International Journal of Informatics Society, VOL.12, NO.1 (2020) 59-67

59

Industrial Paper

Development of a Life Watching Service for Elderly People by using Interactive
Home Robot
Keiichi Abe*, Hiroki Fuse**, Makiko Okumura*, and Masao Isshiki *****Kanagawa Institute of Technology, Japan
Graduate School of Engineering, Kanagawa Institute of Technology, Japan
abe@he.kanagawa-it.ac.jp

Abstract - Since the life watching system using the existing
voice interactive robot uses a camera, there is a problem thatsome users feel mental stress from the constant monitoringby the camera. Therefore, in this research, we propose aservice to watch the life of a target person without using any
camera by using a voice interactive home robot for the life
watching target including the elderly living away from theirfamilies. Specifically, by recording and analyzing information such as conversations between the watching target
and the home robot as a log, we examined whether it could
be applied as a life watching service equivalent to a camera.
We developed a trial system of the life watching service
using an interactive home robot proposed in this paper andevaluated by comparing it with a camera.
Keywords: Communication Robot, Life Watching Service,
ECHONET-Lite.

1

INTRODUCTION

In Japan, the number of elderly people living alone is increasing with the declining birthrate and aging population.
Under such circumstances, life watching services for theelderly [1]-[2] are attracting public attention. There aremany services [3]-[4] that use robots for the watching. The
basic functions of the existing watch robots are communication, and various functions such as telephone functions andschedule functions are implemented. Many of these robots
use cameras for the watching. A robot equipped with a camera [3]-[4] can watch the life of the target person with video,
but it cannot watch the target person when he/she is in aroom where the robot is not installed or within a blind spotof the camera. There is also a possibility that the image may
be diffused on the net by being hacked. Also, the subjects
may have multiple problems such as the mental stress due tothe constant monitoring with a camera [5].
Therefore, in this study, we propose a service to watch thelife of a target person without using any camera by using avoice interactive home robot for the life watching target
including the elderly living away from their families. Specifically, we examined whether it could be applied as a newlife watching service by analyzing a log recording the talks
between the watching target and the home robot. What isessential for such a system is how to increase the chances of
conversation with the subjects of the life watching including
the elderly. Therefore, the dialogue type home robot for life
watching that we developed this time starts talking whensomeone is detected by the human sensor etc. The robot also
produces facial expressions and movements such as "nod-ding" and "swinging" when talking. We will describe howsuch a method helps to increase the chances of conversationin an easy-to-talk manner. The prototype development andverification evaluation of the interactive home robot proposed by this research is carried out, so the details will be
described. In this paper, Chapter 2 describes related works
and issues, Chapter 3 is an outline of the life watching service using the interactive home robot proposed by this research, Chapter 4 relates to prototype development, Chapter
5 is evaluation by demonstration experiment of prototype,
Chapter 6 is questionnaire evaluation on comparing prototype and AI speaker, Chapter 7 presents the conclusion, andChapter 8 is concerned with future works.

2

RELATED WORKS AND ISSUES

Existing technologies for home robots can be divided into
two types: interactive and non-interactive. Examples of interactive home robots include Pepper [6] and RoBoHoN [7].
In addition, there is LAVOT [8] developed by GROOVE X
as a non-interactive home robot. LAVOT is a robot thatcannot communicate with the user, but can express a warm
feel and cute gestures like a pet. On the other hand, in thecase of interactive home robots such as Pepper and RoBoHoN, their appearance is designed to be familiar to general users. In addition, by adding a dedicated application anddeveloping an original application, it is possible to enter aloving nod or gesture, recognize a person with a camera, etc.,
and have a voice conversation with a specific person. These
home robots such as Pepper [6], RoBoHoN [7], and LAVOT[8] are all equipped with cameras, so they can identify thetarget person or monitor the situation in the house with thecameras. It is possible to perform advanced monitoring,such as sending the captured images by e-mail to the users.
However, when using a camera-equipped robot, some target
persons feel mental stress due to the constant monitoring by
a camera. Also, if the system is hacked, there is a possibility
that the life situation of the target person will be completely
viewable, and there are security and privacy issues with thecamera-mounted type. In this study, we propose a service
that uses an interactive home robot to monitor the user's life
only by analyzing log data without using any camera.

3 LIFE WATCHING SERVICE USING INTERACTIVE HOME ROBOT
Chapter 3 gives an overview of a life watching service

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K. Abe et al. / Development of a Life Watching Service for Elderly People by using Interactive Home Robot

Figure 2: System of IHR
Figure 1: Overview of life watching service using IHRusing the interactive home robot (hereinafter referred to as“IHR”) proposed by this paper and details of the prototype
IHR developed this time.

3.1

Outline of life watching service using IHR

Fig. 1 shows the outline of the life watching service using
IHR proposed by this paper. This service has an implemented function to record the contents of the conversations between IHR and target person as a log. Log data is saved as afile in CSV format and analyzed by a control program of theinteractive home robot and used for the life watching. IHR's
natural conversation is realized by combining Google
Speech API of speech recognition service and chat conversation API of NTT docomo [9]. Classification numbers areprovided according to the contents of the log data acquired
by IHR (conversation, home appliance operation, etc.), andIHR estimates the life behavior of elderly people living
alone by graphing the data in time series etc. Using thosefunctions, we devised a system service to monitor life bycomparing the living condition data with those acquired in
the past. The final analysis results of life surveillance aretransmitted to the families and relatives living in remoteareas by e-mail. The IHR is designed for use in a smart
house [10] where home appliances and sensors compatible
with ECHONET-Lite are connected to the home network.

4
4.1

PROTOTYPE DEVELOPMENT OF IHRHardware of IHR

In developing the IHR, we referred to the Magbot [11]devised by Professor Koike and modified it to enable naturalconversation with the user freely. Fig. 2 shows the system
configuration of the IHR developed this time. As shown in
Fig. 2, RaspberryPi3 B + (hereinafter referred to as RPI) is
used as the main MCU (Micro Control Unit) of IHR, anddistributed processing is performed using the Arduino microcomputer as the sub MCU. RPI is in charge of the IHRmain processing, performs communication processing with
ECHONET-Lite compatible home appliances, etc., performs
speech synthesis processing and communicates with various

Figure 3: Flowchart of natural conversation processing of IHR
network services (Google Speech API and docomo’s chatconversation API), etc.The Arduino microcontroller performs sub-controls such as the light emission of five LEDs
that appear to be the face of the IHR and the swing motionof the robot by a servomotor. Therefore, the Ardunio microcontroller controls the part of the IHR facial expression andmovement. In addition, a human sensor (Model TypeEKMB1301111K / Panasonic) is installed on the body of the
IHR to enable the IHR to actively talk to a user near the IHR.
The sensor circuit is designed to react to a person within 300
mm of detection distance. Therefore, the proposed IHR operates only when a human movement is detected, and a program that can record the result as a log in the IHR file is
implemented in the RPI. The main program of this IHR RPI
was developed in Python 2.7 language, and the processing
program on the Arudino microcomputer side was developedin C language.

4.2

Mechanism of natural conversation

In order to allow the target person to talk naturally with the
IHR, we have used Google Speech API for speech recognition service and docomo’s chat conversation API for naturalconversation service for our IHR. Fig. 3 shows the flow of
the natural conversation processing implemented in the IHR[9]. The IHR acquires the user's voice information with themicrophone connected to the RPI, and first creates a voice
input file. It performs speech recognition processing thatconverts speech input files into text information using theGoogle Speech API. The converted speech text informationis sent to docomo's Zatudan-API, which creates text information containing natural speech information. This text in-International Journal of Informatics Society, VOL.12, NO.1 (2020) 59-67

61

formation is speech-synthesized using Open JTalk preparedin the IHR, and the obtained voice conversation is outputtedfrom the speaker connected to RPI. In this manner, the natural conversation between human and the IHR has been realized.

4.3

IHR facial expression

Fig. 4 shows the face of the IHR developed this time. The
robot's face was made of a breadboard, five LEDs, and fiveresistors. We arranged two large-size LEDs side by side,
protruding as the eyes, and three LEDs located below thecenter as the mouth. Various expressions were created bylighting and blinking each LED as shown in Fig. 4 as needed in the conversation. In practice, a translucent plastic mug
was placed over the breadboard to prevent the circuit frombeing exposed. When talking with people, IHR lights its
eyes and mouth, and produces a swinging motion with the
DC servomotor so that it looks friendly and is easy to talk to.

4.4

Figure 4: Facial expression of IHRConversation log recording function

IHR has a function to keep a log of the talks of the IHRwith the target person of the life watching. In order to distinguish between daily conversations and household appliance operation requests as the conversation content, classification numbers are provided to make it easy to extract necessary data using computer programs. The classification
number is 1 for daily conversation, 2 for household appliance operation request, and 10 for no conversation. This "noconversation" is written as a log to a file inside the IHR
when nobody is detected by the human sensor and there isno conversation.

4.5 Life watching method by conversation log
analysis
By analyzing the log data acquired by the IHR using a
program or application, the life rhythm and behavior of thewatched person can be estimated to some extent. For example, a set of log collected over a long period can be treatedas reference data of the living behavior of the target person.
Comparing the latest data with this reference data makes it
possible to detect a continuation of an abnormal situationand notify relatives and families living in remote areas ofthe situation by e-mail etc. For example, if the state without
conversation has continued for half a day or more during thetime when such conversations are normally expected, it ispossible to determine that the life behavior is different fromusual, so the IHR notifies the family and relatives of thesituation by e-mail etc. with a message such as "Please call
Mr. OO/Mrs. OO," to prompt them to contact the watchingtarget. The e-mail transmission is performed by the main
program of RPI, and realized by the standard library of python.

Figure 5: Appliance operation and information service by keyword of 2 words

4.6 Acquisition of living information and appliance operation in the smart house
The IHR developed this time uses ECHONET-Lite [12] asa communication protocol for acquiring living informationand operating home appliances in the smart house. Bycommunicating with smart meters and home appliances inthe smart house, the IHR is able to acquire the power consumption of the entire home, acquire the ON / OFF status ofvarious home appliances, and remotely operate home appliances. Also, when the user asks time, weather, etc., the IHR
acquires necessary data in JSON format from the time [13]or weather web service site [14], and outputs it as voicefrom IHR. The IHR developed this time was designed toremotely turn on / off a home appliance with the words corresponding to the keyword or to provide the weather andtime information when the corresponding keywords are included in the conversation. Fig. 5 shows an example of theremote control of the home appliances and information provision of the weather and time by keywords. For example, if
you ask the IHR to turn on the Cooler in the room, there is apossibility that the word “Cooler” may be misrecognized.
Therefore, the IHR is programmed to remotely turn on the“Cooler” only when the two words "Cooler" and "ON" havebeen recognized found. If you want to hear the weather information, the IHR provides today's weather information byvoice only when the two words "Today" and "Weather"have been recognized.

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K. Abe et al. / Development of a Life Watching Service for Elderly People by using Interactive Home Robot

Software of IHRFig. 6 shows the flow chart of the main software implemented in the prototype IHR. A pyroelectric infrared sensor
is used as the human sensor. When the motion of a person isdetected by the human sensor, the robot starts talking, andwhen the person is absent, the IHR does not speak. Upon
receiving an input of human voice through the microphone,
the IHR creates a voice file and sends it to Google Speech
API to convert the audio information into text information.
If there is no keyword related to home appliance operationsin this text information, the IHR checks whether there is a
keyword related to time or weather. If there are no keywords
related to time or weather, the text information is passed to
docomo's chat service and converted to text information for
natural conversation. The text information is converted tospeech information using the speech synthesis software
Open JTalk, and speech is output from the speaker to realize
natural daily conversation between a human and a robot [9].
Also, if the 2-word keyword of the home appliance has beenrecognized in the acquired voice information, the target
home appliance compatible with ECHONET-Lite connectedto the network is remotely operated and the result of theoperation is presented to the user. Also, if there is a keyword
related to time or weather, the IHR acquires informationfrom the dedicated site and provides it to the user by voice.

Figure 6: Main software flow chart of IHR

5EXPERIMENT

An experiment using the IHR prototype developed thistime was conducted to demonstrate how accurately the living behavior of a subject can be analyzed from the log dataacquired by the IHR.

5.1

Experimental method

This demonstration experiment was conducted at E602 on
the 6th floor of Kanagawa Institute of Technology C2. The
subject was a 20-year-old male, and the experiment period
was from May 14, 2019 to May 20 except Saturday and
Sunday. Fig. 7 shows the system of the demonstration experiment, and Fig. 8 shows the layout of the demonstration
experiment.
Using the IHR prototype developed this time, we asked
the subject to behave as usual, and recorded informationsuch as the subject talking with the IHR or remotely operating home appliances via the robot as log data. The recorded
information was analyzed to evaluate the accuracy of humanbehavior estimation. A camera was installed to acquire correct data in the demonstration experiment. Fig. 9 shows apicture of the IHR and the subject during the demonstration
experiment taken with the camera. Table 1 shows theequipment used in the demonstration experiment. As shownin Fig. 8, the two appliances used this time were an ECHONET-Lite compatible Cooler and lighting. The subject entered the room and had a conversation with the IHR at any
time. During the demonstration experiment, the camera captured moving images of the IHR and the user's conversation
situation along the time display. The correct data was created manually while recognizing the content and time of theaction of the subject from the moving image of this camera.
After the demonstration experiment, the reliability of the
IHR log data was evaluated by comparing the log data recorded by the IHR with the correct data obtained from thecamera's moving image. Also, as shown in Fig. 9, the subject sat at his desk and talked to the IHR only when necessary. The subject was asked to take action with a natural
feeling, such as requesting the IHR to turn on the Cooler
when it is hot, or to turn on the lighting when the room isdark. As shown in Fig. 8, the distance between the IHR and

Figure 7: Experimental system

International Journal of Informatics Society, VOL.12, NO.1 (2020) 59-67

Figure 8: Layout of Experimental (Top View)

Figure 9: Demonstration experiment state from camera
Tabel.1 Equipment used63

the subject was 300 mm, while the distance between the
Cooler or lighting and the subject was 1900 mm.
Fig. 10 shows log data acquired by the IHR. The log data
was saved in CSV format in order of date, time, conversation content, and classification number. This classification
number is an optional item provided so that information canbe easily extracted later by a database or Excel.
The classification number is 1 for conversation and 2 forhome appliance operation and 10 for no conversation. In
addition, the number of conversations of the subjects and thenumber of times the home appliances were operated wereobtained from the log data obtained by the IHR every 30
minutes. In addition, the number of conversations and thenumber of home appliance operations performed by the subject in 30-minute units from the images taken by the camera
were used as the correct answer values. The error and error
rate were obtained by equations (4.1) and (4.2) from the log
data obtained by the IHR and the correct answer values obtained by the camera.

NClog is the number of conversations captured in the log or
number of appliance operations captured in the log. NCcam isthe number of conversations acquired by the camera or
number of appliance operations acquired by the camera.

5.2

Figure10: Log data acquired by IHR(CSV format data)

Results and discussion

Fig. 11 is a graph in which the log data obtained in thedemonstration experiment from 14 May to 20 May, 2019
excluding Saturday and Sunday and the correct answer dataacquired by the camera are summarized as the number ofappliance operations and the number of conversations in 30
minutes.
The horizontal axis is the time axis, and the vertical axis is
the number of conversations and home appliance operations.
The upper graph is the graph acquired by the IHR, and thelower graph is the correct data obtained by the camera. In
addition, Table 2 shows the correct answer rate of the number of conversations and the correct answer rate of the number of operations of the home appliance. As shown in Fig.
11, the number of conversations in the log acquired by the
IHR is larger than in the correct answer data acquired by thecamera. Next, Table 2 shows the error and error rate of thenumber of conversations with the user and the number oftimes the home appliances were operated. The number ofconversations acquired by the IHR has an error of 2 to 25
times compared with the camera, and it can be seen thatwhen converted to the error rate, it is up to 82%. A possible
reason for this was that the IHR unnecessarily responded to
the voices of individuals who were there other than the subject during the demonstration experiment, which led to anincrease in the number of conversations. Therefore, the error
in the number of conversations in Table 2 can be consideredas the number of conversations other than the subject. On
the other hand, the error rate of the number of home appli-

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K. Abe et al. / Development of a Life Watching Service for Elderly People by using Interactive Home Robot

Figure 11: Number of conversations and appliance
operations from 14-May-2019 to 20-May-2019（The upper graph shows IHR log data. The lower
graph shows camera data）
ance operations was zero except for the data on May 15th,
indicating that log information was obtained accurately.
Next, referring to the 5-day data in Fig. 11 in chronologicalorder, the time zone for conversation, the type of conversation, etc. are almost synchronized with the correct data takenwith the camera. Therefore, IHR's log data can be used toestimate the subject's behavior as well as the camera's correct data. The working hours of the subject this time are
11:00 to 16:00, but it is possible to estimate the early departure and overtime hours etc. depending on the day of theweek by combining with the subjects' action memos in Fig.
11. It will be also possible to accurately estimate the time
when the subject left for lunch. With 14-May and 16-May
data, it is possible to clearly read the time spent leaving forlunch because there is no conversation with the IHR or theoperation of home appliances, but with 15-May data it cannot be judged whether the subjected was absent, because
there is a conversation recorded. As we summarized the datain units of 30 minutes this time, an error of ± 30 minutes
may occur depending on the time zone as compared with thecorrect data as shown in the graph of 17-May in Fig. 11. For
this reason, it is difficult to estimate the exact leaving timeetc. when the subject leaves for a short time at lunch etc. in
the analysis in units of 30 minutes. Table 3 compares theactual working hours with the working hours estimated fromthe log data acquired by the IHR. In this result, it is within

about ± 15 minutes, but it can be expected that the error will
deviate by about 30 minutes if the room entry time is before
11:00, for example. Therefore, the accuracy of the living
activity estimation using the proposed IHR depends on theaggregation interval of logs. In other words, if the log data isaggregated every 30 minutes, it is thought that the daily activities such as entering, leaving and working hours will
have an error of about ± 30 minutes. Also, since it is possible to determine whether the conversation content with the
IHR is a daily conversation or an operation of a home appliance, it is considered that it is possible to extract a subjectspecific life behavior pattern from these kinds of information. Next, in order to analyze the subject's specific behavior, Fig. 12 shows the number of conversations with the
IHR and the number of home appliance operations in eachtime zone from 5/14 to 5/20. From this graph, it seems to bepossible to find out the time zone where there are many
conversations with the IHR and the time zone where there
are many times to operate home appliances. However, it isactually difficult to locate such time zones at a glance inthose graphs. Therefore, as shown in Fig. 13, we tried theaverage value of the number of conversations with the IHR
and the number of operations of home appliances for eachtime slot from 5/14 to 5/20. The red line in Fig. 13 shows
the average value of the number of conversations and thenumber of operations of home appliances from 11:00 to
16:00. The time zone where the bar exceeded the red line
indicating the average value in Fig. 13 was considered to be
the time zone where the subject talked with the IHR andoperated the home appliance frequently.
As shown in Fig. 13, when the time zone exceeding the
red line is extracted, the time zone in which the subject talks
frequently with the IHR is 11:30, 12:30, 14:00, 15:00, 15:30.
By comparing this extraction result with that in Fig. 12, it ispossible to extract a high frequency time zone as a whole,although it includes a day without conversation and home
appliance operation. In this way, subject-specific behavior
can be analyzed from log data analysis acquired by the IHR.
Moreover, it can be used as a judgment standard of the life
watching for determining whether the life is ongoing as usual by comparing the data obtained through this behavior
analysis and the log data of the latest value.

Table.2 Comparison of Error of log data vs camera(correct data)

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Table.3 Comparison of actual working time vs working time obtained from log data

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6

QUESTIONNAIRE EVALUATIONThe IHR proposed by this study is a technology that does
not have a camera at all, but performs natural conversation
based on voice information from users, voice control ofhome appliances, and watching life by collecting log data.
There is also a technique using AI speakers [15]-[16] as arelated technology for voice interactive system without
cameras. Most of the commercially available AI speakers
have an inorganic design, so it seems that there are a certain
number of people who find it difficult to talk to. Therefore,
we aimed to develop an IHR without cameras that is not a
simple AI speaker but can also express non-verbal information such as facial expressions and movements [17] likehuman beings in a rich manner and that can perform dialogue by voice.
We thought that IHR would be useful in a life watching
service if it could increase the opportunity of conversation
with the user. Therefore, in order to evaluate whether thedesign of the IHR proposed in this study makes conversationeasier than AI speakers, we conducted a comparative evaluation questionnaire using the IHR vs AI speaker. As for the
implementation method, we asked the public to see ademonstration of the IHR prototype we developed and an AI
speaker. A questionnaire was conducted on 29 men andwomen in their teens and 60s. Fig. 14 shows the age andgender of the questionnaire respondents. By age group, 20s
and less than 20s occupy 60% of the whole. Fig. 15 shows
the answer to the question, “Which of the IHR or AI speakers did you think is easier to talk to?” As a result, about 80%of the respondents answered that the IHR we developed waseasier to talk to than AI speakers.

Figure 12: Number of conversations and appliance
operations by each time zone from 14-May-2019 to
20-May-2019
Figure 14: Age and gender of questionnaire respondents

Figure 13: Average number of conversations andappliance operations by each time zone from 14-May2019 to 20-May-2019

Figure 15: Questionnaire on comparing IHR and AI
speaker

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K. Abe et al. / Development of a Life Watching Service for Elderly People by using Interactive Home Robot

CONCLUSION

In this paper, we proposed a service that uses an IHR tomonitor the life of a target person without using a camera at
all for those who want to monitor the lives of their target
persons including the elderly, who live away from their families. Specifically, by increasing the chances of conversation
between the IHR and the target person, it was possible tocollect a large amount of log information, so that the accuracy of the target person's behavior estimation was improved,
and as a result, we thought that it could be used as a life
watching service. The prototype of the IHR was developed,
and the reliability of the data acquired by the log file was
evaluated by comparing the log file acquired by the IHRwith the result of the moving image acquired by the camera.
As a result, it became clear that when there were multiple
people other than the subjects, the voices of those other people were picked up, and the number of conversations counted in the log data of the IHR was larger than the actual
number. However, it was found that it was possible to estimate the behavior at which time the subject talks with the
IHR well and operates home appliances. In addition, it was
found that the estimation accuracy of the living activities
such as entering, leaving, and working hours depends on theaggregation interval of IHR log data. In addition to this, it ispossible to determine whether the contents of the log information acquired by the IHR is a daily conversation or an
operation of a home appliance, so it is possible to extract a
subject-specific life pattern from these kinds of information.
By the way, there is AI speakers as a related technology ofthe voice interactive system without a camera. Most of thecommercially available AI speakers have an inorganic design, so it seems that a certain number of people will find it
difficult to talk to. Therefore, as a means for increasing opportunities for conversations with users, we developed an
IHR that can express richly non-verbal information such as
facial expressions and movements, but also human speech.
This time, we conducted a questionnaire on comparativeevaluation of the IHR and the AI speaker to evaluate whether the design of the IHR we developed was easier to talk tothan an AI speaker. As a result, according to the answer tothe question, “Which of the IHR and AI speakers did you
think is easier to talk to?”, approximately 80% of the respondents answered that the IHR was easier to talk to.
Therefore, the proposed IHR is more effective as a life
watching service for acquiring conversation log data thancommercially available AI speakers because the IHR canmore easily induce users to talk to.

8

FUTURE WORKS

It is thought that human conversations can be made to gosmoothly and useful information can be easily extracted
from users by a robot that makes appropriate gestures suchas agreeable responses and nodding in the conversation [18].
In the future, we would like to study how to control the timing of the swinging, nodding and other motions of the IHR
when talking with users, and to conduct smooth conversa-

tions with the users. In addition, we would like to consider
the use of displays etc. in order to increase variations in the
IHR expressions.

REFERENCES[1] “About the present conditions and future way of watching of elderly people spreading-Mizuho Information &Research Institute (In Japanese)”, <https://www.mizuhoir.co.jp/publication/report/2017/pdf/mhir14 kourei.pdf>, [referred April.2019][2]“Elderly person watching business | Akiruno city (In Japanese)” ， <http://www.city.akiruno.tokyo.jp/ 0000004255.html>, [referred April.2019].
[3] “Tapia ROBOT”, <https://mjirobotics.co.jp>, (referred
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[5] "Soft Bank Pepper View (In Japanese)", <https://www.softbank.jp/robot/consumer/products/application/ncp/view/>,[referred Dec.2019].
[6] "RoBoHoN (In Japanese)", <https://robohon.com/apps/app31.php>, [referred Dec.2019].
[7] LOVOT, <https://lovot.life/features/>, [referred Dec.2019].
[8] T. Sugihara, T. Fujinami, and K. Nakagawa, ”Effects of
Cameras and Monitors on Caregivers’ Work Stress in theGroup Home (In Japanese)”, The Institute of Electronics,
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[9] Tai Ritu, “Let's make a natural conversation robot! Artificial intelligence made with RaspberryPi and Arduino(In Japanese)”，SYUWA SYSTEM，(2016).
[10] S. Mori, S. Yoshida, " ECHONET device adapter (Special feature / net home appliance: home network, terminal) (In Japanese)", Matsushita technical journal 47(1),pp.79-83, (2001).
[11] S. Koike, “Talking Robot [Mug bot]-electronic workwith Raspberry PI and Arduino-(In Japanese)” ， RIC
TELECOM. (2016).
[12] “ECHONET Consortium”, <http://www.echonet.gr.jp/>, [referred April.2019]．
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[18] T. Misu et al. , ”Toward Construction of Spoken Dialogue System that Evokes Users' Spontaneous Backchannels (In Japanese)"，The transactions of the Instituteof Electronics, Information and Communication Engineers. A，Vol.J95-A，No.1，pp.16-26，(2012)．
(Received November 6, 2019)(Revised February 5, 2020)
Keiichi Abe received Ph. D. in
informatics from Shizuoka University in 2012. He has been an
associate professor of the Faculty of Creative Engineeri-ng,
Kanagawa Institute of Technology since 2016. His researchinterests include smart systemusing IOT technology, Interactive system using Robot and CG, and Victims Information Management
System at Large Scale Disaster, HEMS. He is amember of Information Processing Society of Japan,
the institute of Electronics, Information and Communication Engineers of Japan and a member of IEEE.
Hiroki Fuse received his B.I.
degree from Kanagawa Instituteof Technology and entered the
Graduate School of Kanagawa
Institute of Technology in 2019.
His research subjects are HEMS
and AI robots.

Makiko Okumura received her
B.E. degree from Keio University in 1982, and Ph.D. degree
in electrical engineering fromTohoku University in 1994. She
joined Toshiba R & D Center,
Kawasaki, Japan, and transferred to Kanagawa Insti-tute ofTechnology in 2000. She has
been working on the development of analog circuit
simulators and now studying consumer electronics.
She is a member of the institute of Electronics, Information and Communication Engineers of Japanand a member of IEEE.

67

Masao Isshiki Ph.D Prof.
Professor of Kanagawa Instituteof Technology since 2011. Director of Smart House Rese-archCenter since 2012. JSCA
HEMS-Task Force chairman
since 2011 and also working formany chairmen of METI, MIC
and MLIT. Project professor of
Keio University since 2009 to 2015 responsible for
W3C/keio site manager. Fellow of ECHONET Consortium. Masao holds a Ph.D. degree in heat transfer
engineering from the Tokyo University of Agriculture and Technology, and a master's degree in mechanical engineering from the University of Tokyo
Institute of Technology. He currently lives in Tokyo,
Japan.