Sensors and Materials, Vol. 11, No. 8 (1999) 457-468

MYUTokyo
S &M0388

Odor Sensing Characteristics in Residential Space
Using Metal-Oxide Sensors
Takashi Oyabu, Hidehito Nanto1,Hiroshi Kasahara2 ,Takeshi Onodera 3 and Naomasa Nakata 1
Kanazawa University of Economics, Kanazawa 920-8620, Japan
1
Kanazawa Institute of Technology, Kanazawa 924-0838, Japan
2New Cosmos Electric Co. LTD., Osaka 532-0036, Japan
3Faculty of Engineering, Kanazawa University, Kanazawa 920-8667, Japan(Received June 25, 1999; accepted October 18, 1999)
Key words:

odor sensor, sensor system, welfare, frequency analysis, sensor array

Various odors are generated in an indoor environment. It is necessary to know thekinds and grades of odors in order to recognize patterns of behavior in the daily life of aresident. In this study, four kinds of odor sensors are adopted, all of which are the metaloxide type. The daily behavior of the resident is recognized and understood using sensor­
output characteristics. A sensor unit, which consists of four odor sensors, is installed in
the residence. Twelve sensor units are set up at various points. Opening and shutting adoor, taking a meal and sleeping can be identified as behavior based on the sensor
characteristics. A running spectrum-method, which is a kind of frequency analysis, isused to identify the operation of an air-conditioner. The spectrum has a high frequency
component at meal times. The aim of this system is to construct a surveying system forthe aged individual living alone as a part of a welfare system.

1.

Introduction

The aged population is rapidly increasing in Japan. It is estimated that the number ofpeople over 65 years old will constitute 27% of the population in the year 2020. Thisphenomenon is remarkably noticable in rural districts and secluded places in the moun­tains. The fees for medical treatment and nursing care for the aged are also increasing, anda self-governing body may experience severe financial difficulties under the heavy

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burden of the costs of services. It is important to solve these problems using new social
systems and to improve social security. It is also necessary to decrease the number ofpeople requiring care and nursing by using an engineering system, and to derive a newsocial system and economical system from interdisciplinary research.
An experimental residence for the aged is built .as a model and various daily periodical
patterns in their behavior are examined to identify whether these patterns are typical ornot. It is possible to know a person's pattern of living using an odor sensing system.< 1 l The
number of times the toilet is used, eating breakfast, lunch and supper, and taking a bathand so on are examined to survey daily life of the aged _(2) The purpose of this study is tounderstand the aged person's lifestyle using odor sensors and to construct an odor sensor
system for the healthcare system. Human beings generate various odors as vital signs andvarious air polluting gases, C3l for instance, while preparing a meal, eating the meal andheating a room. Odor sensors have a high reliability and stability, and are adopted in gas
leakage detectors installed in residences.<4l Odor is a gas. The kinds and the concentra­
tions of the generated odors depend on the resident's behavior. Namely, the sensor is able
to identify the behaviors of the aged using an odor sensing system. The system can also
diminish the infringement of privacy of the resident more than systems using infrared
detectors or microphones.
In this study, sensor units composed of four types of odor sensors are installed at
twelve points in a residence. Tl1e output characteristics of the sensors are analyzed byfrequency analysis to recognize when the resident eats breakfast, lunch and supper.

2.

SystemA monitoring residence was constructed to aid in the experimental development of
medical and welfare instruments and techniques for the aged. The house is subsidized by
the Ministry of International Trade and Industry (MITI) of Japan. This institution is also
used to create a comfortable residential environment for the aged. Another purpose is tocombine various techniques for both medical-welfare treatments and care equipment.
There are six working groups (WG) attached to this experimental residence. They are as
follows;(1) hardware(2) software(3) examining living activity(4) equipment for health and welfare(5) residential environment for the aged(6) developing support instruments for the life of aged people.
This study belongs to the (2) WG, and one of the purposes of this group is to develop an
odor monitoring system for recognizing living patterns of the aged resident. The outside
and inside views of the house are shown in Fig. 1. The first floor is a centralized dataprocessing room in which there are many computers and work-stations. The second floor
is the residential area for the aged.
Tin oxide and zinc oxide odor sensors are employed in the system. There are four odor
sensors in a sensor unit (SU). The materials and gases or odors to which each sensor has

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(a)

(b)

Fig. 1. Welfare-oriented experimental residence. (a) Exterior of the residence and (b) interior of theresidence.

Table 1 Employed odor sensors which are fabricated by New COSMOS Inc. of Japan.
Type

Material

High sensitivity gases and odors

CH-E2
CH-E3
CH-N
AET-S

SnO2
SnO2
·snO2
ZnO2

VOC, CH3CHO, CH4, alcoholhigh sensitivity type for CH-E2
NH3, C2H5OH, CH3OH
H2S, CH3SH, CH3SCH3, CH3 SSCH3

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high sensitivity are listed in Table 1. These sensors are adopted in odor detecting
apparatuses, which are manufactured by New COSMOS Inc. of Japan and are commer­
cially available. The output of these sensors is somewhat influenced by changes intemperature. The system can, however, isolate human behavior characteristics from thesensor outputs. Humidity does not have any influence on the sensor output in theresidential environment. The sensors can be used for over 5 years in an indoor environ­ment. Many kinds of odors and gases are generated in an indoor environment. Some ofthem are generated from building materials, for example VOC (volatile organic com­pounds). Body odor also occurs in an indoor environment. The odors are due to NH3 , H2S
and CH3SH. Therefore, four types of odor sensors are employed. There are twelve SUs in
the second floor of the house, with a total of forty-eight sensors. All sensor signals areentered into a computer through a data converter and a data logger as shown in Fig. 2. The
second floor plan is shown in Fig. 3. Installation points of SUs are also indicated in the
figure. Each sensor signal can be sampled every 2 s, but for this study, is usually sampled
once a minute. The signals are memorized on a PC card memory (PCCM) and a hard disk,
and the data can be viewed over the Internet.

3.

Experimental

3.1 Odor sensor characteristics in daily life
Odor sensors, which are made of metal oxide, do not have a high gas selectivity. The
sensors are sensitive to many reducing gases and odors, and catalysts are added into thematerial to produce some selectivity.
Aged people prefer a barrier-free house, and most of them live in a one room
residence. The scope of their activity is centered in the dining room. Therefore, the odor
sensor signals in the room are influenced by their daily activities. The sensor output
characteristics in the dining room of the experimental house are shown in Fig. 4(a). A
door to a roof terrace was opened to the outside at 21 :48 and 6:53, and all sensor outputs
decreased at that time. The concentrations of the gases and odors in the indoor environ-

Data logger(AID converter)D

Data converter

Keyboard

Fig. 2. Data accumulation system for odors.

Mouse

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Bathroo@Toilet

Stu y
room

E]

Bed

@
Bedroom

Living room@

Mildle@@@0Im@ Sensor Unit : SU

KotatsutNFig. 3. Floor plan of welfare oriented residence and points of installation of SUs.

ment decrease when the door is opened. They return to their original levels when the door
is closed. The behavior of the sensor outputs on the roof terrace is shown in Fig. 4(b). The
sensor output characteristics increase when the door is opened. If the characteristics insidedecrease and those outside increase, the door has been open. The subject ate supper at
about 20:00 and afterwards washed the dishes. The characteristics fluctuate sharply when
the table is prepared, a meal is eaten and the table is cleaned. These sensor output
characteristics contain high frequency components. Fuel gas is not used when preparing
the meal; an electromagnetic cooking apparatus is used. The air polluting gases generated
with combustion do not appear. The subject awoke twice to use the restroom at 3:03 and
4:53. The sensor output characteristics barely vary at these times. The main behavior of
the subject is as follows:
20:02 eats a meal, drinks tea, clears the table
21 :43 opens door after clearing the table
22:21 closes the door
0:23 switches air-conditioner off (it had been operating all the time)
6:53 rises and opens the door
7:53 closes the door
8:03 breakfast (without cooking)Sensors and Materials, Vol. 11, No. 8 (1999)

462

5.0

>

(a)

Nov.18-19, 1998 /D Dining room

�,........,�-,---,'-,--,�-,--'f--,�--,---,-,........,r,

4.0

'i3.0

"$

02.0

1.0
0.0

5.0

19 21 23 1 3 5Time (h)7911Nov.18-19, 1998 / 0 Roof terrace

4.0
(b)

-s3.0

"$

02.0

1.0
0.0

CH-N
CH-E3

19 21 23 1 3 5Time (h)7911Fig. 4 Sensor output characteristics in the dining room and roof terrace. (a) In the dining room and(b) on the roof terrace.

8:53 clears the table
12:05 cooks and eats lunch
12:25 clears the table
A time lag occurs in Figs. 4(a) and 4(b) when the door is opened, namely, the decrease
in Fig. 4(a) and the increase in Fig. 4(b) have a discrepancy of three min. Thisphenomenon is also shown in Figs. 5(a) and 5(b). The sensor unit in the roof terrace isinstalled near the door and is 5.6 m away from the sensor unit in the ceiling of the dining
room. A three-min discrepancy over that distance (5.6 m) must be considered whenhuman behaviors are interpreted. It is difficult to identify an action that is completed�ithin only three min, because an odor molecule takes about three min to travel 5.6 m.

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5.0

Nov.18, 1998 ID Dinin room

4.0
�... 3.0

CH-E3

52.0
(a)

1.0
0.0
21:40 :50

CH-N

CH-E2

22:00 :10 :20
Time (h):30:40Nov.18, 1998/ORoofterrace
5.0 .-----.-----+---.------r-------,----,

4.0

(b)

>�.O

-a
02.0
1,0 __,,,,
0.0 ����.....__.....__�_....._____.
21:40 :50 22:00 :10:20 :30 :40
Time (h)Fig. 5. Output characteristics in which the time axis of Fig. 4 is expanded. (a) In the dining room and(b) on the roof terrace.

3.2 Frequency analysis

The characteristics of the CH-N odor sensor in Fig. 4(a) are analyzed using the"Running Spectrum" method which is also called "Sonic Analysis."C5l The results areshown in Fig. 6. Figure 6(a) shows the original data. The horizontal axis corresponds tothe passage of time. The de component is removed in the figure. In this method, the data
are divided into short periods, which are called "windows," and the analysis is applied toeach window. The sampling time is 60 s and the width ofthe window is 64 min. Changes
in frequency components are emphasised by this method. The result is indicated in Fig.
6(b). The data in Fig. 6(a) fluctuate sharply at about 20:00. The subject eats supper at that

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1.5

(a)

I

LO

0.5

0.0

-0.5

9

19

X 10-

21

23

3

5

Tnn:(h)

9

7

11

6

8

7

if 6

(b)

J

5
4

3
2

0

19

21

23

3

Tnn:(h)

5

7

9

11

(c)
X 10
9

-6

Fig. 6. Result of running spectrum analysis. (a) Output of odor sensor CH-N, (b) running spectrum
in two dimensions and (c) spectrum of (b) in three dimensions.

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time. The data also fluctuate at about 8:00. These phenomena are indicated by the striped
patterns of light and shade in Fig. 6(b ). Light patterns indicate the high frequency
components. The operation of the air-conditioner is noted until 0:23, and the spectrum of
the operation appears as lateral stripes. A three-dimensional graph is shown in Fig. 6(c).
The vertical axis is [20*log V] and the units are decibels [dB]. The term [V] is the gradeof the signal in units of volts. The top view of Fig. 6(c) is shown in Fig. 6(b). The
frequency-component variation and signal amplitude as a function of time are understoodfrom this figure.
Results of frequency analysis for outputs of four odor sensors in Fig. 4(a) are shown in
Fig. 7. Figure 7(a) indicates high frequency components which exceed a period of 35 min.
Figure 7(b) indicates the low frequency components below that period. The CH-E2 andNov.18-19, 1998/DDinin room

s

AET-S

0

CH-E3

"'

0

(a)

VJ

CH-N

Q.)

CH-E2

19 21 231

3 5 7Time (h)9 11Nov.18-19, 1998 /D Dinin room

s

0
(b)

0

"'

VJ
Q.)

19 21 231

3 5 7Time (h)9 11Fig. 7. Results of frequency analysis. (a) High frequency components of various sensor outputs and(b) low frequency components of various sensor outputs.

Sensors and Materials, Vol. 11, No. 8 (1999)

466

CH-E3 sensors are of the same type. Therefore, the frequency characteristics of thesesensors closely resemble each other; the data from the CH-N sensor are also quite similar.
All sensors are of the tin oxide type. Preparing and eating a meal as a specific behavior
can be identified by observing Fig. 7(a); the impulses indicate this behavior. The impulse
response includes various frequency components. A behaviour lasting for a long period,for example opening and then shutting a door after several ten minute intervals haveelapsed, can be identified in Fig. 7(b). The time of eating a meal is also detectable in the
figure. Of course, the de components are removed from the figure. In Fig. 7, the four odor
sensors appear to exhibit similar characteristics, but these characteristics differ subtly. In
this study, we could not determine which sensor was best suited to recognize daily humanbehavior; hence four types of odor sensors are adopted.

3.3 Data fusion in a three-dimensional graph
The outputs of each CH-N odor sensor in the different locations are fused into a 3Dgraph. It is thought that the sensor output expresses the minute variations in humanbehavior. Opening and closing the door, the extent of cooking (snack, light meal or usualmeal) an.d so on as specific behaviors can be recognised in the form of a graph. The 3Dgraphs are shown in Fig. 8(a) and 8(b). In Fig. 8(a), the z-axis indicates the CH-N sensor
putput on the roof terrace. The y-axis indicates the output in the dining room and the x­
�xis indicates the lapse of time. If the output on the roof terrace increases and the output
in tlie ciining room decreases, this means that the door is open. In the opposite case, it
meqns that the door is closed. The preparation of a meal is idfntified whep the output inthe dining room exceeds a threshold value. In Fig. 8(b), the z-axis indicates the sensor
output in the bedroom and the y-axis indicates the output in the dining room. The
characteristics do not fluctuate in either figure while the person is asleep. It is useful tocombine plural sensor outputs to identify various behaviors. Human behavior can be
identified using various types of sensor-characteristic graphs. If the kind of the odor
generated by a specific human behavior is known, the number of sensors can be reduced.

4.

Conclusions

Four odor sensors are used to identify the daily life behaviors of an elderly person
living alone. The resident generates various odors, depending on his behavior. Eachbehavior can be recognized by analyzing the odor sensor patterns in a residential environ­ment, because the patterns indicate a resident's behavior. It is useful to be able to isolate
various vital actions from raw odor sensor data; it is important to know whether the
resident of the house is living as usual or not. Frequency analysis is effective in
identifying the resident's daily behavior and the condition of the indoor environment. In
this paper, the daily behavior and the data from the results are matched. Information on
the daily patterns of a resident can be obtained using this odor sensor system.
�fore specific information to understand the patterns of human life will be obtained byn"ipti.ng a filtering process, and the reliability of the recognition of various behavior will
increase. This system is effective in the field of care and nursing for the aged.

Sensors and Materials, Vol. 11, No. 8 (1999)

467

2.0

(a)

0

1.5

0

1.0

� 0.5
3
Time (h)

0.0

0 .5

1.0

1.5

2.0

Diping room

Nov. 18-19, 1998(p)

E 2, 0
0

� 1.5

0
,&,

'o 1.0Oil� 0 .5.Q)
U 0 .019
0 .0Time (h)

0.5

1.0

1.5

2.0

Dinin g room

Fig. 8. The correlation between the sensor outputs in two different locations as a function of time. (a)Relationship of sensor outputs in dining room and roof terrace and (b) relationship of sensor outputs
in dining room and ceiling of the bedroom.

Acknowledgement
The authors thank Dr. S. Hirobayashi of Toyama University for useful discussions and
Dr. Y. Matsuura of Figaro Engineering, Inc. for performing some of the measurements.

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