一般社団法人 電子情報通信学会
THE INSTITUTE OF ELECTRONICS,
社団法人 電子情報通信学会
INFORMATIONAND COMMUNICATION ENGINEERS

信学技報
IEICE Technical Report
HCS2020-75(2021-03)

信学技報
TECHNICAL REPORT OF IEICE.

THE INSTITUTE OF ELECTRONICS,
INFORMATION AND COMMUNICATION ENGINEERS

在宅高齢者を対象とした「こころ」の見守りサービスの運用と品質評価
三浦 稚咲†

佐伯 幸郎†

中村 匡秀†,†† 安田清†††† 神戸大学 〒657–8501 神戸市灘区六甲台町 1–1†† 理化学研究所・革新知能統合研究センター〒103-0027 東京都中央区日本橋 1-4-1††† 大阪工業大学 〒535–8585 大阪市旭区大宮 5–16–1
E-mail: †cmiura@ws.cs.kobe-u.ac.jp, ††sachio@carp.kobe-u.ac.jp, †††masa-n@cs.kobe-u.ac.jp
あらまし

高齢者の在宅での自立生活を支援するためには，高齢者の自助機能を高めることに加え，高齢者の心の状態まで考慮した支援が必要である．しかしながら，人手による聞き取りや既存見守りサービスの限界，毎日の心理状態の記録や自発的な外化が難しいという問題がある．こうした問題を解決するため，先行研究では，在宅高齢者を対象とし，心理状態の見守りと自発的なメンタルヘルスケアを促すことを目的とした「こころ」の見守りサービスを提案している．提案サービスでは，チャットボットが高齢者に対し，心理状態の取得に特化した問いかけを継続的に行う．
その回答に基づき，サービスは対象者の心を見守り支援へつなげる．本稿では，実装した提案サービスを実際の世帯に対して適用し，サービスを実運用する．またソフトウェアの品質評価規格である SQuaRE に基き，提案サービスを評価する．運用の結果，長期間の被験者の心理状態データを取得することができ，提案サービスの効率性が確認された．
キーワード

在宅介護，高齢者見守りシステム，心理状態，センシング，エージェント，チャットボット

Operation and Quality Evaluation of Mind Monitoring Service for Elderly
People at Home
Chisaki MIURA† , S. SAIKI† , M. NAKAMURA†,†† , and K. YASUDA†††† Kobe University Rokkodai-cho 1–1，Nada-ku，Kobe，Hyogo，657–8501 Japan†† Riken AIP, 1-4-1 Nihon-bashi, Chuo-ku, Tokyo 103-0027††† Osaka Institute of Technology Omiya 2–16, Asahi-ku, Osaka, 535–8585 Japan
E-mail: †cmiura@ws.cs.kobe-u.ac.jp, ††sachio@carp.kobe-u.ac.jp, †††masa-n@cs.kobe-u.ac.jp
Abstract In order to support sustainable in-home long-term care, it is essential to monitor mental states of elderly people athome, and to encourage their ability of self-care. However, many challenges exist in practice, including limitations of humaninterventions, sensor-based monitoring, as well as the daily recording and externalization of mental states. In the previous
research, we have proposed Mind Monitoring Service, which aims to monitor mental states and promote self-care of elderly
people at home. In the proposed service, a chatbot asks a user speciﬁc questions to acquire his/her mental state. Based on theanswers, the service then assesses the mental state and sends the feedback. In this paper, we apply the proposed service toactual households and operate the service. In addition, we evaluate the service based on SQuaRE, which is the international
standards to evaluate the implementation of software. As a result, we were able to obtain long-term mental states data of thesubjects and to conﬁrm the eﬃciency of the proposed service.
Key words in-home long-term care, elderly monitoring system, mental state, sensing, agent, chatbot
care workers. To cope with the problem, the Japanese government is

1. Introduction

shifting the policy from the conventional facility-based care into theCurrently, Japan is facing a super-aging society. The proportion

in-home long-term care. The Ministry of Health, Labor and Welfareof people over 65 years old in the total Japanese population was lessin Japan declares the Community-based Integrated Care System [2],than 5% in 1950, but it increased to 28.4% in 2019 [1]. Under thesewhich ensures the provision of health care, nursing care, preven-circumstances, there are chronic shortage of nursing facilities andtion, housing, and livelihood support. The system consists of four

- 43 -—1—
This article is a technical report without peer review, and its polished and/or extended version may be published elsewhere.
Copyright ©2021 by IEICEprinciples: self-care, mutual care, public support, governmental aid.

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loss experiences [3]. Taking these facts into consideration, in the inhome long-term care, it is essential to monitor their “mind” and to

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図 1 Overall Architecture

In monitoring the psychological aspects of elderly people at home,
we consider the following three challenges:(P1)Limitations on human interventions and sensor-based mon-2. Mind Monitoring Service [5] [6] [7]itoring : Traditionally, the mental states of elderly people have beenassessed via human intervention such as inquiries and counseling by

2. 1 System Architecture

professionals. However, it is not realistic to conduct such interven-

The Mind Monitoring Service is a new ICT service which eval-tions every day at home. Recently, the elderly monitoring systems

uates and monitors the mental state of an elderly person via com-using sensors and IoT come onto markets. However, they can only

munication with a chatbot. Figure 1 shows the overall architecture

monitor externally observable events. Thus, the conventional moni-of the proposed service. The service consists of the following three

toring systems do not cover internal mental states of elderly people.

approaches (A1) to (A3).

(P2)Challenge in recording and externalizing mental state : Ev-(A1) Interaction with a chatbot using Mind Sensing Service :ery mental illness of an elderly person (e.g., depression) is caused

The Mind Sensing ( kokoro

by various factors. The symptoms also vary from one person to an-sensing technique being developed by our research group [9]. It aims

other [4]. Thereby, to specify the mental illness is not easy. Further-to record the internal mind of a target person that cannot be observed

more, the elderly also cannot grasp their own mental states accurately

externally by general sensors or IoT. In the Mind Sensing, a virtualand externalize the states.

agent (VA) [10] or a chatbot asks the person various questions to(P3)Challenge in realizing mind monitoring and appropriate

externalize his/her mind as words. The Mind Sensing Service [11] is

support based on the mental state : Considering (P1) and (P2),a core Web service of the Mind Sensing, which deﬁnes and manages

it is diﬃcult for any third person to objectively grasp, record andthe questions ﬂexibly, and automates the delivery of the questions.

monitor the mental states of elderly people at home. The elderly

The proposed service utilizes the Mind Sensing Service and collects

also have no opportunity to reﬂect on their mental states. Therefore,

internal states through interaction with a chatbot.

it is yet challenging to support elderly people based on their internal
mental states.

sensing in Japanese) is a new type of(A2) Inquiry method specialized for acquisition of mental state: In order to understand mental states of elderly people at home, we

To overcome these challenges, in this research, we have proposeddevelop speciﬁc questions. For this purpose, we ﬁrstly introduce

Mind Monitoring Service [5]. In this service, we ﬁrstly introduce

a framework characterizing mental health using three perspectives:a continuous interaction platform between an elderly person and a

Physicality, Mentality, and Sociality. Physicality evaluates the phys-chatbot. Secondly, the chatbot asks various questions to acquire theical symptoms that can be seen objectively, such as fatigue, pain,

mental state. Finally, the service evaluates the “mind” of an elderly

sleep disorders. Mentality evaluates the subjective feelings such as

person based on the answers to the questions and realizes “mind”

emotions, moods, stress. Sociality evaluates the self-evaluations ormonitoring.

behaviors of a target person, such as happiness, self-esteem, and so-

In the previous research [6] [7], we have implemented the pro-cial behavior. These three perspectives are taken by WHO’s "health"posed service. In this paper, we apply the implemented proposeddeﬁnition [12]. Secondly, we create inquiries that reveal the current

service to actual households and operate the service. We also evalu-

state for each of the three perspectives. We assume the target el-

ate the service from the viewpoint of SQuaRE (Systems and software

derly person answers the questions with yes or no. The chatbot in

Quality Requirements and Evaluation) [8], which is one of the in-(A1) asks these questions to an elderly person, and the service thenternational standards to evaluate the implementation of systems orrecords his/her answers to grasp the mental state.

software.

(A3) Self-care assistance and feedback by monitoring mental
state : By collecting the answers to the questions, the service evaluates the mental state of an elderly person. For this purpose, theservice calculates the degree of psychological health by assigning- 44 -—2—

表 1 Seven Questions
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the score to the answers. Analyzing the changes of the scores ona weekly or monthly basis enables the proposed service to monitor

図 2 Actual LINE Screen

the mental state of the target person. Also, the service gives feedbacks based on the acquired mental states, in order to encouragefollowing three scoring methods for assigning scores to the answers.

self-reﬂection and spontaneous self-care of mental health. Furthermore, if an abnormal state which cannot be solved by self-care is(i) Score_answer : The binary score of the answer. We assign 1
points for a positive answer and -1 points for a negative answer.

detected, the service connects to an external supporter, and asks forappropriate instructions.

(ii) Score_observation : The score obtained by observing how
the answer has changed from the previous week. For example, when

2. 2 Implementation of Chatbot Interaction

the user answered positively in the previous week, if the answer

In [6], we have mainly implemented the chatbot interaction of theservice. We ﬁrstly asked an expert to create seven questions which

remains positive in the target week, 1 points will be assigned to theanswer. If the answer turns negative, -0.5 points will be assigned.

will be sent to users. Table 1 shows the questions. These questions
ask the state of the past one week. "Survey item" in Table 1 indicateswhat to investigate by the question. In addition, "Category" in the
Table 1 shows the results of classifying each question into the three(iii) Score_sentiment : The analytic score of sentiment for theuser’s answers to an additional question from a chatbot. If the user
answers the additional question after answering the ﬁrst yes-no question, the service analyzes the text and calculates the sentiment.

perspectives described in 2. 1 (A2). Secondly, we built a continuousinteraction between an elderly person and a chatbot, using LINE [13]which is a well-known messaging application on smartphone. The
chatbot sends one question in Table 1 once a day to the user. The

The total score of the answer is calculated as the average of thethree scores. By aggregating these scores for each of the three categories described in 2. 1 (A2), the service grasps and visualizes thedegree of psychological health of the user.

user answers the question with two choices, yes or no. All of theseven questions can be covered in a week, so the chatbot sends theﬁrst question again from the next week. In other words, the chatbot
will continue to send the same set of questions each week.

About self-care assistance, we developed a new feature of weekly
feedback. In the feedback, the service selects one question whosescore is the worst in a week. Then, the service creates a feedback
message that presents the user’s past states and gives advices to

Figure 2 shows the actual screen of the proposed service implemented on LINE. The user can answer the question from the chatbot
just by pushing the button of the screen. When the user answers thequestion, the chatbot sends an additional question for the purpose ofinvestigating the user’s situation in more detail. For example, Figure
2 shows the case where the user answers "Yes, I’ve slept well." tothe question, "Have you slept well in the past week?" After under-

improve the current situation. Figure 3 shows an example of a feedback message. In this feedback, the question about “psychology”
was picked up. The chatbot indicates that the user said she had beenfeeling anxiety before, and suggests that she should have her family
or friends listen to her anxiety. By receiving such feedback on a
weekly basis, users can periodically reﬂect on their mental states.
The service can also promote the user’s self-care consciousness.

standing a good sleep condition of the user, the chatbot sends an

3. Operating Mind Monitoring Service

additional question to ask any concerns regarding sleep. The user
then answers the additional question by inputting text messages.

3. 1 Outline of Operation

2. 3 Implementation of Self-care Assistance

The operation period was from November 1, 2019 to January 31,

In [7], we have developed scoring methods for evaluating mental
states, and a feature of weekly feedback for promoting spontaneous
mental health care. As for the scoring methods, we introduced the2021, one year and two months (14 months) in total. During thisperiod, in addition to 8 elderly subjects (in the 50s to 80s), we also
had 19 men and women in the 20s to 40s use the service continu-- 45 -—3—

to more than 90% of the questions from a chatbot. For subject C andE, the overall response rate was low, not because they had stopped
using the service, but simply because they responded less frequently.
Regarding the other 19 subjects (in the 20s to 40s), the response rates
averaged 80%. Considering these results, it was conﬁrmed that the
responsiveness and continuity of the service were generally good.
3. 3 Analyzing Fluctuation of Mental State
Through 14 months of the operation, the service obtained a large
amount of mental state data. Figure 4 shows the graph of the mental
state scores of subject A (male in 70s) in 2020. In the graph, thevertical axis represents the average score value and the horizontal
axis represents months.
In Figure 4, we can ﬁrstly conﬁrm that subject A’s scores of each

図 3 Example of a Feedback Message

perspective are generally positive. This means his condition is relaously. Although the proposed service was originally intended fortively stable throughout the year, and his mental health in terms of

the elderly generation, we also recruited young subjects in order toPhysical, Mental and Social is maintained to some extent. However,

evaluate the eﬀectiveness of the proposed service itself. During the

we can see that his Physicality score dropped sharply in the middle ofoperation, if the subject feels any burden, the person can quit answer-May. When we asked subject A about the reason, he said that he had

ing questions from the chatbot at any time. Besides, we explained tohurt his leg by walking too much at that time. Afterwards, thanks

the subjects that the logs of their interactions with the chatbot would

to treatment and rehabilitation, his leg ﬁnally started to get betterbe stored in our database, and would not be disclosed to any thirdaround July. Also, we can ﬁnd that his Sociality score dropped inparty except the experiment administrators.

early March. This was because the spread of the coronavirus (covid-

These studies have been approved by the research ethics commit-

19) reduced his opportunities to go out. For a while after that, hetee of Graduate School of System Informatics, Kobe University (No.

stopped exercising at the gym, and his Sociality score continued to

R01-02). Written informed consent was obtained from subjects forstagnate. However, he started to go to the gym again around October,publication of this thesis and accompanying images.

and his Sociality score started to increase.

3. 2 Responsiveness and Continuity

4. Investigation of Quality in Use

As a result of the operation, two elderly subjects stopped using
the service within a few months. About one of the two elderly (male4. 1 Evaluation Methodin the 70s), it was diﬃcult for him to use the service because he

In this section, we discuss the quality of the Mind Monitoring

did not use his smartphone frequently in his daily life. The otherService based on the opinions of the subjects.

elderly person (male in his 70s) had been using the service for theAfter the long-term operation, we evaluated the proposed service

ﬁrst three months, but he eventually stopped using it because hisfrom the viewpoint of SQuaRE [8]. SQuaRE represents one of the in-asthma worsened, and made him diﬃcult to continue to answer the

ternational standards (“ISO/IEC 25000 Series”) which indicates thequestions from a chatbot every day. For the remaining 25 subjects,

line of thinking for the stakeholders to deﬁne various quality require-

we were able to get them to use the proposed service continuously.

ments and to evaluate the implementation of systems or software.

We especially show the response rates of elderly subjects for the en-

SQuaRE deﬁnes the quality characteristics from three perspectives

tire 14-month in Table 2. The response rate is calculated by the ratio(Product Quality, Data Quality, Quality in Use) to build a qualityof the number of questions answered by the subject to the number

model of the system. In this paper, we especially use the Qualityof questions sent by the chatbot.

in Use Model that evaluate the degree to which the user can use thesystem in a certain usage situation comparing with the user’s needs.

表 2 Total Response Rate of Elderly Subject

The Quality in Use Model is deﬁned by ﬁve quality characteristics

Subject

Age

Gender Rate

A

70〜79

M

91%

B

60〜69

M

92%

C

80〜89

F

30%

D

70〜79

F

90%

4. 2 Results of Questionnaire

E

70〜79

F

54%

In this paper, we especially discuss the quality characteristics of

F

50〜59

F

95%

Eﬀectiveness, Eﬃciency, Usefulness, Comfort and Flexibility. Fig-and eleven sub-characteristics (Figure 5). Since the quality in use isevaluated through user usage, we created a questionnaire to evaluate
all of the quality characteristics and sub-characteristics.

ure 6 shows the questions and results of the questionnaire regarding
From Table 2, we can see four out of six elderly subjects responded

above characteristics. In each question, our proposed service is- 46 -—4—

図 4 Mental Sate of Subject A

the proposed service can be assessed by whether the users were able
to reﬂect on their own state and achieve spontaneous mental healthcare. From Q1, we can see that most of the subjects were able
to reﬂect on their own states. However, Q2 shows that the subjects
could not make much eﬀorts to improve their mental states. Thereby,
although we were able to conﬁrm the eﬀectiveness of the service tosome extent, it was not suﬃcient.
EﬃciencyEﬃciency means the degree of the resources used by a user forachieving a particular goal, so the eﬃciency of the proposed service

図 5 Quality in Use Model
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Q3 and Q4, it can be seen that interacting with the chatbot was notlaborious, nor diﬃcult to answer questions in terms of frequency.
Therefore, the eﬃciency of the service was conﬁrmed.
Usefulness
Usefulness means the degree of user satisfaction. Therefore, theusefulness can be measured by how much the user ﬁnds the service
useful. From Q5, more than half of the subjects answered that interacting with the chatbot was useful for reﬂecting on their own states,
while nearly 30% did not. Besides, since it was revealed that somesubjects did not want to continue to use the service in Q6, we could
not conﬁrm the usefulness of the service very well.
Comfort
Comfort means the degree of user satisfaction with comfort whenusing the system or software. Thus, the comfort can be evaluated by

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the ease of using the service including the operability. Since Q7 and

Q8 show that the subjects were able to interact with the chatbot and

reﬂect on their own conditions easily, we can conﬁrm the comfort of

the proposed service.
Flexibility

図 6 Results of Questionnaire

Flexibility means the degree to which a product or system canmaintain the quality even in a unexpected usage situation. In thenamed Mei-chan, which is the display name of the LINE chatbot.
The questionnaire was answered by 18 subjects.

proposed service, users are expected to answer one question from achatbot every day. However, from Q9, we found there were someEﬀectivenesscases where the subjects entered their answers at a later time inEﬀectiveness means the degree of accuracy and completeness withwhich the user achieves the stated goals. Thus, the eﬀectiveness ofbatches. Although these cases were not originally intended, the factthat there was no problem in using the service suggests that the ser-- 47 -—5—vice maintained a certain degree of ﬂexibility. In addition, in Q10,

further develop the service.
謝辞

the subjects said that this kind of ﬂexibility was important.

本 研 究 は ，JSPS 科 研 費 JP19H01138, JP17H00731,

JP18H03242, JP18H03342, JP19K02973 の助成を受けている．

4. 3 Discussion

文Based on the results of operation and questionnaire of quality inuse, we can discuss if the purpose of the proposed service has beenachieved. The purpose of this service can be roughly divided into
following two, visualization and monitoring of mental states andpromotion of spontaneous mental health care.
As for visualization and monitoring of mental state, since we wereable to acquire mental state data for more than one year, we believe
this purpose has been achieved. In particular, it is noteworthy that
we were able to continuously acquire mental state data during sucha long period. We consider this is because that the eﬃciency andﬂexibility of the service had a positive impact on the subjects’ continuity. Namely, the facts that it was easy for the subjects to interact
with a chatbot and that they were able to use the service wheneverthey wanted may have promoted the continuity of service use.
On the other hand, promotion of spontaneous mental health care
has not been achieved, considering the results of the questionnaire.
We thought that reﬂecting on one’s own condition would lead to
self-care, but in fact, although the subjects were able to reﬂect on
their own states, they could not achieve improvements or self-care.
One possible reason for this is the diﬀerences of interests in healthof the subjects. We applied the service to 27 subjects in total, buteach subject has a diﬀerent health consciousness. In particular,
since many of the subjects were relatively young, it is likely that the
majority of the subjects did not feel any anxiety or problem about
their own health. Therefore, they may have felt there is no need for
improvement and have failed to achieve self-care.
Also, as revealed in the questionnaire, the usefulness of the service was also insuﬃcient. Moreover, we obtained comments fromthe subjects such as “I felt that chatbot’s replies were like ﬁxed form
sentences.” or “I felt a little stressed to be asked the same question
every week.” In order to encourage the users to conduct spontaneous
self-care and to perceive the usefulness of the service, it will be necessary to prevent the users from becoming accustomed to interactingwith a chatbot. We would like to devise methods not to make users
feel boring including to change questions or to provide interesting

献[1] Government of Japan, “Annual report on the aging society (2020),”
http://www.cao.go.jp/, July 2020.
[2] The Ministry of Health, Labor and Welfare in Japan, “Communitybased integrated care system,” https://www.mhlw.go.jp/stf/
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information for users, as a future work.

5. Conclusion
In this paper, we operated the Mind Monitoring Service to actual
households. We also evaluated the eﬀectiveness and quality in useof the service based on the viewpoint of SQuaRE. The results showthat while the service was able to obtain long-term mental state dataof the subjects, the usefulness of the service was still insuﬃcient.
As a future work, we would like to devise more eﬃcient methods for monitoring mental states of elderly people at home, andencouraging them to conduct self-care. Furthermore, we would liketo have more elderly people use the proposed service in order to- 48 -—6—