Proceedings of iiWAS2020

iiWAS2020 Papers

Implementing and Evaluating feedback feature of Mind
Monitoring Service for Elderly People at Home
Chisaki Miura1 , Sachio Saiki1 , Masahide Nakamura1,2 , Kiyoshi Yasuda3
1 Graduate School of System Informatics, Kobe University, Japan
2 Riken AIP, Tokyo

3 Faculty of Informatics, Osaka Institute of Technology, Japan

cmiura@ws.cs.kobe-u.ac.jp,sachio@carp.kobe-u.ac.jp
masa-n@cs.kobe-u.ac.jp,yasukiyo.12@outlook.jp

ABSTRACT

1

To support sustainable in-home long-term care, it is essential tomonitor mental states of elderly people at home, and to encourage
their ability of self-care. However, many challenges exist in practice, including limitations of human interventions, sensor-based
monitoring, 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-careof elderly people at home. In the proposed service, a chatbot asks
a user specific questions to acquire his/her mental state. Based on
the answers, the service assesses the mental state. In this research,
we develop a new feature of weekly feedback. The feature automatically reviews answers of past one week, and sends advice toimprove the current situation. We conduct an experiment to evaluate the effectiveness. Through the experiment, it was confirmedthat the weekly feedback encouraged self-care consciousness andincreased motivation of subjects.

Japan is facing a super-aging society. The proportion of people
over 65 years old in the total Japanese population was less than
5% in 1950, but it increased to 28.1% in 2018[2]. Under these circumstances, there are chronic shortage of nursing facilities andcare workers. To cope with the problem, the Japanese governmentis shifting the policy from the conventional facility-based care into
the in-home long-term care. The Ministry of Health, Labor and Welfare in Japan declares the Community-based Integrated Care System[12], which ensures the provision of health care, nursing care,prevention, housing, and livelihood support. The system consistsof four principles: self-care, mutual care, public support, governmental aid. Due to the limitation of the security cost, the government especially expects elderly people to conduct the self-care under the system.
However, the self-care and independent living are not easy formost elderly people, as their physical abilities and cognitive functions are being declined. Moreover, elderly people have higher riskof mental depression, compared to those of younger ages due totheir lost experiences[11]. Taking these facts into consideration, in
the in-home long-term care, it is essential to monitor their mind
and to provide appropriate supports according to the state of themind . In monitoring the psychological aspects of elderly people
at home, we consider the following three challenges:(P1)Limitations on human interventions and sensor-based
monitoring : Traditionally, the mental states of elderly people
have been assessed via human intervention such as inquiries andcounseling by professionals. However, it is not realistic to conduct
such interventions every day at home. Recently, the elderly monitoring systems using sensors and IoT come onto markets. However,they can only monitor externally observable events. Thus, the conventional monitoring systems do not cover internal mental statesof elderly people.
(P2)Challenge in recording and externalizing mental state :Every mental illness of an elderly person (e.g., depression) is caused
by various factors. The symptoms also vary from one person toanother[9]. Thereby, to specify the mental illness is not easy. Furthermore, the elderly also cannot grasp their own mental statesaccurately and externalize the states.
(P3)Challenge in realizing mind monitoring and appropriate support based on the mental state : Considering (P1) and(P2), it is difficult for any third person to objectively grasp, record

CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Ubiquitous and mobile computing systems andtools; • Applied computing → Health care information systems.

KEYWORDS
in-home long-term care, elderly monitoring system, mental state,sensing, agent, chatbot
ACM Reference Format:
Chisaki Miura1 , Sachio Saiki1 , Masahide Nakamura1,2 , Kiyoshi Yasuda3 .
2020. Implementing and Evaluating feedback feature of Mind Monitoring
Service for Elderly People at Home. In The 22nd International Conference on
Information Integration and Web-based Applications & Services (iiWAS ’20),
November 30-December 2, 2020, Chiang Mai, Thailand. ACM, New York, NY,
USA, 6 pages. https://doi.org/10.1145/3428757.3429124
Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others thanACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from permissions@acm.org.
iiWAS ’20, November 30-December 2, 2020, Chiang Mai, Thailand© 2020 Association for Computing Machinery.
ACM ISBN 978-1-4503-8922-8/20/11. . . $15.00
https://doi.org/10.1145/3428757.3429124

390

INTRODUCTION

Proceedings of iiWAS2020

iiWAS2020 Papers

Chisaki Miura1 , Sachio Saiki1 , Masahide Nakamura1,2 , Kiyoshi Yasuda3

iiWAS ’20, November 30-December 2, 2020, Chiang Mai, Thailand

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and monitor the mental states of elderly people at home. The elderly also have no opportunity to reflect their mental states. Therefore, it is yet challenging to support elderly people based on theirinternal mental states.
To overcome these challenges, we have proposed Mind Monitoring Service[5]. In the Mind Monitoring Service, we firstly introduce
a continuous interaction platform between an elderly person anda chatbot. Secondly, the chatbot asks various questions to acquire
the mental state. Finally, the service evaluates the "mind" of an elderly person based on the answers to the questions and realizes"mind" monitoring.
In the previous research[6], we have implemented the proposedservice and conducted a demonstration experiment. Based on the
result of the experiment, in this paper, we develop a new feature ofweekly feedback, which automatically reviews answers of past one
week and sends advice to improve the current situation. Besides,
we conduct an experiment in order to evaluate the effectiveness ofthe feature.

2 MIND MONITORING SERVICE[5][6]2.1 Overview
The Mind Monitoring Service is a new service which evaluatesand monitors the mental state of an elderly person via communication with a chatbot. Figure 1 shows the overall architecture of
the proposed service. The service consists of the following three
approaches (A1) to (A3).
(A1) Interaction with a chatbot using Mind Sensing Service: The Mind Sensing ( kokoro sensing in Japanese) is a new
type of sensing technique being developed by our researchgroup[7]. It aims to record the internal mind of a target person that cannot be observed externally by general sensors
or IoT. In the Mind Sensing, a virtual agent (VA)[8] or a chatbot asks the person various questions to externalize his/hermind as words. The Mind Sensing Service[3] is a core Web
service of the Mind Sensing, which defines and manages thequestions flexibly, and automates the delivery of the questions. The proposed service utilizes the Mind Sensing Service and collects internal state of an elderly person at home,through interaction with a chatbot.

(A2) Inquiry method specialized for acquisition of mental
state : In order to understand mental states of elderly people
at home, we develop specific questions. For this purpose, wefirstly introduce a framework characterizing mental healthusing three perspectives: Physicality, Mentality, and Sociality. Physicality evaluates the physical symptoms that can beseen objectively, such as fatigue, pain, sleep disorders. Mentality evaluates the subjective feelings such as emotions,moods, stress. Sociality evaluates the self-evaluations or behaviors of a target person, such as happiness, self-esteem,
and social behavior. These three perspectives are taken by
WHO’s "health" definition[13]. Secondly, we create inquiries
that reveal the current state for each of the three perspectives. We assume the target elderly person answers the questions with yes or no. The chatbot in (A1) asks these questions to an elderly person, and the service then records his/heranswers to grasp the mental state of the target person.
(A3) Self-care assistance and feedback by monitoring mental state : By collecting the answers to the questions, theservice evaluates the mental state of an elderly person. For
this purpose, the service calculates the degree of psychological health by assigning the score to the answers. Analyzing
the changes of the scores on a weekly or monthly basis enables the proposed service to monitor the mental state of thetarget person. Also, the service gives feedbacks based on theacquired mental states, in order to encourage self-reflection
and spontaneous self-care of mental health. Furthermore,
if an abnormal state which cannot be solved by self-care is
detected, the service connects to an external supporter, andasks for appropriate instructions.

2.2

Demonstration experiment

In the previous research[6], we have conducted a demonstration
experiment. The experiment was conducted for three months from
November 1st, 2019 to January 31st, 2020. The subjects were eight
men and women in the 50’s to 80’s.
We have implemented the proposed service for the experiment
as follows. Firstly, we asked an expert to create seven questions
which 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 indicates what to investigate by the question. In addition, "Category" in the Table 1 shows the results of classifying each question
into the three perspectives described in 2.1 (A2). Secondly, we built
a continuous interaction between an elderly person and a chatbot,
using LINE[1] which is a well-known messaging application on
smartphone. The chatbot sends one question in Table 1 once a day
to the user. The user answers the question with two choices, yesor no. All of the seven questions can be covered in a week, so thechatbot sends the first question again from the next week. In otherwords, the chatbot will continue to send the same set of questions
each week.
Figure 2 shows the actual screen of the proposed service implemented on LINE. The user can answer the question from thechatbot just by pushing the button of the screen. When the user
answers the question, the chatbot sends an additional question forthe purpose of investigating the user’s situation in more detail. For

391

Proceedings of iiWAS2020

iiWAS2020 Papers

Implementing and Evaluating feedback feature of Mind Monitoring Service for Elderly People at Home

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Table 1: Seven Questions

Figure 3: Mental States Data

2.3

Figure 2: Actual LINE Screen

example, Figure 2 shows the case where the user answers "Yes, I’ve
slept well." to the question, "Have you slept well in the past week?".
After understanding a good sleep condition of the user, the chatbot
sends an additional question to ask any concerns regarding sleep.
In order to evaluate the proposed service, after the experiment,
we calculated the response rate of the questions and visualized themental states of the subjects based on the answers. We also sent
questionnaire to the subjects. For the visualization of the mental
states, we assigned the score of +1, -1 to a positive answer and anegative answer respectively. By aggregating these scores for eachof the three categories described in 2.1 (A2), we grasped the degreeof psychological health for each subject.
In the proposed service, we assume the service sends a feedback
based on the mental state at weekly or monthly interval. However,
we could not implement the feedback feature by the time that theexperiment started. Therefore, in the demonstration experiment,
we decided to carry out and evaluate only the feature of monitoring the mental state in 2.1 (A3).

Results of the demonstration experimentDuring the experiment, five of eight subjects answered more than
80% of the questions from the chatbot. Moreover, we were able tovisualize the mental states data of the subjects by collecting andscoring the answers to the questions. We show one subject’s data
in Figure 3. The vertical line shows the scores and the horizontal line shows the number of weeks. As seen in Figure 3, we canconfirm changes of the mental states by week or overall characteristic of the user’s internal states, which cannot be obtained byconventional monitoring systems. From these results, we were able
to confirm the effectiveness of the proposed service.
On the other hand, the opinions were divided about the questions in the questionnaire, "Is the conversation with the chatbot
useful?" and "Does the conversation with the chatbot make you
feel better?". This means we could not verify if the interaction withthe chatbot is useful for maintaining and improving the mental
states of the elderly. One possible cause of this result is that we
could not implement the feedback feature, and the service could
not provide useful advices according to the user’s situation.
Therefore, in this paper, we implement a new feature of weekly
feedback in order to make the proposed service more useful for theelderly. The weekly feedback reviews the answers of the past one
week and provides advice automatically according to the mental
state. We also conduct an experiment using this feature and measure the effects of the feedback.

3 IMPLEMENTATION OF WEEKLY FEEDBACK
3.1 Design
The feedback feature evaluates the mental state of the user in acertain span and sends further inquiries or advices according to
the state. The purpose of the feedback is to promote the user’s selfreflection and spontaneous mental health care. In this paper, we
implement a new feature of weekly feedback which automaticallyreviews answers of past one week, and sends advice to improvethe current situation.
For the implementation, we divide the feature into two parts:
scoring the answers that numerically quantifies the one week’smental states of the user, and creating a feedback message thatgenerates a message according to the scoring results. We describe
the details of each part below.

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Proceedings of iiWAS2020

iiWAS2020 Papers

Chisaki Miura1 , Sachio Saiki1 , Masahide Nakamura1,2 , Kiyoshi Yasuda3

iiWAS ’20, November 30-December 2, 2020, Chiang Mai, Thailand

3.2

Scoring the answers

In the previous research[6], we assigned the score of +1, -1 to a
positive answer and a negative answer respectively. However, inorder to capture the user’s state in more detail, it would be better
not to use a simple binary score, but to assess user’s answers fromseveral aspects. Therefore, we introduce the following new threescoring methods.
(i) Score_answer : The binary score of the answer. We assigned+1 for a positive answer and -1 for a negative answer.
(ii) Score_observation : The score obtained by observing how
the answer has changed from the previous week. When theuser answered positively in the previous week, if the answer remains positive in the target week, +1 will be assigned
to the answer. If the answer turns negative, -0.5 will be assigned. Similarly, when the user answered negatively in theprevious week, if the answer remains negative in the target
week, -1 will be assigned to the answer. If the answer turns
positive, +0.5 will be assigned.
(iii) Score_sentiment : The analytic score of sentiment for theuser’s answers to an additional question from the chatbot.
If the user answers the additional question after answering
the first yes-no question, the service analyzes the text sentences and calculates the sentiment using Microsoft Azure
Text Analytics API[4]. The score is normalized so that it
takes a value from -1 to +1 (+1 means the most positive).
The total score of the answer is calculated by the weighted sumof the above three scores.

S tot al = w 1 · S answer + w 2 · Sobservat ion + w 3 · Ssent iment(w 1 + w 2 + w 3 = 1)

In this time, we set w 1 = w 2 = w 3 = 13 , and calculate the total

score as the average of the three scores.

3.3

creating a feedback message

Based on the scoring methods in 3.2, the service firstly selects one
question from the seven questions which were sent in a week.
Here, the service picks up the question whose score is the worst.
Secondly, the service creates a feedback message related to thecontent of the selected question. In order to generate natural sentences, we structure the feedback message from four paragraphs:
Greeting, Reflection, Advice, and Conclusion. More specifically, inthe greeting paragraph, the chatbot greets the user according to theseason or climate. In the refection paragraph, the chatbot shows
how the user answered the selected question in order to get theuser to look back him- or herself. The advice paragraph gives theuser useful information about the content of the question. We referto the information of the "Kenko-Choju Net"[10], which provides
a lot of information about health and longevity for Japanese elderly people. Lastly, in the conclusion paragraph, the chatbot gives
a closing remark, such as "Let’s do our best again this week.".
Figure 4 shows an example of a feedback message. In this feedback, the question about "psychology" was picked up. The sentences of each paragraph are pre-defined, and the service combines

Figure 4: Example of a Feedback Message

these paragraphs according to user contexts so that the service canprovide appropriate feedbacks to the user.

3.4

Operation of weekly feedback

For the actual operation of the weekly feedback, we automate thetwo processes of scoring the answers and creating a feedback message by Python programs, so that they will be executed regularly.
The service conducts the process of scoring the answers at designated time on a daily basis, and executes creating and sending a
feedback message at 20:00 on Saturday. Thus, the user will receive
a feedback message from the chatbot at 20:00 every Saturday.

4 EXPERIMENTAL EVALUATION
4.1 Outline
We have performed an experiment using the feature of weekly
feedback. The purpose of the experiment is to clarify the effects
and problems of the feedback.
The experiment was conducted with thirteen subjects for twomonths from May 2020 to June 2020. The subjects were two men
and four women in the 50s to 80s, a man and a woman in the 40s,
and five men in the 20s. Originally, the proposed service was intended for the elderly generation, but we also recruited young subjects in the experiment in order to get a lot of opinions.
The subjects have been using the proposed service since theprevious experiment. Therefore, in this experiment, we evaluate
the effects of the weekly feedback by comparing with the previous two months from March 2020 to April 2020, when the service
did not have the feedback feature. Namely, we investigate how thesubjects have been changed or been affected by receiving the feedback message. We show the results of the experiment by calculating the response rates before and after implementing the feedback
feature, presenting the responses from the subjects to the feedback
message, and the questionnaire after the experiment.
The experiment has been approved by the Research Ethics Committee of Kobe University Graduate School of System Informatics(No. R01-02).

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Proceedings of iiWAS2020

iiWAS2020 Papers

Implementing and Evaluating feedback feature of Mind Monitoring Service for Elderly People at Home

4.2

iiWAS ’20, November 30-December 2, 2020, Chiang Mai, Thailand

Results

4.2.3 Results of questionnaire. After the experiment, we senta questionnaire to the subjects in order to evaluate the effects of
4.2.1 The changes of the response rates. Table 2 shows thethe weekly feedback. In the questionnaire, we asked the subjects
response rate of each subject for the two periods before and after
if the self-care consciousness has improved by the feedback, and if
the implementation of the feedback feature. The response rate is
the usefulness of the proposed service has improved owing to thecalculated by the ratio of the number of questions answered by the
weekly feedback.
subject to the number of questions sent by the chatbot.
Figure 5 shows the questions and results of the questionnaire.
The answers were obtained from eleven subjects except for subject
Subject
Age
Gender Rate(March-April) Rate(May-June)
C and subject E.
A
70〜79
M
98.4%
96.6%
From Q1 in Figure 5, most of the subjects felt that what the chatB
60〜69
M
80.3%
98.3%
bot pointed out in the feedback could apply to themselves. From
C
80〜89
F
95.1%
93.3%
Q2, we can see that the subjects felt they were able to look back
D
70〜79
F
41.0%
45.0%
their own states thanks to the feedback. Besides, we can confirm
E
70〜79
F
19.7%
32.0%
from Q3 that most subjects tried to keep the advice in mind. On
F
50〜59
F
98.4%
100.0%
the other hand, some subjects answered that they were not able to
G
40〜49
M
90.2%
100.0%
put the chatbot’s advice into practice. When we asked the subjects
H
40〜49
F
100.0%
100.0%
the reason why they could not put the advice into practice, they
I
20〜29
M
91.8%
96.6%
said it was difficult to change their actions because they were used
J
20〜29
M
82.0%
81.0%
to their usual lifestyle.
K
20〜29
M
93.4%
98.3%
About Q4, nine subjects answered that their motivation to anL
20〜29
M
96.7%
97.0%
swer daily questions has improved by receiving the feedback mesM
20〜29
M
90.2%
94.9%
sage. Moreover, according to Q5, many subjects felt pleased to reTable 2: Response rates of the subjects
ceive the feedback message. We asked the subjects why they felt
pleased to receive the feedback, by the vote that allows multiple
answers in Q6. As a result, the top reasons were "Because the chatbot gives advices based on your answers.", and "Because you can
As seen in Table 2, the response rates of eight subjects have imfeel the chatbot always cares about yourself.". In contrast, the reaproved. In particular, about subject B and subject E, we can see
son "Because the advice from the chatbot is useful." was not voted
more than 10% of the improvement. On the other hand, the reby any subjects. It means this reason is not directly related to thesponse rates of some subjects have decreased or not changed. Howpleasure of the feedback message.
ever, the degree of decrease is very slight. Considering all of the
At the end of the questionnaire, we asked opinions and comsubjects, the response rates would have improved after implementments about the weekly feedback message. We obtained various
ing the weekly feedback.
comments from the subjects. They are, for example, "I was a little
4.2.2 Responses to the feedback message. When the chatbot
glad because I felt like the chatbot reads my messages.", "It would
sent a feedback message, some subjects responded to the message.
be better if the chatbot gives me information about my concerns.",
As an example, we show the actual feedback message which was"I would like the chatbot to ask questions about the topic which I
sent to subject F below.
 have talked so far in the dialogue.".
Hello, Ms.F! It is a comfortable climate, isn’t it?
Last week, you said you had not had many opportunities togo out, to talk and to have hobbies. How is the situation afterthat?
Speaking or singing can move the muscles around yourmouth and help maintain muscle strength.
Please keep it in mind! And let’s do our best again this week.

4.3

Discussion

Firstly, from the changes of the response rates, we can conjecturethat the weekly feedback feature gives good effects to subject’s
motivation. This is also confirmed by the results of Q4 in the questionnaire. Moreover, we can presume that the feedback was able to promote the subject’s self-reflection and self-care consciousness
Subject F responded to the feedback as follows.
 from the results of Q2 and Q3. These results show that to provide
an objective evaluation of mental states is effective for the subjects.
I totally agree with you. Fortunately, I live with my family, so
Additionally, it is also clear that the feedback message gave theI sometimes have opportunities to talk with someone. But it’ssubjects a generally positive impression, because many subjects
just a daily conversation. In this week, I will try to eat well, tofelt pleased to receive the message. In particular, we should notelearn many things and to laugh a lot! Thank you very much.
that the subjects felt pleased to receive the feedback because they
 thought the chatbot gave advices based on their answers and caredabout them. This means the feedback gives the subjects the sense
It is clearly seen that subject F looked back on her own behaviorof being constantly monitored and makes them feel safe. In otherand tried to keep the advice in mind. In other examples, there werewords, the feeling of relief may affect the pleasure and the satissubjects who responded to the feedback messages such as, "Thankyou for your concern.", "I really appropriate your messages.".
faction of the feedback message.

394

Proceedings of iiWAS2020

iiWAS2020 Papers

Chisaki Miura1 , Sachio Saiki1 , Masahide Nakamura1,2 , Kiyoshi Yasuda3

iiWAS ’20, November 30-December 2, 2020, Chiang Mai, Thailand

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Figure 5: Questionnaire and Results
On the other hand, to evaluate how much the self-care consciousness of the subject has changed or the message from thechatbot can relieve the subject’s feelings, we need more empiricalstudies. Besides, we also found some issues regarding the feedback
message. One of the major issue is the effectiveness of the advice.
No subjects answered that "The advice from the chatbot is useful." in Q6. In this implementation, the feedback was designed toprovide useful information for health or longevity. However, wefound just providing general health information was not very useful for the subjects. In order to give the advice which is truly usefulto the user, it is necessary to discover what the person is usuallyinterested in, through the dialogue. It’s also needed to provide specialized information with regard to user’s interest. We would liketo study personal adaptation of the advice as a future work.

5

CONCLUSION

In this paper, we have developed a new feature of weekly feedback, which automatically reviews answers of past one week andsends advice to improve the current situation. We also have conducted an experiment in order to evaluate the effectiveness of thefeature. The experimental results show that the weekly feedback
encouraged self-care consciousness and increased motivation of
the subjects. Besides, through the experiment, we were able to obtain various opinions and comments to the feedback message fromthe subjects.
In our future work, we try to develop more useful feedback
message for the user. We also conduct an extensive experimentin longer span with many elderly people to improve the proposedservice.

[3] Haruhisa Maeda, Sachio Saiki, Masahide Nakamura, and Kiyoshi Yasuda. 2019.
Rule-Based Inquiry Service to Elderly at Home for Efficient Mind Sensing. In 21stInternational Conference on Information Integration and Web-based Applications& Services (iiWAS2019). 666–670. Munich, Germany.
[4] Microsoft
Azure.
[n.d.].
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ACKNOWLEDGMENTS
This research was partially supported by JSPS KAKENHI Grant
Numbers JP19H01138, JP18H03242, JP18H03342, JP19H04154, JP19K02973,
JP20K11059, JP20H04014, JP20H05706 and Tateishi Science and Technology Foundation (C) (No.2207004).

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