Activity Recognition for Dogs Using Off-the-Shelf Accelerometer

Tatsuya Kiyohara, Ryohei Orihara, Yuichi Sei, Yasuyuki Tahara and Akihiko Ohsuga

Graduate School of Information Systems, University of Electro-Communications, Chofu-city, Tokyo, Japan

Keywords:

Activity Recognition, Accelerometer, Time Series Data Mining, Sensor Data Mining, Acceleration Sensor,

Dynamic Time Warping(DTW), DTW-D.

Abstract:

Dogs are one of the most popular pets in the world, and more than 10 million dogs are bred annually in Japan

now (JPFA, 2013). Recently, primitive commercial services have been started that record dogs’ activities and

report them to their owners. Although it is expected that an owner would like to know the dog’s activity

in greater detail, a method proposed in a previous study has failed to recognize some of the key actions.

The demand for their identiﬁcation is highlighted in responses to our questionnaire. In this paper, we show

a method to recognize the actions of the dog by attaching only one off-the-shelf acceleration sensor to the

neck of the dog. We apply DTW-D which is the state-of-the-art time series data search technique for activity

recognition. Application of DTW-D to activity recognition of an animal is unprecedented according to our

knowledge, and thus is the main contribution of this study. As a result, we were able to recognize ten different

activities with 65.8% classiﬁcation F-measure.

1 INTRODUCTION

There are services for dog owners that record dog’s

activity in the form of life logs and report it to them.

Examples of the services include the one provided

by Whistle Lab’s “Whistle” (Whistle Labs, 2013)

and NTT docomo’s “pet ﬁt” (NTT DOCOMO, 2014).

These commercial services recognize raw actions

such as “walking”, “running”, “resting” and “sleep-

ing”. These services themselves are evidences of the

demand to learn pets’ behavior when the owners are

away. However, the variety of actions recognizable

by the current commercial services is limited and far

from being satisfactory. In our analysis, which will

be veriﬁed in Section 2 by analyzing the results of a

questionnaire, there are three aspects of the demand

for pet activity monitoring. The ﬁrst aspect arises

from the interest in short-term healthcare. The sec-

ond aspect originates from the interest in long-term

healthcare. The third and ﬁnal aspect is related to

problematic behavior of pets.

For example, vomit reporting is desired for a pet

monitoring system because the action is directly re-

lated to internal health condition. The action should

be detected from the aspect of short-term healthcare.

Eye-scratching is an action that can lead to a seri-

ous disease if repeated multiple times. If a pet moni-

toring system reports the number of times the action is

occurred, early treatment by a veterinarian is possible

and a serious condition can be avoided. Therefore the

detection of the action is desirable from the aspects of

long-term healthcare.

In Japan, approximately 70% of the dogs share the

life space with human beings. In such circumstances,

the pet may exhibit problematic behavior such as bit-

ing the furniture and entering the places where it

should not, especially in the absence of owner. The

owners need to know the problematic actions in or-

der to take appropriate corrective measures, hence the

third aspect of the demand for pet monitoring. An ac-

tion related to this aspect is jumping. It is problematic

because it could reﬂect pets’ intention to touch things

at higher place, which are kept there by the owners

so that the pets could not play with them. Although

there is a study on monitoring the actions of a dog, the

accuracy of detection for those actions is not high.

In this article, we propose a method to monitor

dog’s behavior, which is especially effective in the

recognition of those actions whose demand of detec-

tion is high, according to our analysis of the demands

of the owners. The remainder of this paper is orga-

nized as follows. In Section 2, we will investigate

and analyze a questionnaire to see whether there is a

background to the kind of needs. In section 3, we will

write about the work related to the activity recogni-

tion of the dog and the search technique of time se-

ries data. In Section 4, we will present the algorithm

100

Kiyohara T., Orihara R., Sei Y., Tahara Y. and Ohsuga A..

Activity Recognition for Dogs Using Off-the-Shelf Accelerometer.

DOI: 10.5220/0005212001000110

In Proceedings of the International Conference on Agents and Artiﬁcial Intelligence (ICAART-2015), pages 100-110

ISBN: 978-989-758-074-1

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

for the calculation of Euclid distance, DTW distance

and DTW-D distance. Section 5 is dedicated to the

description of the experiments. We will describe the

experimental environments, experimental procedures,

experimental results and discussion. In Section 6, we

will discuss the conclusions and recommendations for

future works.

2 QUESTIONNAIRE SURVEY

FOR NEEDS

2.1 Questionnaire Result

We performed a questionnaire survey with pet own-

ers in order to investigate which actions of the pets

should be recognized by a remote pet monitoring sys-

tem. The questionnaire listed 22 typical actions of the

pets and the owners were asked to tell if they were in-

terested in knowing their occurrence when they were

away. Furthermore, a free-format comment ﬁeld was

provided to collect the reasons why the owners were

interested in knowing those actions. Figure 1 shows

the questionnaire results and Table 1 shows the com-

ments ﬁlled in the free-format ﬁeld. The action that

gathered the most interest from the owners is vomit-

ing. In addition, the questionnaire result and the free-

format description suggest that many owners are con-

cerned with the health condition of their pets.

2.2 Questionnaire Analysis

Let us focus on behaviors in which more than 70%

of the owners are interested. “Vomiting” and “shiver-

ing” directly reﬂect the health conditions of dogs, thus

their monitoring is desirable from the aspect of short-

term healthcare. “Coughing” can suggest respiratory

diseases if its frequency is unusually high and so its

monitoring is desirable from the aspect of long-term

healthcare. “Scratching” can lead to a serious dis-

ease if done repeatedly and therefore its monitoring

is also desirable from the aspect of long-term health-

care. “Barking”, “chewing”, “drinking”, “eating”,

“urinating”, “defecating” and “jumping” are poten-

tially problematic actions. “Barking” could make the

neighbors complain. “Chewing” may indicate dam-

age to the furniture. The problem with “drinking” and

“eating” is that a pet might eat or drink something that

the owner does not want it to. “Urinating” and “defe-

cating” could mean a blunder. With “jumping”, a dog

may try to take things at high places. As a result, the

eleven behaviors in which more than 70% of owners

are interested are related to the three aspects intro-

duced in Section 1.

Figure 1: Results of Questionnaire.

3 RELATED WORK

3.1 Activity Recognition for Dogs

There is a study on activity recognition (Ladha et al.,

2013). They use PCA-based feature extraction and

empirical cumulative density function (ECDF) (Ham-

merla et al., 2013). They sample acceleration data

at 30Hz. The acceleration data are divided into one-

second frames and each frame is analyzed separately.

A frame has 50% overlap with its predecessor and

is created with sliding window procedure based on

(Pl¨otz et al., 2010). Each frame is labeled using the

movie which is recorded by one annotator. The fea-

ture vector of each frame is trained and tested using

10-fold cross validation and is classiﬁed in each of the

16 actions and one rejection class using k-NN (k=1).

They show the result as a confusion matrix. In their

study, jumping in which 70% of the owners are inter-

ested is not recognizable. Furthermore, seven actions

are with less than 50% recognition accuracies in their

study, and recognition accuracy is less than 80% for

12 actions. Therefore, we must say that there is room

for improvement in the recognition accuracy.

ActivityRecognitionforDogsUsingOff-the-ShelfAccelerometer

101

Table 1: Comments in Free-Format Field.

How much is my dog relaxed?

What kind of facial expression does the dog have?

When the owner is away, what kind of action does the dog often take?

I keep some cats. I am not worried about the state of my house when I am away, because the cats usually sleep. When

I had a dog before, I was worried how the dog was doing. I think it depends on animal species.

It would be nice to talk to a dog at home via a mobile device, when the owner is away.

(Dog)

Showing the stomach.

Going around.

Running around as energetically as possible.

Shaking the tail buzzingly.

Excited with the sound that promises food items even if they are invisible.

(Cat)

Making rumbling sound at the throat.

Putting the face into a paper or plastic bag.

Climbing the curtain when excited.

Waiting at the door for a family member to come home.

Grooming.

The reaction to the sound of phone calls and intercom during the absence of the owner.

I am concerned if the dog gets into trouble while I am away.

My dog silently vomits without having a cough. That makes it difﬁcult for me to notice the vomiting instantly. I want

to notice abnormality as early as possible.

Because my dog is elderly, I am very interested in knowing the behavior of the dog during the absence of my family.

In addition, I am concerned if the dog does some action that leads to an illness.

I currently keep my cat in the room. When I go home, the room is so messy that I can imagine what the cat has been

doing.

Because the dog spends the daytime alone everyday, I leash the dog. So the range that the dog can move within

is narrow. Sometimes my dog can neither jump nor walk. But, I think if the dog spends time without doing any

mischievous act, there would be no need of the leash... [in order to realize the situation] it would be nice if the whole

of the dog’s behavior could be recognized.

I want to see how the dog behaved during the earthquake.

I want to know the action of the dog when it thunders during the absence of me and my family. Because the dog comes

to see me to the door when I go home, I want to know when the dog begins to move. Is it when I open the front door,

I stop the bicycle, or I open the gate?

3.2 Time Series Data Mining

3.2.1 Searching and Mining Trillions of Time

Series Subsequences under Dynamic Time

Warping

There is a problem of ﬁnding a subsequence that is

similar to a query sequence in a large scale time se-

ries data. The problem is solved by calculating the

distance between the sequences and the query using

Dynamic Time Warping (DTW). However, the com-

putational cost of DTW is high. As time series data,

that is to be searched, becomes longer, the number of

calculations of DTW increases linearly. As a result,

the computational time for the search becomes enor-

mous. The study proposed a method to solve the prob-

lem by eliminating unpromising candidates at early

stages.

3.2.2 DTW-D: Time Series Semi-Supervised

Learning from Single Example

Time series data with little up-and-downs tend to be-

come close to any data in DTW distance. Because

of this, a sequence with signiﬁcant temporal change

could be classiﬁed as data without one. In order to

avoid a situation like this, Chen et al. proposed a dis-

tance measure called DTW-D.

4 ALGORITHMS FOR

SIMILARITY CALUCULATION

Suppose there are two sequences X = hx

|i =

1, ..., Ni, Y = hy

| j = 1, ..., Mi. We would like to mea-

sure the distance between X andY in order to measure

the similarity of the sequences of X and Y in wave-

forms. The smaller the distance, the more similar X

and Y. Some distances are commonly used.

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4.1 Euclidean Distance

Euclidean Distance (ED) is the classic scale for mea-

suring the similarity among the time series data. It is

measuring the distance between the time series data

of the same length. It is determined by summing up

the distances between the data at the same time index.

ED =

∑

i=1

− y

)

, (N = M) (1)

Figure 2 is a ﬁgure of alignment of the Euclid dis-

tance.

Figure 2: Alignment of the Euclid distance.

4.2 Classical DTW

A weak point of the ED is that it tends to be large

when the time series data are out of phase. Dynamic

Time Warping is a technique used to ﬁnd distance

more ﬂexibly between time series data than ED. Fig-

ure 3 shows alignment of the data points that is used

to calculate the DTW distance.

Figure 3: Alignment of the DTW distance.

At ﬁrst, we calculate cost matrix C ∈ R

N×M

which is

deﬁned by the distance between each element using

equation (2). An example of the cost matrix is shown

in Figure 4.

C(i, j) := c(x

, y

) = abs(x

− y

(i = 1, ..., N, j = 1, ..., M) (2)

Then, we calculate accumulated cost matrix AC us-

ing equation (3). An example of the accumulated cost

matrix is shown in Figure 5.

Figure 4: Cost Matrix.

Figure 5: Accumulated Cost Matrix.

AC(i, 1) =

∑

k=1

c(x

, y

)

AC(1, j) =

∑

l=1

c(x

, y

)

AC(i, j) = C(i, j) + min{AC(i− 1, j− 1),

AC(i− 1, j), AC(i, j − 1)}, (i, j ≥ 2) (3)

DTW distance is AC(N, M). We have shown the

above-mentioned algorithm in Algorithm.1.

4.3 DTW-D

Let us consider to calculate the distance between each

sequence R, G, B in Figure 6. If we use ED measure,

the alignment of data points will be similar to that in

Figure 7. On the other hand, if we use DTW measure,

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Algorithm 1: Calculate DTW Distance.

Input: sequence X = hx

|i = 1, ..., Ni,

Y = hy

| j = 1, ..., Mi

Output: DTW Distance AC(N, M)

1: /*Calculate Cost Matrix*/

2: for i = 1 to N do

3: for j = 1 to M do

4: C(i, j) ⇐ abs(x

− y

)

5: end for

6: end for

7: /*Calculate Accumulated Cost Matrix*/

8: AC(1, 1) ⇐ C(1, 1)

9: for i = 2 to N do

10: AC(i, 1) ⇐ AC(i− 1, 1) +C(i, 1)

11: end for

12: for j = 2 to M do

13: AC(1, j) ⇐ AC(1, j− 1) +C(1, j)

14: end for

15: for i = 2 to N do

16: for j = 2 to M do

17: AC(i, j) ⇐ C(i, j) + min{AC(i − 1, j −

1), AC(i− 1, j), AC(i, j −1)}

18: end for

19: end for

20: return AC(N, M)

the alignment will be like that in Figure 8. In both

cases, the counter-intuitive result that G is more simi-

lar to R than B is, will be derived, as shown in Figure

In order to avoid a situation like this, Chen et

al. (Chen et al., 2013) proposed a distance measure

called DTW-D. DTW-D is calculated by equation (4)

where ε is a small positive constant placed in order to

avoid the division by zero. As shown in Figure 10,

based on DTW-D, B is more similar to R than G is.

DTW−D(x, y) =

DTW(x, y)

ED(x, y) + ε

(4)

Figure 6: Three sequences to calculate distance.

Figure 7: alignment of the ED between R and B, R and G.

Figure 8: alignment of the DTW distance between R and B,

R and G.

Figure 9: ED and DTW Distance in the three Sequence [R,

G, B].

Figure 10: DTW-D distance is the distance that DTW dis-

tance divided by ED.

5 EXPERIMENTAL PROTOCOLS

5.1 Experimental Environments

The experimental subjects and environmentsshown in

Table 2. The conditions of the experiment are as fol-

lows. The acceleration data was collected at sampling

frequency 25Hz. The video was recorded in order to

put the ground truth label. We prepared the accel-

eration sensor shown in Figure 11 on the left side.

The sensor was attached to the neck of the dog as

shown in the Figure 11 on the right side. The ac-

celeration sensor which we used for the experiment

is AX3 Watch of Axivity (Axivity Ltd., 2011). The

sensor is equipped with 3 axes MEMS which works

as an accelerometer. Sampling frequency can be se-

lected from several predetermined values provided by

the tool between 12.5Hz and 800Hz. It has a mounted

NAND ﬂash memory of 512MB to store the data. The

maximum recording time is 14 days at 100Hz, and 30

days at 12.5Hz. Measurement range of the acceler-

ation is ±16g. This sensor has IP68-rated dust- and

water-proof capability that is standardized by Interna-

tional Electrotechnical Commission 60529. Acceler-

ation data is transferred to the PC from the sensor by

using USB. We have shown the deﬁnition of the activ-

ity of the dog in Table 3. Figure 12 shows appearance

and dimension of the subjects.

5.2 Experimental Procedures

5.2.1 Procedure of Our Approach

Using ELAN (MPI for Psycholinguistics, 2013),

which is a tool for video annotation, every sample of

the acceleration data is labeled. The label consists of

one of 10 activities shown in Table 3. “Unspeciﬁed”

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Table 2: Information of the experimental subjects and envi-

ronments.

Name Yuzu Oreo

Breed Pembroke

Welsh Corgi

Toy Poodle

Sexuality Female Male

Age 4-year 8-month

Weight 10.7kg 2.7kg

Body Length 60cm 43cm

Body Height 30cm 30cm

Room Outdoors

Environment (Wooden

Flooring)

(Paved Road

or Ground)

Table 3: Deﬁnition of Behavior (the number of DTW-D

frames & PCA-based frames shown in parentheses).

walking: (2781 & 216 frames)

Walking. Movement of the left and right limb is

alternating and not aligned.

eating: (5223 & 401 frames)

Put food in the mouth, and swallow.

sitting: (4140 & 319 frames)

Sitting quietly with buttocks on the ﬂoor or

ground.

laying: (5746 & 442 frames)

Lying down and put his head against a ﬁxed object

such as a ﬂoor or ground.

snifﬁng: (370 & 29 frames)

Snifﬁng the smell of the ﬂoor or ground.

running: (961 & 74 frames)

Running. Movement of the left and right limb is

almost aligned.

jumping: (660 & 50 frames)

Foot of all is away from such as a ﬂoor or ground.

drinking: (1068 & 82 frames)

Drinking such as water from the dish on the ﬂoor

or ground.

shaking: (670 & 50 frames)

Shaking itself to shake off the water.

scratching: (47 & 5 frames)

Scratching eyes by foreleg. Scratching the front

side from the chest by hindleg.

label is given to the behavior that cannot be judged as

one of the 10 activities.

Let us call a subsequence of 25 samples a frame.

A new frame is created by sliding the 25-sample win-

dow forward by one sample. As a result, adjoin-

ing two adjoining frames shares 24 samples of each

other, that is 96% of the frames. When the same

ground truth label appears in more than 20 samples in

a frame, that is 80% of the frame, the whole frame is

given the ground truth label. This is because, average

F-measure became maximum at 80% in 52%∼100%.

Figure 11: The appearance of the sensor and how the dog

wears it.

Figure 12: Size of dogs. (left) Oreo, (right) Yuzu.

Otherwise, the frame is labeled “mixed” and used for

the test and training frames, but that frame is not listed

in a result. We choose one frame from all frames and

set it aside as a test frame. Remaining frames is as-

sumued to be training frames. The test and training

frames are chosen so that there are no shared samples.

The distance between the test and training frames is

calculated using each of the Euclidean, DTW, and

DTW-D methods. We infer a label of a test frame

from the label of the training frame nearest to the test

frame. In other words, we used the nearest neigh-

bor method. This is because, in a preliminary study

comparing performance of k-nearest neighbor meth-

ods for the data, average F-measure became maxi-

mum at k = 1 in k = 1, 3, 5. Recognition accuracy is

calculated through cross validation. Overview of the

experimental setting is shown in Figure 13.

5.2.2 Procedure of Existing Approach

The existing PCA-based approach was also applied

to our data set. Each parameters were chosen to be as

close as possible to the existing study. A new frame

is created by sliding the 25-sample window forward

by 13 samples. As a result, adjoining two adjoining

frames shares 12 samples of each other, that is 48% of

the frames. When the same ground truth label appears

in more than 19 samples in a frame, that is 76% of the

frame, the whole frame is given the ground truth la-

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Table 4: Precision, Recall and F-measure at ED, DTW Distance and DTW-D Distance of 2 dogs.

Table 5: Precision, Recall and F-measure at ED, DTW Distance and DTW-D Distance of Yuzu.

Figure 13: Overview of the experimental setting.

bel. We choose one frame from all frames and set it

aside as a test frame. Remaining frames is assumued

to be training frames. In some cases part of the test

frame and training frames have 48% overlap. Each

frame is normalized by inverse ECDF. We projected

them into ﬁrst 25 principal components in order to

reduce the dimension of the feature of the frames.

The distance between the feature vectors of the test

frame and training frames are calculated. The label

of the test frame is estimated using nearest neigbor

method. Recognition accuracy is calculated through

leave-one-out cross validation.

5.3 Experimental Results and

Discussion

5.3.1 Analyses of the Result of Our Approach

Table 4 and Figure 14 show accuracies and confusion

matrix of our approach obtained through the exper-

iments using data of the two dogs. Table 5 and Fig-

ure 15 show accuracies and confusion matrix obtained

through the experiments using data of Yuzu alone. Ta-

ble 6 and Figure 16 show accuracies and confusion

matrix obtained through the experiments using data

of Oreo alone. In Table 4, 5 and 6, a cell is marked

red if the corresponding distance measure gives the

best result among the three measures.

According to Figure 4, the DTW-D has yielded

high F-measures as compared to the DTW and ED.

Using DTW-D, subtle differences in the actions of

the dog are recognized more precisely than using the

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Table 6: Precision, Recall and F-measure at ED, DTW Distance and DTW-D Distance of Oreo.

Figure 14: Confusion Matrix of Euclidean Distance, DTW Distance and DTW-D Distance of 2 dogs.

Figure 15: Confusion Matrix of Euclidean Distance, DTW Distance and DTW-D Distance of Yuzu.

DTW and ED. With DTW-D, it is expected that the

accuracy of activity recognition will be stable even

when the number of actions to be recognized in-

creases. If an action appears only in a part of a frame,

such as jumping, it is difﬁcult for a statistical method

to detect the difference in the feature value from other

actions.

It can be explained that a few samples with signif-

icant feature can be obscured by many ordinary sam-

ples in a statistical method. On the other hand, the

methods that calculate the similarity of waveforms,

such as DTW, are able to detect the difference result-

ing in superior accuracy.

In “drinking”, F-measure of DTW-D is lower than

F-measure of ED and DTW. The reason is because a

part of “eating” frames are estimated to be “drinking”

frames as shown in Figure 14. “Scratching” has re-

sulted an extremely low F-measure. There are only

47 frames of “scratching” in the data. The recall can-

not be good simply because of the shortage of the

data. The resulted few true positives are further over-

whelmed by vast amount of false positives, yielding

the poor precision. This can explain the remarkably

poor F-measure.

The F-measures from the data of the two dogs

roughly falls between those of Yuzu-alone and Oreo-

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107

Figure 16: Confusion Matrix of Euclidean Distance, DTW Distance and DTW-D Distance of Oreo.

alone. However, there are some behaviors that has re-

sulted very poor F-measures in the one-dog data, such

as “shaking” for Yuzu and “sitting” for Oreo. Improv-

ing them is a challenge for the future.

5.3.2 Comparison with the Existing Approach

Table 7 and Figure 17 show accuracies and confusion

matrix of the existing approach obtained through the

experiments using data of the two dogs. Comparing

this with Table 4 and Figure 14, it can be said that our

approach has resulted higher F-measures than the ex-

isting approach. This could be explained by a theory

that the amount of data might be too small to perform

the statistical feature extraction. We also think that

valuable information of the data could have been lost

by the interpolation used in the existing approach.

6 CONCLUSIONS AND FUTURE

WORKS

6.1 Conclusions

As seen in the emergence of commercial services that

recognize simple behaviors of the dogs and to record

them as life log, the desire to record the behavior of

the dog has been increasing. However, the activity

recognition ability of the services is limited and the

need to record more detailed actions will arise in the

future. We have investigated the needs and analyzed

what kind of actions of the dog should be recognized

by a pet monitoring system. As a result, we have

found that there are three aspects of the demand for

pet activity monitoring, namely, short- and long-term

healthcare and problematic behavior. The action that

the owners wanted to know the most was “vomiting”.

Furthermore, we have found that there are approxi-

mately 70% of the owners who would like to moni-

Table 7: Precision, Recall and F-measure at the existing

study.

Figure 17: Confusion Matrix of the existing study.

tor “jumping”, whose recognition accuracy was low

in the existing study (Ladha et al., 2013). We ob-

served that the reason behind low recognition accu-

racy of “jumping” was that they used statistical tech-

nique in the study. In addition, in our data set, our

approach has higher F-measure than the existing ap-

proach. Therefore, we focused on the waveform of

time-series data. We applied DTW-D, which is a

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method to measure the similarity of the waveform, to

activity recognition in dogs for the ﬁrst time. As a

result, recognition accuracy for “jumping” is particu-

larly improved as compared to the previous study.

It can be said that it is difﬁcult for a statistical

method to differentiate an action which appears only

in a part of a frame, such as jumping. On the other

hand, methods that calculate the similarity of wave-

forms such as DTW perform well for the actions and

result better recognition accuracy. “Vomiting” which

is the most desired action to be monitored, is also a

brief action. We are optimistic to detect it better with

DTW-D.

6.2 Future Works

6.2.1 Measurement of Heart Rate and

Respiratory Rate at Rest

By measuring the respiratory rate and heart rate at

rest, it is possible to detect the heart or lungs diseases

at early stages. It also makes it possible for dogs to re-

ceive the appropriate treatment by a veterinarian. For

human beings, there is a study by Poh et al. (Poh

et al., 2011). However, because this study measures

the transition of the reﬂection of light in the skin, ap-

plication of this method to dogs with lots of hair is

difﬁcult. Therefore, we think that the measurement of

heart rate by acceleration sensor is effective.

6.2.2 Pet Location Monitoring in a Room

Whether the behavior becomes problematic or not de-

pends on the place where the pet is kept. If the de-

tailed position of the dog in the room was available,

it would further enhance the usefulness of the activity

recognition. The research of Paasovaara et al. (Paaso-

vaara et al., 2011) could be a hint. Their study pro-

posed the concept of human-dog interaction with so-

cial media. They planned to use a RFID device for

indoor position detection as one of human-dog inter-

actions.

6.2.3 Improvement of Recognition Accuracy

There are actions whose accuracy is low in this re-

search and the existing research. We think that further

improvement in accuracy becomes an issue. Many

small sensors are available now. By the analysis of

the behavior with low recognition accuracy, it can be

decided what kind of sensor needs to be added. Cur-

rently, we are focusing on using sound. We want to

improve the accuracy by adding microphone as a sen-

sor in future.

6.2.4 Further Inspection of the Validity of Our

Approach

We cannot say that our approach has been sufﬁciently

validated by experiments shown in this paper, both in

terms of the number of individual dogs and the vari-

ety of breeds. Ultimately we would like to have higher

F-measures for any unknown dogs. However, as the

ﬁrst step, we will carry out an experiment using many

dogs of the same breed and do cross validation be-

tween individuals to verify the robustness of the ap-

proach among the same breed.

ACKNOWLEDGEMENTS

This work was supported by JSPS KAKENHI Grant

Numbers 24300005, 26330081, 26870201.

In performing this study, We would like to thank

everyone that has helped us questionnaire survey.

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