to the vertical axis of rotation. However, there arecertain objects that have circular cross-section shape. A
cylindrical mobile robot is a typical example. We developed
a method to measure the orientation in such cases.
The idea is to attach distinguishable optical features
around the robot base. An optical feature is in our case atransition between reflective and not reflective material. Thistransitions are indicated in Fig.10a as boundary points BPs.
Geometric distance between optical features is designed sothat they are distinguished from each other around the robot
base as shown in Figs. 10a, 10b and 10c.
Once the robot base is scanned by the LRF, visible opticalfeatures are detected and identified (Fig.10d), and then arematched with the robot base model (Fig.10e). The complete
pose of the robot is computed based on the position ofidentified features.
We have wrapped a strip of reflection encoded tape aroundthe body of a Roomba robot and evaluated accuracy of thepose measurement. The robot was located at 9 different
positions as shown in Fig.11. At the right center position,
the robot took 8 different orientations. We have measured
100 times for each pose and obtained mean errors of 5.6mm
along x-axis, 3.5mm along y-axis, and 3.4degrees about
vertical axis. The variance was 18.9mm, 16.9mm, 9.5degrees
respectively.

Fig. 10.

each mode in terms of how the human feet are observed by
the floor sensing system, and then based on these models
the human behavior is measured and estimated from sensor
data.
1) Ordinary walk
The left foot and the right foot of a walker repeat moving
in air and landing on the floor alternately. In the floor sensing
data, appearance and disappearance of clusters repeat in aconstant walk cycle. The disappeared cluster appears within
the stride after a period of a walking cycle. This appearance
may be influenced by self-occlusion by another foot and/or
occlusion by objects on the floor.
2) Standing still
A resident stands still while he is picking up objects on
the floor, taking out objects from a cabinet, or putting objects
into a cabinet. Transition from ordinary walking occurs andclusters of two feet typically approach each other and stop.
3) Sitting down on a chair
While sitting, clusters of feet irregularly approach, disappear, appear, and stop near the chair. Mutual occlusion
among human feet and chair legs occurs. Transition between
this mode and ordinary walking mode occurs.
4) Staying on the bed
After approaching the bed, the clusters of feet disappear.
When leaving the bed, the clusters appear again and transition to ordinary walking mode occurs.

Eaxample for measurement of robot position

B. Unified Estimation of Human Behavior based on different
Human Behavior Models

Fig. 11.

The human motion is composed of a series of differentmode of behaviors. Possible transition among different modeof behaviors is analyzed to establish a mode transition
diagram (Fig.12). Then based on this transition diagram,
human behavior is estimated as shown in Fig.13.
Estimation modules of human behavior are implemented
corresponding to each human behavior models. These modules work on the input LRF data independently and output
the estimation result in parallel in every 1s. Then the behavior is uniquely estimated according to the mode transition
diagram (Fig.13).

Evaluation of pose error

V. MODEL BASED ESTIMATION OF HUMANBEHAVIOR

VI. EXPERIMENTAL RESULTS

A. Human Behavior Model
It is very difficult to correctly find human feet based on
the shape of cluster and reflectance value in the single scandata obtained from LRF when the resident is walking in theroom populated with furniture and everyday objects on thefloor. Therefore we use time series of consecutive scans tofind human feet and to recognize human behaviors.
Human behavior in a room can be classified into fourmodes: walk, stop and standing still, sitting down on a chair,and resting on a couch or bed. Human feet move differently
depending on each mode. The floor sensing by LRF willlose sight of feet when the human is resting on a bed,
however, walking feet can be observed when approaching
to and leaving from the bed before and after the rest. So
we establish models of human behaviors corresponding to2174

We have performed experiments for estimating humanbehaviors in our everyday life environment shown in Fig.14.
Roomba is waiting for command. A bed is set at a cornerof the room. A desk and a bookshelf are located along awall. A table and a chair are positioned at the center area.
Since the robot and the chair are equipped with retroreflective
tape, they are immediately identified based on the reflectanceintensity value by the LRF sensing system. Distance datafrom other pieces of furniture is treated as background data
because they are not movable.
The scenario of the experiment is as follows:
1) A resident enters the room in ordinary walk mode.
2) He comes to a book shelf and stands still to pick up one
book.

Fig. 14.

Fig. 12.

Fig. 13.

Fig. 15.

Mode transition diagram

Flow of human behavior estimation
TABLE IA SSIGNMENT OF NUMBERS TO BEHAVIOR
Number
0
1
2
3
4
5
6

Behavoir
Lost
WalkingStanding
Staying near chair
Sitting on chair
Sitting on bed
Staying on bed

3) Then he approaches the table.
4) He sits on the chair for reading the book.
5) He stands up and approaches the book shelf to return it.
6) Then he walks out the room.
7) He enters the room again, approaches the bed.
8) He sits on the bed.
9) He lay down on the bed.
10) Finally he gets up and walks out of the room.
The original data obtained by the LRF is cluttered withmany data points as shown in Fig.15a. The clusters belonging
to the robot and the chair are recognized based on thereflection values as shown in Fig.15b, then the clusters of

2175

Everyday life environment

Original data by LRF

human feet are obtained in the remaining clusters.
The trajectory of the human feet was obtained as shownin Fig.16, corresponding well to the behavior scenario of theexperiment. The result of behavior estimation is shown in
Fig.17, where the estimated behavior is arranged along thevertical axis and the time is shown in second in the horizontal
axis. Fig.17a shows outputs of each estimation module ofbehavior model. The outputs are unified based on the modetransition diagram as shown in Fig.17b.
The module of walking/standing-still model estimateseither walking or standing-still, and outputs corresponding
number, 1 or 2 (See table I). The module outputs 0 meaning“lost” when the resident of the room is out, staying on thebed, or when the module fails identifying the behavior due
to, for example, occlusion or noise.
The chair sitting module outputs 4 when the resident isreading a book on the chair, and outputs 3 meaning that
he is staying near the chair before and after the sitting
mode (À in Fig.17a). This is because the foot clusters aremeasured near the chair while he is moving it to sit on
and to leave from. The mode of staying near the chair isalso identified for a short period when he walks near thechair while moving toward the door after staying on thebed (Á in Fig.17a). In this short period, the walking/staying
module outputs 1 estimating “walking” simultaneously andindependently. Then unified estimate is made to be “walking”
based on the mode transition diagram (Á’ in Fig.17b). The
part Â in Fig.17b corresponds to “leaving out of the room”
in the scenario. This part is correctly estimated because thecluster being tracked has disappeared near the door andthen appeared later again. The dashed blue line in Fig.17bshows the grand truth obtained by human observation of thevideo image of the experiment. Estimated sequence of humanbehavior coincides mostly with the truth.

TABLE IIR ECALL AND PRECISION RESULT
Person
1
2
3
4
5
6
7
8
9
10
Average

Recall
93.3%
93.3%
86.7%
100%
93.3%
86.7%
100%
93.3%
73.3%
93.3%
91.3%

Precision
93.3%
87.5%
81.3%
93.8%
87.5%
86.7%
88.2%
93.3%
70.6%
87.5%
87.0%

VIII. ACKNOWLEDGEMENTS
The present study was supported in part by a Grant-in-Aid
for Scientific Research (B) (23360115).
Fig. 16.

R EFERENCES

Trajectory of human feetFig. 17.

Estimation result

Table II shows the evaluation of resulted estimate ofexperiment with 10 subjects.
VII. CONCLUSION
A method of measurement and estimation of both humanbehavior in a room and layout of objects on the floor has
been proposed. The ultimate goal of this work is to provide
information of surroundings to a service robot working ina dynamically changing human daily life environment. The
proposed sensing system design enables the acquisition of
the above mentioned data by using only one laser range
finder (LRF) in a room thus achieving minimal invasion ofprivacy of a resident. The system simultaneously utilizes
distance measurement data and reflectance measurement
data obtained by LRF from the surface of objects. Humanbehavior models and the model based estimation have been
implemented and the experiments validate effectiveness of
our approach.
Future work would include a more complete set of human behavior models and robust estimation of the humanbehaviors.

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