An Occupant Sensing System for Automobiles Using a Flexible
Tactile Force Sensor
By Naobumi Kuboki *, Hiroyo Okamura *, Takayuki Enomonto *,Takuya Nishimoto *, Toshikazu Ohue *2 and Kazuyoshi Ando *2

With a view to upgrading the functions of automotive safety systems and driverassistance technology, work is going forward on the development of sensors tomake judgments of seat conditions, including the body build of the occupant, and the presence orabsence of a child restraint system. Particularly in the U.S. there are demands to reduce injuries
inflicted by air bags, and this requires sensors that detect occupant body build, posture, etc.
Using a flexible tactile sensor, we have measured data for load distributions applied by car seatoccupants and have investigated a method of classifying occupants by body build.
We have verified the accuracy of a method for judging the standing body height of occupants
based on peak distance and buttock print width as shown by the loads applied to the seat, andhave found that in discriminating between adults and children, an accuracy of about 90% can beobtained.

ABSTRACT1.

INTRODUCTION

To enhance automobile safety, supplemental restraint
systems (SRS, more commonly known as air bag systems) have come into wide use in recent years and a variety of technologies including seat-belt pretensioning andforce limitation are being adopted. In the United States,
however, fatal accidents caused by air bags have also
been reported and the U.S. Department of Transportation's National Highway Traffic Safety Administration
has called for development of a safer air bag 1) having
reduced potential for causing injury. With such a smart air
bag system, air bag deployment should be regulated bysuch factors as the severity of the impact and the seating
configuration (whether a child restraint system is installed,
the build and posture of the occupant, seat position, etc.),
and determining these requires sensors. A number ofsensing methods--electric field 2) , infrared 3) , etc.--havebeen proposed, and their advantages and disadvantages
are known.
Accordingly we have developed a flexible tactile sensor
suitable for use in an in-vehicle environment. We are also
studying the feasibility of a system in which judgments ofbody build and so on are made from data on the distribution of occupant load on the seat, and herewith report on a
prototype occupant sensor system using load distribution
data.

2.

DETERMINING BUILD WITH A FLEXIBLE TACTILE SENSOR

Flexible tactile sensors are used for electronic keyboard
instruments and in medical instrumentation applications,and are similar in structure to the membrane switches
found in computer keyboards. The difference lies in thefact that whereas a membrane switch has only two positions--on and off--the flexible tactile sensor has electrodes
of pressure sensitive ink so that the electrical resistance
between electrodes varies continuously in accordancewith the pressure applied to the electrode. Like membraneswitches, flexible tactile sensors can be configured byscreen printing in a wide variety of patterns, and can beinstalled in currently used car seats without the need for
major structural modification. They are also so thin andsupple that, when installed between the urethane foam

Top view

A

A'

Ag inkPressure sensitive ink

A-A' Cross section
Insulation
layer

PEN film

Adhesive layer*

Automotive Technology Center*2 NITTA CORPORATION

Figure 1

Schematic drawing of a flexible tactile sensor

89and the cover, their presence is not noticeable to theoccupant.
The in-vehicle flexible tactile sensor developed in thisstudy uses a polyethylene-naphthalate (PEN) base film.
Figure 1 shows a top view and cross section of a pressure-sensitive point. The electrodes at the pressure-sensitive points are approximately 10 mm in diameter and thesensor has an overall thickness of about 0.2 mm. An insulating layer is printed over the silver ink circuitry, preventing migration of the silver. There is also a gap between
the electrodes at the pressure-sensitive points, so thatwhen the air in the gap expands or contracts with theatmospheric temperature, the minimum pressure required
to activate the flexible tactile sensor changes. An air outlet
is therefore provided (not shown here) as in the case of amembrane switch.
As Figure 2 shows there is an inversely proportional
relationship between the resistance of the flexible tactile
sensor and the pressure applied, and it is dependent of
atmospheric temperature. Sensitivity is determined primarily by the composition of the pressure-sensitive ink,
the type and thickness of the base film and the structure(electrode area and gap) of the sensor cell. The main reason for the temperature dependence is thought to be thatthe stiffness of the base film and pressure-sensitive inkchanges in accordance with the ambient temperature.
Changes in the resistance of the flexible tactile sensor
are read out using the inverting amplifier circuit of an operational amplifier (see Figure 3), and Rs indicates the resistance of the pressure-sensitive point. There is an inversely
proportional relationship between the pressure applied tothis point and its resistance (Equation 1), and the output
voltage Vout of operational amplifier is inversely proportional to the pressure P applied to the pressure-sensitive point

Resistance (kΩ)

10
90°C
60°C
25°C
0°C
-30°C
1

0.1
0

2

4

6

8

10

Pressure (N/cm2)

Figure 2

Sensor performance

Rf
Rs
Vin

+

Figure 390

Read-out circuit

Vout(Equation 2). It is thus possible to find the pressure on thepressure-sensitive point simply by reading the voltage.
(1)
A = Rs . P
where:A is sensitivity,
Rs is the electrical resistance of the pressure-sensitive point, andP is the pressure applied to the pressure-sensitive
point.
Vout = -Vin . (Rf /Rs)
(2)
= -Vin . (Rf /A) . P
where:
Vout is the output voltage of the operational amplifier,Vin is the input voltage of the operational amplifier,and
Rf is the feedback resistance
Next let us consider the method by which occupantbody build is judged using the flexible tactile sensor.
Taking first the traditionally adopted method in which
judgments are based on the load applied to the seat cushion, 4) we may expect to find a correlation to body weight
as long as the seated posture remains unchanged, but
this poses a problem in that the load on the seat cushionvaries with backrest angle. Another problem is that withsmall children their feet do not reach the floor, while withadults some of the load bears on their feet and the floor,
so that estimating body weight simply from the load on theseat cushion introduces a large error. Additional problems
in measuring load using a flexible tactile sensor include
the temperature dependence of the sensor itself, and thetemperature and humidity dependence of the stiffness of
the urethane foam used for the seat cushions, as well as
its deterioration over time.
Accordingly a flexible tactile sensor mat consisting of alarge number of pressure-sensitive points arranged in a 2dimensional matrix was installed between the urethane
foam and the cover of the seat, and the relationshipbetween load distribution data obtained from measurements of the pressure applied to all pressure-sensitive
points and the parameters of body build were observed.
Figure 4 shows a typical load distribution applied to theseat cushion when a person is seated. Note the twin pressure peaks beneath the buttocks. It is known that thesepressure peaks appear below the ischial tuberosities, andit has been reported by Mizukami et al. 5) that no significant
correlation can be found between the peak value and theRohrer index (which indicates standing body height, bodyweight, degree of adiposity), or the maximum thigh circumference. This is because the shape of the ischial
tuberosities become tapered and narrow toward the front(tip), so that the area in contact with the surface of theseat varies according to the angle between the surface
and the pelvis, thereby causing the peak value to change.
This makes it difficult to estimate body weight of the occupant from the measured peak values.

Furukawa Review, No. 20

20012 peaks

100

Rear
Pressure (a.u.)

80
Peak distance
60
40
20
Buttock print width

0
-10

0

10

20

30

40

Position (cm)Front

Figure 5
Figure 4

Typical load distribution

Turning then to the relationship between skeletal structure and the parameters of body build, wide use has been
made in Japan of Fujii's equation for estimating standing
body height 6) with respect to all the long bones based on arecursive linear equation. Reports have also been madeon estimating standing body height by measuring thelength of the vertebral column, 7) but with respect to therelationship that concerns us here between the lower
pelvis and parameters of body build, it has been reported
by Kochi et al. 8) that there are major gender and individual
variations. In terms of the relationship between buttock
size and parameters of body build, it is known that there isa correlation between seated hip breadth and bodyweight.
Accordingly we investigated the relationship between
the distance between the two characteristic peaks observable below the ischial tuberosities (hereinafter referred toas the "peak distance") and the standing body height ofthe occupant, and the relationship between the width of
the area subjected to pressure (which is associated withseated hip breadth and is hereinafter referred to as "buttock print width" and the body weight of the occupant. We
also present some discussion of the accuracy of estimatesof the standing body height and body weight of occupants
derived from these parameters.

3.

Typical pressure profile

EXPERIMENTAL

Reducing the pitch between pressure-sensitive points will
provide more data on load distribution, but this requires
expanded interface circuitry between the flexible tactile
sensor and the signal processor, making the system too
bulky and expensive for in-vehicle use. With an eye to feasibility, what was required was the capability of judging theparameters of occupant body build with the smallest possible number of pressure-sensitive points.
Figure 5 shows the pressure profile for pressure-sensitive points on a straight line passing through the two pressure peaks in Figure 4 and spreading out to left and right.
Points shown in the figure represent the data measured
for the pressure-sensitive points, and solid lines represent
the results of approximations using approximation curves.

Photo 1

Installation of flexible tactile sensor mat

We undertook to reorganize the relationship between
these approximation curves and the parameters of bodybuild, or, in more concrete terms, taking the distance
between the two peak positions on the approximation
curves as the peak distance and taking the distance
between the lower reaches of the peaks that spread out toleft and right as the buttock print width, the relationshipbetween standing body height and body weight.
The objective of the experiment was to investigate therelationship between buttock size and parameters of bodybuild. A flexible tactile sensor mat with 64 pressure-sensitive points arranged in an 8x8 matrix at a pitch of 2.5 cm
front-to-rear and 3 cm left-to-right was installed in a compact sedan, between the urethane foam and the cover ofthe passenger seat, in the area where the buttock would
come when the occupant was seated (see Photo 1).
In the experiment, load distribution measurements weremade by having test subjects ride in a vehicle having asensor-equipped seat. The test subjects comprised 212
men and women aged 3 to 65. Adult subjects were permitted to adjust the back rest and slider to their liking andwere given no guidance regarding posture. Children, on
the other hand, were directed to sit so that, at a standard

91

back rest angle, their buttocks went deep into the seat.
After an adult test subject got in, the car was driven about
300 m at a speed of 10 km/hr, after which, with the car
stopped and the engine on, the load distribution data wascollected. When the subject was a child, data was collected without moving the car (engine off). The standing bodyheight and body weight of the subjects were measured
immediately before they got in the test vehicle. Height andweight were measured with shoes of; weight was measured with clothes on.
Data on those subjects who sat too far forward or wereotherwise placed their buttocks outside the area with sensors was excluded. Figure 6 shows the relationshipbetween the peak distance found from approximation
curves and the standing body height for the remaining 166
subjects. It can be seen that as peak distance increased,
there was a linear increase in the height of the subjects.
There was also a gender-based difference in the adults. A
recursive straight line was obtained for the data, and it
was found that in estimating standing body height from thepeak distance using that recursive equation, the difference
between estimated height and actual height represented astandard deviation of 11.6 cm.
Figure 7 shows the correlation between the buttock print
width and body weight as obtained from approximation
curves. It can be seen that as buttock print width increases, there is a linear increase in body weight. And forweight, as for height, there was a gender-based differencein the adults. A recursive straight line was obtained for thedata, and it was found that in estimating body weight fromthe buttock print width, the difference between estimatedweight and actual weight represented a standard deviationof 9.5 kg.

4.

DISCUSSION

Here we estimated the accuracy of judgments for adults
and children from results obtained with the test equipment, using the size of crash test dummies as standard.
The method of classification by body build was to determine classification probabilities for the standing bodyheight and weight of occupants from the standard deviation for differences in intermediate values for height andweight of crash dummies as class boundaries, withrespect to four classes:. Class 1: child up to 3 years old. Class 2: child up to 6 years old. Class 3: female of slight build. Class 4: male of average build
Figure 8 shows the results of a determination of probability of occupants being classified in any class by standing body height, as estimated from peak distance. From
this graph we see, for example, that when an adult about
150 cm in height sits down, the probability of being classified in the four classes is:Standing body height (cm)200

. Class 1: 0.01%. Class 2: 9.10%. Class 3: 74.77%. Class 4: 16.12%

Male
Female

150

100

50

0
0

5

10

15

20

Peak distance (cm)Figure 6

Correlation between peak distance and standing
body height

100
Male
FemaleBody weight (kg)

80
60
40
20
0
0

10

20

30

40

Buttock print width (cm)Figure 7

92

Correlation between buttock print width and bodyweightFigure 9 shows the results of a determination of classification probability by weight, as estimated from buttock
print width. From this graph we see, as in the case ofstanding body height, that when an adult weighing about
50 kg sits down, the probability of being classified in thefour classes is:. Class 1: 0.10%. Class 2: 9.38%. Class 3: 84.11%. Class 4: 6.41%
Table 1 shows the results of analogous calculations forstanding body heights and weights corresponding to theother crash dummies.
These results showed that taking those the same sizeas or smaller than the 6-year-old dummy as children, andthose the same size as larger than the light-build female
dummy as adults, an accuracy of about 90% could be
anticipated in distinguishing children and adults, irrespective of which algorithm--peak distance based or buttock
print width based--was used.

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100

100

1

Class 1

Class 4

Class 2

Class 3

Class 410

Probability (%)

Probability (%)

10

0.1

1

Class 1

Class 3

0.1

0.01
0

50

100

150

0.01

200

0

20

40

Standing body height (cm)Figure 8Class 2

80

100

120

140

Body weight (kg)

Probability curves for classification calculated fromexperimental results for peak distance

Table 160

Figure 9

Probability curves for classification calculated fromexperimental results for buttock print width

Probabilities for classification of occupants of different size
Probability (%)
Peak distance5.

Buttock print width

Class 1

Class 2

Class 3

Class 4

Class 1

Class 2

Class 3

Class 4

3 year old child dummy

85.90

14.08

0.02

0

68.16

30.64

1.20

0

6 year old child dummy

14.10

76.79

9.10

0.01

31.84

58.68

9.48

0

5 percentile American female dummy

0.01

9.10

74.77

16.12

0.10

9.38

84.11

6.41

50 percentile American male dummy

0

0.05

16.07

83.88

0

0

6.41

93.59

CHILD RESTRAINT SYSTEM POLICYWith child restraint systems now being compulsory, it
becomes of practical importance in classifying the occupant in the passenger seat to determine whether a child
restraint system is installed or not. Car manufacturers recommend that for reasons of safety, that child restraint systems be installed in the back seat in cars equipped with afront passenger air bag, but it is always possible thatmotorists will install the child restraint system in front.
Figure 10 shows an example of the load distribution
when a child restraint system is in place. Note the significant difference in load distribution compared to when aperson is seated. We therefore examined whether it waspossible to discriminate between a person and a child
restraint system or other object occupying the seat. Using
the test equipment, load distribution data was collected
when the seat was occupied by a child restraint system, asandbag or a briefcase. Half of the data, combined withthe data on human occupants (see previous section) wasused as learning data for a neural network. When theremaining data was fed to the neural network and results
obtained, it was found that accuracy for the experimental
data of this study was about 90%. There are, however, anumber of problems to be considered when using thelearning function of a neural network.
One is that the accuracy of judgments varies according
to how the data used as learning data is defined, and,given the innumerable types of child restraint system now
on the market, it is a practical impossibility to develop aneural network that encompasses all of them. What is

Rear

Front

Figure 10 Typical load distribution with child restraint seat onseat cushion

more, as new types of child restraint systems are brought
to market, there is a high probability that neural networks
that only take account of the older types will produce
errors, so that motorists will not be able to use the new
child restraint systems. Judgment methods incorporating
pattern learning and fuzzy rules have also been considered, 9) but it is our opinion the algorithm that discriminates
between persons and objects by the relationship between
the points at which the pressure peaks are observed andthe load data in the vicinity of the peaks is superior.

6.

CONCLUSION

We have developed an flexible tactile sensor capable ofbeing used in an in-vehicle environment, and have considered a method for determining the body build of a seat93

occupant on the basis of data on load distribution from thesensor. It was found that in classifying occupants into twoclasses--child or adult--using the method of judgment considered, an accuracy of about 90% could be anticipated.
Using a prototype incorporating an algorithm for classifying occupants by body build and an algorithm for distinguishing a child restraint system or other object, we intend
to verify the effect of the occupant's posture and the determination of when a child restraint system or other object isplaced on the seat, examining the accuracy of judgments
made under field conditions, thereby proceeding to clarify
problems relating to practical use.

ACKNOWLEDGMENTS
In developing the flexible tactile sensor, Atsushi Ueda andMasahiro Hatsuta of the RETS Division, Nitta Corp.
worked on the pressure-sensitive ink and the structure of
the sensor. In developing the judgment algorithms, we
received many suggestions from Ken'ichi Arai of the
Science and Technology Center, Furukawa Information
Technology Co., Ltd. relating to the application of techniques of pattern recognition. In constructing the testing
equipment, Tatsuya Fujisaki and Toru Tomai of the RETS
Division, Nitta Corp. extended their cooperation for hardware design and software development. In developing theprototype, we received cooperation from Yasuhiro Arimaof the Science and Technology Center, Furukawa
Information Technology Co., Ltd.
Last but not least are the employees of Furukawa
Electric Co., Ltd. and their families, who cheerfully participated in the testing of the new algorithms. There are also
many others without whose support this work could not go
forward. We would like to take this opportunity to express
our thanks to them all.

94

REFERENCES
1) e.g., U.S. Department of Transportation NEWS November 22,
1996 : NHTSA ANNOUNCES COMPREHENSIVE PLAN TO
IMPROVE AIR BAG TECHNOLOGY AND REDUCE AIR BAG
DANGER2) K. Jinno et al.: SAE PAPER 971051(1997)
3) G. Wetzel et al.: SAE PAPER 971047(1997)
4) K. Kompaβ et al.: SAE PAPER 960226(1996)
5) Mizukami et al.: The journal of the Japan Medical Society ofParaplegia, (1994), 114 (in Japanese)
6) Yoshino et al.: Annual Report of the National Research Institute of
Police Science, 39(1986), 1 (in Japanese)
7) Terasawa et al.: Nippon Hoigaku Zasshi, 39(1985), 35 (inJapanese)8) Kochi et al.: Bultin. Industr. Pro. Res. Inst., 120(1991), 51 (inJapanese)
9) W. Fultz et al.: Vehicle Safety Restraint Systems: Performance,
Technologies, and Implementation Strategies, TOPTEC, August
10, 1999 Costa Mesa, California

Manuscript received on May 8, 2000.

Furukawa Review, No. 20

2001