THEORETICAL ADVANCES

Soft authentication and behavior analysis using a chairwith sensors attached: hipprint authentication

M. Yamada Æ K. Kamiya Æ M. Kudo ÆH. Nonaka Æ J. Toyama

Received: 5 September 2007 / Accepted: 12 April 2008

� Springer-Verlag London Limited 2008

Abstract An authentication system using a chair with

sensors attached is described. Pressure distribution (hip-

print) measured by network-connected sensors on the chair

is used for identifying the person sitting on the chair.

Hipprint information is not sufficient for maintaining a

high level of security but is sufficient for providing per-

sonalized services such as automatic log-in at home or in a

small office. In experiments, we obtained correct identifi-

cation rates of 99.6% for five people and 93.2% for ten

people. A false rejection rate of 9.2% and a false accep-

tance rate of 1.9% were achieved using another group of 20

people. The results also showed that changes in hipprints

can be used to estimate what the person sitting on the chair

is doing, for example, using a mouse or leaning back.

Keywords Soft authentication � Sensor-attached chair �Behavior analysis � Posture analysis

1 Introduction

Due to the rapid development of network devices and the

downsizing of computers and sensor devices, it is now

possible to provide many kinds of personalized service in

response to the user’s implicit/explicit demands. For

example, Coen has proposed a conceptual environment

called ‘‘intelligent environment’’ (IE) [1] in which people

are fully supported by information/communication tech-

nology. In a typical IE, people can use voice, gait, face, and

other physical traits in order to communicate with com-

puters connected by a network.

To provide personalized services, it is necessary to

identify who the user is, and what the user wants to do

anytime and anywhere. In other words, user authentication

and intention analysis are necessary. The most important

issue for authentication in this situation is no disturbance of

our daily life. Such a system must accord with our life at

home or at the office. The sensors used for identifying the

user or for monitoring the user’s behavior should, there-

fore, be invisible or unnoticeable. A video camera is

usually used for such a purpose, but a camera gives people

an uncomfortable feeling about being observed or being

recorded. Use of video cameras is, therefore, not a good

choice. For the same reason, the system should not require

the user’s cooperation for authentication.

In this paper, we propose the use of a sensor-attached

chair to achieve this goal. People sit on chairs for a large

part of a day for working and for relaxing. Therefore, such

a system is one of the most ideal devices. When a user sits

on the chair, the user’s ‘‘hipprint’’ (pressure distribution) is

measured for distinguishing the user from others and for

continuously monitoring the user’s behavior.

Several biometrics such as iris, fingerprint, and palm

vein have been used for commercial authentication sys-

tems. These biometrics provide strong evidence for

identification, but they are not appropriate for our goal,

mainly because they need the user’s cooperation. At home

or at the office, we do not need a high level of security but

personalized services. Therefore, we distinguish them by

calling the former ‘‘hard authentication’’ and by calling

the latter ‘‘soft authentication.’’ In soft authentication, the

authentication process should not impose a burden on the

user in the physical sense or in the psychological sense or

both.

M. Yamada � K. Kamiya � M. Kudo (&) � H. Nonaka �J. Toyama

Graduate School of Information Science and Technology,

Hokkaido University, Sapporo, Japan

e-mail: mine@main.ist.hokudai.ac.jp

123

Pattern Anal Applic

DOI 10.1007/s10044-008-0124-z

In general, we can only obtain several weak pieces of

evidence in soft authentication because we cannot expect

the user’s cooperation. Therefore, we cannot expect a high

rate of correct identification in situations in which many

persons are candidates to be identified. As a possible situa-

tion, we assume five persons for a ‘‘home situation’’ and

ten persons for a ‘‘small office situation’’ in the following.

Another important assumption is the existence of, not a

single system, but multiple systems for soft authentication.

It should be possible to increase the rate of correct iden-

tification by combining several pieces of weak evidence.

As such an alternative system for soft authentication, we

have been developing a tracking system using infrared

sensors attached to the ceiling [2]. The tracking system

provides rough/probabilistic information about where a

specified person is. Such evidence is also weak for

authentication in its single usage, but it is useful for nar-

rowing down the number of candidates for authentication

on the chair. A system in which tracking information and

hipprint information is combined should have good

performance.

The weakness of evidence would be useful in a different

sense. The typical biometrics such as iris are strong enough

to provide a high level of security, but there is nothing that

we can do if they are stolen or completely copied. On the

other hand, weak pieces of evidence are applicable only in

limited conditions/situations and a combination of them is

necessary. Therefore, even if a single piece of evidence is

stolen, it would not be crucial.

Information obtained from pressure sensors attached to a

chair has been used for posture identification [3, 4], but

there has been no study on the usefulness of this infor-

mation for person identification. We tried to identify one

person in a small group of up to ten persons. We also

investigated whether it is possible to estimate what the

sitting user is doing on the basis of changes in hipprints.

As related applications of pressure sensors, Murase

et al. [5] tried to detect the user’s walking status from the

pressure distribution of multiple footprints, and Iwashita

et al. [6] tried to determine body direction and behavior of

the user sitting on a stool with pressure sensors attached to

the bottom surfaces of four legs.

There are many potential applications of user identifi-

cation by this system to personalized services. For

example, a TV channel could be changed to the user’s

favorite program when the user takes a seat. A recorded

message or email notification could be provided to the user

when the user sits on a chair. Automatic log-in to and log-

out from a computer is another potential application.

Continuous monitoring of the user’s health condition is

another possibility. We could detect the situations in which

the user has been taking a wrong posture for a long time

and the user falls down suddenly by some kind of illness.

2 Equipment

The photographs of the chair are shown in Figs. 1 and 2,

and the dimensions are shown in Table 1. The photograph

of a pressure sensor is shown in Fig. 3. The specifications

of a sensor (Flexiforce [7]) are summarized in Table 2.

Thirty-two pressure sensors are placed on the seating face

as shown in Fig. 2. Each sensor is placed 40 mm apart

from the other sensors on 8 9 8 cells.

The sensors are very thin (Table 2) and can be easily

bent to fit the contour of the seating face. In the experi-

ments, we placed a cushion of 1 cm thickness on the

sensors. One unit consists of four sensors, and eight units

are connected to a network in a cascading manner. The

sampling rate is 2 Hz (once every 0.5 s). This slow rate is

chosen so as to cooperate with the other system with

infrared ceiling sensors [2]. Therefore, we measured a set

of 32 values at one time. We call this set a ‘‘frame’’.

3 Identification information and individual differences

We examined the sensor responses during the period in

which a person is sitting. The sum of output values for one

subject is plotted in Fig. 4.

We noticed that the time required for sitting on the chair

is 1.0–3.0 s. After that, the sensor responses become stable

with slight fluctuation. We, therefore, separated a time

Fig. 1 Chair with pressure sensors

Pattern Anal Applic

123

series of sensor responses into two parts: a ‘‘transient part’’

and a ‘‘stable part’’ (Fig. 4).

Then we examined the difference in distributions of

sensor responses over users to confirm whether there exist

any individual differences, in the transient part and in the

stable part, separately. Some services could be provided

promptly to a user if identification in the transient part is

possible, while identification in the stable part would make

it possible to constantly identify the user and to monitor

what the user is doing.

The change in pressure when a person starts sitting is

shown in Fig. 5. Here, some cells are not measured, so they

are interpolated by the neighboring cells with measured

values. In Fig. 5, we can see that pressure is gradually

distributed from the front part to the back part. This person

(user A) leans back in the left direction (looking from the

person) after 1.0 s.

Figure 6 shows the ‘‘hipprints’’ (pressure distributions)

of four users. A comparison of these pictures indicates

some differences. User A sits in such a way that he leans

back towards the left. User B leans forward. The hipprint of

user C is spread, indicating that user C sits with knees

Fig. 2 Seating face with pressure sensors

Table 1 Dimensions of the chair

Height 82.5 cm

Seat depth 38 cm

Seat width 39 cm

Length of leg 44 cm

Table 2 Specification of a pressure sensor

Thickness 0.208 mm

Length 203 mm

Width 14 mm

Diameter 9.53 mm

Connector 3-pin male square pin

Maximum load 4.4 N

Fig. 3 Pressure sensor

0

500

1000

1500

2000

2500

3000

3500

4000

0 5 10 15 20 25 30

Frame (0.5 sec each)

Sum

of

Sens

or V

alue

s (1

pt ≅1

.5e-

4 N

)Stable part

Transient part

Fig. 4 Change in sensor outputs

Fig. 5 Change in pressure distributions of user A in the transient part

(the brighter, the larger pressure value)

Pattern Anal Applic

123

apart. User D leans to the right. These observations demon-

strate that there are significant differences between users in

the stable part. When a person starts sitting, the pressure

spreads gradually and then settles down the user’s hipprint

in the stable part. For example, for user A, the pressure

distribution in the third frame in Fig. 5 is already similar to

the hipprint of user A in Fig. 6. Therefore, we can expect

that there is also a cue for identifying the user even in the

transient part.

4 Experiments

In the following, we use three kinds of datasets (Table 3).

A. Twelve male students aged 20–26 years and with

weights ranging from 50 to 70 kg (Fig. 7). All the

subjects belong to our laboratory, and therefore, sat in a

relaxed manner.

A0. Twenty students including some female students.

This dataset is additionally used with dataset A in order

to examine the robustness against pretenders. They are

all first visitors.

B. Twenty-five students with 15 female students (weight

range 45–59 kg, ages 18–21 years) and 10 male students

(weight range 45–64 kg, ages 18–25 years). They visited

our lab for the first time, and so sat neatly.

C. Twenty-one subjects (15 male and 6 female subjects)

sat on the chair. They came once every 3 weeks and sat

3 times a day. They are students taking a lecture and sat

nervously after the lecture.

In these datasets, a single subject is included in only one

dataset. The difficulty of problems increases from A to C.

Especially, in C, subjects forget the previous way of sitting.

To examine to what degree the performance depends on

the problem size (the number of persons), five and ten

persons were randomly chosen from the subjects, in com-

mon to all datasets. Here, a size of five is taken as a typical

home situation and a size of ten is as a small office situa-

tion. Such a random subset selection is repeated 20 times.

4.1 Basic performance based on dataset A

We first used dataset A in which male students belonging

to our laboratory sat down as they usually do. Each subject

was asked to sit down for about 15 s on the chair. Between

measurements, they were asked to leave the chair and take

Fig. 6 Hipprints of four persons in the stable part (the brighter, the

larger pressure value)

0

1

2

3

4

5

6

7

45-50 45- 50 50-55 55-60 60-65 65-70 70-75

stcej

bus f

o re

bm

uN

Weight (kg)

Fig. 7 User body weights of dataset A

Table 3 Three datasets used

for experiments

Dataset A’ is used with A

N.A. not available

Dataset No. of persons

(male/female)

Age Weight (kg) Subject type Situation Measuring times

A 12 (12/0) 20–26 50–70 Laboratory students Sat relaxed 20 times during 1 day

A0 20 (N.A.) N.A. N.A. First visitors Sat tensed Once during 1 day

B 25 (10/15) 18–21 45–64 First visitors Sat tensed 10 times during 1 day

C 21 (15/6) 18–20 40–80 First visitors Sat tensed 9 times in 3 weeks

Pattern Anal Applic

123

a short walk. The number of frames (sets of 32 responses at

a sampling time) was 30 in each trial. Each subject sat on

the chair 20 times (20 trials).

We divided 20 sets of data for each subject into 19 for

training and 1 for testing, and repeated this division for 20

different testing sets. As a result, the identification rate was

calculated by 20-fold cross-validation. The classifier was a

support vector machine (SVM) [8, 9] with a radial basic

kernel with default parameter values [the soft margin

parameter was 1.0 and the variance parameter was taken as

the dimensionality (number of features)].

In this experiment, rates of identification of five and ten

persons at the first and third rank were calculated. Here, the

identification rate at the third rank means that the decision

is judged as ‘‘correct’’ if the true user is included in the top

three guesses. Six sets, F1–F6, of features were examined

in effectiveness as shown in Table 4. The value of ri,j(t) is

the value of the sensor located at position (i,j) at frame

(time) t. When the values are averaged over some frames, it

is denoted by ri,j. The first three feature sets F1–F3 are used

for identification in the stable part, and the latter three

feature sets F4–F6 are used for identification in the tran-

sient part, taking into account the dynamic change in

pressure distributions.

4.1.1 Identification in stable part

First, we tried to identify persons in the stable part using

their hipprints. We manually found the starting frame of

stable part from an obtained time series. Then we used 15

frames of Nos. 3–17 where No. 1 is the staring frame. The

sensor values were normalized in each feature so as to be

mean 0 and variance 1 in the training data. The same

normalization was applied to the testing data. This nor-

malization was made because SVM is greatly affected by

the difference in dynamic scales.

In this experiment, we used the three feature sets, F1–F3,

for investigating their potential power for identification.

The results are shown in Table 5.

From Table 5, we see that the difference in weights

(feature set F2) gives to some extent information for

identification. However, the difference in seating positions

(hipprints) measured by feature set F1 provides more

information. With those 32 (time-averaged) features, we

obtained identification rates of 98.4% for five persons,

96.9% for ten persons, and 96.3% for 12 persons on

average. Addition of the total sum (feature set F3) did not

contribute to the increase of performance.

4.1.2 Identification in transient part

Next, we examined the possibility of identifying users in

the transient part. We used the first three frames (1.5 s from

the start of sitting) for this goal. We compared the three

feature sets F4–F6. The results are shown in Table 6.

From Table 6, we see that the time differential of sensor

values (feature set F5) holds almost the same identification

information as the raw sensor values over the first three

frames (feature set F4), while the second-order time dif-

ferential (feature set F6) was not so informative. As a

result, using 96 values of F4, we obtained average identi-

fication rates of 99.6% for five persons, 93.2% for ten

persons, and 92.9% for 12 persons. These high levels of

accuracy indicate that we can distinguish persons even at

the beginning of sitting motion.

Table 4 Examined feature setNo. Feature set ( no. of features) Expression Description

F1 Sensor values (32) ri,j Averaged sensor value at position

(i,j) over 15 frames

F2 Sum (1)P

i;j ri:j Sum of all sensor values

F3 F1 + F2 (33)

F4 Three frame sensor values

(96)

ri,j(t + 1), ri,j(t) and ri,j(t-1) Three sensor values at times t - 1,

t and t + 1 at position (i,j)

F5 Time differential (32) ri,j(t + 1) - ri,j(t - 1) Time differential values at times

t + 1 and t - 1 at position (i,j)

F6 Second-order differential

(32)

ri,j(t + 1) - 2ri,j(t) + ri,j(t - 1) Second-order differential of sensor

values at times t + 1 and t - 1

at position (i,j)

Table 5 Identification rates in the stable part

Feature set

(no. of features)

Identification

rank

Home Office All

Five

persons

(%)

Ten persons

(%)

12 persons

(%)

F1: Sensor

values (32)

First 98.4 96.9 96.3

Third 100.0 99.9 99.6

F2: Sum (1) First 50.6 29.4 24.6

Third 91.8 70.6 65.0

F3 (F1 + F2)

(33)

First 98.3 96.6 95.8

Third 100.0 99.9 99.6

Pattern Anal Applic

123

4.1.3 Authentication experiment

As described in Sect. 1, the aim of soft authentication is not

maintenance of a high level of security. However, it is still

worth knowing to what degree such a system is robust for

pretenders. To confirm the robustness, we added 20 sub-

jects of dataset A0 to A (Table 3).

In the experiment, we used 228 frame sets (19 frame

sets 9 12 persons) of known (registered) 12 persons for

training, and 32 frame sets of which 12 frame sets are taken

from known 12 persons (unused one data 9 12 persons),

and 20 frame sets are taken from the extended subjects (one

frame set 9 20 persons) for testing. This evaluation was

repeated 20 times. The features used were 96 sensor values

in the first three frames (feature set F4).

We varied the value of class-cost parameter p of SVM in

the range (0,1), by step 0.1, in order to measure two kinds of

error rates. Here, parameter p controls the strength of

assignment to the target class in a one-against-other judge-

ment. The measured two kinds of errors are false rejection rate

(FRR) and false acceptance rate (FAR). The results are shown

in Fig. 8. As a result, we obtained FAR of 0.0% at FRR of

39.2% and FAR of 1.9% at FRR of 9.2% as the most balanced

pair. Here, due to the discrete nature of parameter p (these

error values change largely by a little change of the value of

parameter p), we could not obtain a smooth ROC (reciever

operating characteristic) curve. The values show that this

system is robust enough against pretenders, keeping an

identification rate around 90.0%. In soft authentication, mis-

identification does not cause a serious problem. In addition, it

does not seem that so many unknown persons come in at home

or at the office. In these respects, these rates of correct iden-

tification are sufficient for home or office situations.

4.2 Mood and gender differences based on dataset B

We were able to identify up to ten persons with an identifi-

cation rate of over 90.0% on dataset A. The subjects of

dataset A were all men who belong to our laboratory, and

therefore, they sat in a relaxed manner. This might be the

reason for such a high level of accuracy. In other words, in

more tense situations, the performance might be question-

able. Therefore, we carried out an experiment on dataset B

consisting of 25 first visitors (Table 3). Each subject sat on

the chair 10 times (2.3 s each) during 1 day. A 1-h break was

taken after the fifth trial. These subjects had never visited our

laboratory before, so they sat nervously.

In almost the same condition as before, we obtained the

average identification rates (Table 7). For 15 female subjects

(45–60 kg), we obtained the rates of 78.7% for five persons,

68.5% for ten persons, and 63.1% for 15 persons in the first

rank. At the third rank, the rates rose to 97.8% for five per-

sons, 89.5% for ten persons, and 81.9% for 15 persons. These

rates can be regarded as rates in almost the worst case,

because the subjects sat very neatly with their knees together

and the subjects’ weights were similar1. For ten male sub-

jects (45–64 kg), the identification rates were 85.1% for five

persons and 74.7% for ten persons in the first rank. At the

third rank, the rates rose to 98.8% for five persons and 93.5%

for ten persons (Table 7). The difference (4.0–6.4%)

between male and female subjects shows that men are a little

Table 6 Identification rates in the transient part

Feature set

(no. of features)

Identification

rank

Home Office All

Five

persons

(%)

Ten

persons

(%)

12

persons

(%)

F4: Three frames sensor

values (96)

First 99.6 93.2 92.9

Third 100.0 98.1 96.7

F5: Time differential

(32)

First 94.5 90.5 89.2

Third 99.2 96.6 95.8

F6: Second-order

differential (32)

First 73.8 65.1 62.5

Third 91.3 81.8 79.6

0

2

4

6

8

10

0 20 40 60 80 100False rejection rate (%)

)%( etar ecnatpecca esla

F

0.20.3

0.4

0.5

0.6

0.7

0.80.91.0

Fig. 8 An ROC curve in FAR and FRR. Each value at a point shows

the value of the class-cost parameter of SVM

Table 7 Identification rates for first visitors (dataset B)

No. of subjects Identification rate (%) (first/third rank)

Male Female Both

5 85.1/98.8 78.7/97.8 82.8/98.4

10 74.7/93.5 68.5/89.5 74.3/93.5

15 – 63.1/81.9 68.5/88.8

20 – – 63.5/83.8

25 – – 59.9/80.1

There were 10 male and 15 female subjects. A random selection was

carried out for the result of a smaller number than the possible number

of subjects

1 Japanese young girls are very polite, especially in the first meeting.

So, they sat neatly, all alike.

Pattern Anal Applic

123

easier to be identified than women. One of the reasons is that

men open their legs wider than women. The degradation

from the first experiment implies that the tense/relaxed mood

of subjects very much reflects the way of sitting.

4.3 Time factor analysis based on dataset C

Last, we confirmed to what degree the system performance

decreases when a long period of time is taken for the same

person. In dataset C (Table 3), the same person came again,

1 week after the previous measurement. They sat on the

chair, after a regular freshman-class, 3 times a day, once

every 3 weeks (a total of 9 times). In dataset C, 21 students

(15 male students and six female students) came to our

laboratory only for the class. For estimating the identifi-

cation rate, we divided the 3-week data into 2-week data

for training and 1-week data for testing in three possible

combinations.

The results are shown in Table 8. We can see that the

performance decreases largely compared with the result of B

in which the subjects sat 10 times during 1 day. This shows

that the subjects sat differently between weeks. In addition,

they might have worn different clothes from previous mea-

surements. In this respect, these rates are most realistic.

Another explanation of such a large degradation is found

in the difference of the numbers of training trials. Indeed, the

number of training trials for each person is 19 for A, nine for

B, and six for C on the average, since 20- or 10-fold cross

validation is used for A and B, but only 2-week data (six

trials) is used for C. To confirm this, we obtained the results

of 10-fold cross validation without week division in Table 8.

In this case, the number of training trials was raised up to

eight that is comparable to nine of B. Even in this case, the

identification rates are 5–8% less than those of B. Therefore,

a time difference very much affects the system accuracy.

4.4 Total evaluation

It is worth comparing all the results of the three datasets.

They are summarized in Table 9.

The system performance has a range of 78–98% for five

persons and 67–96% for ten persons, depending on situa-

tions. Even for over 20 subjects, the identification rate is

close to 60%.

As we have shown, when persons are a little tensed, and

therefore, sit politely on the chair, or when they come

occasionally to the place, or both, this system cannot

identify them so accurately. Indeed, in the worst situation

that we tested, the rates go down to about 60% for five

persons and 46% for ten persons. To improve the perfor-

mance, it would be effective to collect many more training

trials and to update training samples for continuous usage.

5 Behavior analysis

We proceeded to the analysis of the behavior of a user. A

user sat on the chair for about 1.5 h and did anything he

liked. For simplicity, 32 sensors are gathered into four

groups, each of which includes eight neighbor sensors.

These four groups are named as Front Left (FL), Front

Right (FR), Back Left (BL), and Back Right (BR). The

eight sensor values of a group are averaged to obtain the

value of the group. During this period, the subject stood, sat

down, took a break, typed, and Web-browsed on the PC.

All of the subject’s motions were recorded by a video

camera. The time series was roughly divided into four

states of ‘‘standing’’, ‘‘sitting’’, ‘‘working’’, and ‘‘resting’’,

referring to the camera record (Fig. 9).

We investigated the correspondence between sensor

reading and actual behavior. The results are shown in

Table 10. From Table 10, we can find a correspondence

between the data reading and the user’s behavior. The easiest

reading is obtained when the user sits down or stands up. In

these situations, the sensor values increase and decrease

rapidly, respectively. We can also read the situation in

which the user stretches the user’s right hand to a mouse

Table 8 Identification rates for first visitors (dataset C)

No. of subjects Identification rate (%)

Different weeks Mixed weeks

First Third First Third

5 61.2 92.9 78.6 97.8

10 46.0 77.3 67.5 91.1

15 39.0 66.7 60.3 83.8

20 35.0 59.0 59.2 77.4

There were 15 male and 6 female subjects. They sat 39 a day once in

every 3 weeks. Subjects are randomly chosen

Table 9 Summary of identification rates on three datasets

No. of subjects Recognition rate (first/third) (%)

A B C

5 98.4/100.0 82.8/98.4 78.6/97.8

10 96.9/99.9 74.3/93.5 67.5/91.1

12 96.3/99.6 – –

15 – 68.5/88.8 60.3/83.8

20 – 63.5/83.8 59.2/77.4

25 – 59.9/80.1 –

The situation becomes harder in order of A, B, and C. A: 12 (12 male

subjects) laboratory students sat relaxed 209 during 1 day. B: 25 (10

male and 15 female subjects) first visitors sat tensed 99 during 1 day.

C: 21 (15 male and 6 female subjects) first visitors sat tensed 39 a day

once every 3 weeks

Pattern Anal Applic

123

(‘‘Resting 2’’). It is also easy to read when the user stretches

and leans back (‘‘Resting 3’’). When the user exchanges

crossed legs, the order of FL and FR values also exchanges

(’’Working 2’’). We see from this correspondence that, from

reading of the sensors we can know a subject’s behavior to a

fairly detailed level, such as operation of a mouse.

6 Discussion

In soft authentication, as described in Sect. 1, it is natural

to use a combination of several pieces of weak evidence. In

this respect, the performance of 67–97% at the third rank

for ten persons using a sensor-attached chair seems suffi-

cient as a system, using only a single piece of evidence. In

a typical home, children, young persons, and elderly per-

sons live together. For such persons differing in sex, age,

and weight, the performance would be improved.

In behavior analysis, we examined only one subject. We

think that one user is sufficient for knowing the possibility

of behavior analysis. Posture analysis has been carried out

in previous studies [3, 4]. In those studies, one posture was

successfully identified from seven or ten possible kinds of

postures. They used a larger number of sensors (2,016) than

0

20

40

60

80

100

120

140

160

180

0 50 100 750 800 850 900 950 1000 700 1050

BLBRFLFR

Sitting down

Rest1

2kroW1kroW

Rest2

Work3

Work4

Rest3

Rest4

Work5

Work6

Stnding up

BL

FL

BR

FR

2150 2200 2250

Fig. 9 Sequential sensor

responses and the user’s actual

behavior

Table 10 Sensor readings and the user’s actual behavior

Category Sensor reading Actual behavior

Sitting All four sensor values increase from zero Starts sitting

Rest1 All values increase instantaneously. BL and BR are

higher than FL and FR

Leans back and resits deeply

Work1 FR increases but FL decreases instantaneously, while

BR increases a little

Operates a mouse with right hand and puts left hand on

the keyboard. After a little while, types with both

hands

Work2 Four sensors are almost stable. FR and BR are higher.

At time 820, BR and BLincrease, while FR and FL

exchange their orders

Types while leaning forward. At time 820, resits and

changes the crossed legs

Rest2 BL decreases dramatically, while BR and FR increase Sits upright and extends right hand to the mouse

Work3 BL, FL and FR increase, but BR decreases Leans forward to the left to take a document

Work4 BL greatly decreases and BR decreases a little Looks at the screen and the document while leaning

forward

Rest3 Both BL and BR increase Leans back and stretches out

Rest4 BL and BR decrease rapidly and then BL increases Resits upright, takes a plastic bottle and drinks, and then

leans back again

Work5 All four values are stable Types with left hand and operates the mouse with the

right hand at the same time

Work6 Only BL remains high and the others are stable Leans forward and types with both hands

Standing All sensor values drop to zero Stands up

Pattern Anal Applic

123

ours (32) to obtain detailed static images. We used only

four (combined) sensors and analyzed a signal (an image)

sequence. For analysis of behavior, such dynamic infor-

mation is necessary. The resolution should, therefore, be

appropriately determined. In a task of behavior analysis,

low-resolution images might be more informative than

high-resolution images. This is because a succinct repre-

sentation chosen appropriately in accord with background

knowledge is sometimes more advantageous than detailed

representations.

In this study, we assumed homes or offices as the place

where the system is introduced and have discussed to

combine several pieces of weak evidence. Such a candidate

is personal time schedules (e.g., regular times of arriving/

leaving, conference times, and holidays). We have also

been developing a tracking system with an infrared ceiling

motion sensor network [2]. In this system, people are

identified with their biometrics at the entrance of a room

and then they are continuously tracked by the infrared

sensors. Due to the usage of infrared sensors (to maintain

privacy), we can know roughly where a specified person is

as time passes. The sensor-attached chair can be used as a

tool for recovering the degraded tracking precision. Con-

versely, we can reduce the number of candidates to be

identified by tracking information obtained from the ceiling

sensors.

7 Conclusion

We have tried to identify subjects (five persons for home

situations and ten persons for small office situations) using

their hipprints in order to realize soft authentication for

providing personalized services.

It was revealed that the difference in sitting positions

(hipprints) provided much more information than did

weight difference. The identification rates in the stable part

range from 61.2 to 98.4% for five persons and from 46.0 to

96.9% for ten persons, depending on situations. The rates

are affected by the tense/relaxed mood of the user and a

period of time of measurements. These factors can largely

change individual hipprints.

Even in the first few seconds of sitting, we were able to

obtain high identification rates of 99.6% for five persons

and 94.5% for ten persons at best. These results indicate

that individuals can speedily receive personalized services

such as automatic log-in to a computer. In addition, we

succeeded in roughly knowing what the user is doing on

the chair. We are considering collaboration of this system

with other systems using several sensors to collect more

pieces of weak evidence.

References

1. Coen MH (1998) Design principles for intelligent environments.

In: Proceedings of 1998 national conference on artificial intelli-

gence (AAAI-98), pp 547–554

2. Hosokawa T, Kudo M, Nonaka H, Toyama J (2008) Soft

authentication using an infrared ceiling sensor network. Pattern

Anal Applic. doi:10.1007/s10044-008-0119-9

3. Mota S, Picard RW (2003) Automated posture analysis for

detecting learner’s interest level. In: Proceedings of workshop on

computer vision and pattern recognition for human–computer

interaction (CVPR HCI), pp 1–6

4. Tan HZ, Slivovsky LA, Pentland A (2001) A sensing chair using

pressure distribution sensors. IEEE/ASME Trans Mechatron

6:261–268

5. Murase M, Kijima R, Ojika T (1998) A development of walk-navi

with sensing shoes. In: Proceedings of 14th symposium on human

interface, pp 131–136

6. Iwashita K, Toyama A, Hashimoto N, Hasegawa S, Sato M (2004)

Development of locomotion interface based on step-in-place

movement. IEICE Trans Fundam Electron Commun Comput Sci

J87-A-1:87–95 (In Japanese)

7. http://www.tekscan.com/index.html

8. Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector

machines. Software available at http://www.csie.ntu.edu.tw/

*cjlin/libsvm

9. Fan R-E, Chen P-H, Lin C-J (2005) Working set selection using

second order information for training support vector machines.

J Mach Learn Res 6:1889–1918

Author Biographies

Masafumi Yamada received

the B.E. and M.E. degrees from

Hokkaido University, Sapporo,

Japan, in 2004 and 2006,

respectively. He has been

working in Information Tech-

nology R&D center, Mitsubishi

Electric Corp., since 2006. His

research interests include soft

authentication, pattern recogni-

tion, and service science.

Kazuhiro Kamiya received the

B.E. in 2006 and is now a sec-

ond-year master’s degree

student at Hokkaido University,

Sapporo, Japan. His research

interests include soft authenti-

cation and behavioral analysis.

Pattern Anal Applic

123

Mineichi Kudo received his Dr.

Eng. degree in Information

Engineering from the Hokkaido

University in 1988. At Hokkai-

do University, he was an

instructor (1988–1994), an

associate professor (1994–

2001), and is now a professor

(2001 till present). In 1996, he

visited at the University of

California, Irvine. In 2001, he

received, with professor Jack

Sklansky, the 27th annual pat-

tern recognition society award

for the most original manuscript from all 2000 Pattern Recognition

issues. His current research interests include design of pattern rec-

ognition systems, image processing, data mining, and computational

learning theory. He is a member of the Pattern Recognition Society

and the IEEE.

Hidetoshi Nonaka received the

B.E. degree in 1983, and the

M.E. degree in 1985 from the

University of Tokyo. He

received the D.E. degree in

1993 from Hokkaido Univer-

sity. From 1985 to 1996, he had

been instructor at Hokkaido

University, and since 1996, he

has been associate professor at

Hokkaido University. His

research interests include

human interface, sensor appli-

cations, intelligent information

systems, recreational mathematics, and interactive computing.

Jun Toyama received the B.S.

degree in 1982 and the M.E.

degree in 1984 from Hokkaido

University, Sapporo, Japan. He

is assistant professor in the

Graduate School of Information

Science and Technology, Hok-

kaido University. His research

interests include speech recog-

nition and speech perception.

He is a member of IEEE, ASA,

ASJ, IEICE, and IPSJ.

Pattern Anal Applic

123