JMIR FORMATIVE RESEARCH

Yoo et al

Original Paper

Investigating Older Adults' Use of a Socially Assistive Robot via
Time Series Clustering and User Profiling: Descriptive Analysis
StudyIn-jin Yoo1, PhD; Do-Hyung Park1, PhD; Othelia EunKyoung Lee2, PhD; Albert Park3, PhD
1

Graduate School of Business IT, Kookmin University, Seoul, Republic of Korea

2

School of Social Work, University of North Carolina Charlotte, Charlotte, NC, United States

3

Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, NC, United States

Corresponding Author:
Albert Park, PhD
Department of Software and Information Systems
University of North Carolina at Charlotte
9201 University City Boulevard
Woodward 310HCharlotte, NC, 28223-0001
United States
Phone: 1 7046878668
Email: al.park@uncc.edu

Abstract
Background: The aging population and the shortage of geriatric care workers are major global concerns. Socially assistive
robots (SARs) have the potential to address these issues, but developing SARs for various types of users is still in its infancy.
Objective: This study aims to examine the characteristics and use patterns of SARs.
Methods: This study analyzed log data from 64 older adults who used a SAR called Hyodol for 60 days to understand use
patterns and their relationship with user characteristics. Data on user interactions, robot-assisted content use, demographics,
physical and mental health, and lifestyle were collected. Time series clustering was used to group users based on use patterns,
followed by profiling analysis to relate these patterns to user characteristics.
Results: Overall, 4 time series clusters were created based on use patterns: helpers, friends, short-term users, and long-term
users. Time series and profiling analyses revealed distinct patterns for each group. We found that older adults use SARs differently
based on factors beyond demographics and health. This study demonstrates a data-driven approach to understanding user needs,
and the findings can help tailor SAR interventions for specific user groups.
Conclusions: This study extends our understanding of the factors associated with the long-term use of SARs for geriatric careand makes methodological contributions.
(JMIR Form Res 2024;8:e41093) doi: 10.2196/41093
KEYWORDS
socially assistive robot; older adults; robot use pattern; time series clustering; profiling analysis

Introduction
Overview
The costs of geriatric care and the shortage of home care workers
are major global concerns [1]. Socially assistive robots (SARs)
have the potential to address these issues, but developing SARs
for various types of users is still in its infancy. A majority ofprevious research has focused on physical human-robot
interaction, with an emphasis on the embodiment, control, andhttps://formative.jmir.org/2024/1/e41093

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safety checks of robots [2]. Recent research has delved intosocial human-robot interaction, focusing on the robot’s
communication, emotional exchange, and relationship formation[3-5].
SARs can perform the functions of emotional exchange, medical
assistance, cognitive training, and activity detection. Various
research efforts are now exploring SARs to resolve personal
problems related to the deterioration of physical and mental
health and social exclusion, especially targeting socially
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vulnerable groups, including socially isolated older adults.
Multiple studies have confirmed the positive effects of SARs
on older users including fall prevention, relief of stress anddementia symptoms, and improved quality of life [6-9].
Studies are still limited to the observation of cross-sectionaluser characteristics over a short period of time or to thesuperficial comparison of the effects before and after the
introduction of SARs [10,11]. Few studies are dedicated toanalyzing behavioral changes and psychological mechanismsof potential users of SARs (see [12] for an exception). This ismainly due to the high costs involved in observing user
behaviors and the difficulty of confining the scope of target
behaviors. Given that elder care using SARs could be along-term solution [13], further studies are needed to analyze

Yoo et aluser characteristics and to explore the changes in interactive
behavioral patterns of the users toward SARs through time series
data.

Background
Hyodol SAR
The data used in this study were collected and processed by
Hyodol SARs (Figure 1), a companion robot for home carelaunched by Hyodol Ltd. This 20-inch doll, designed to simulate
a grandchild, incorporates artificial intelligence and body sensors
for communication [14,15]. Hyodol can motivate users to
exercise and create positive social engagement. Two of its mostused functions by users are (1) the interactive relationship and(2) robot-assisted contents.

Figure 1. Hyodol and its features.

The interactive relationship between Hyodol and the user isenabled by the robot’s verbal reactions to the user’s actions suchas petting and holding its hand. Hyodol’s small, soft body allows
people to hold, snuggle, and exchange conversation with it,enabling multimodal communication. This SAR can becustomized and programmed to remind users of meals,
medication, and appointments, and encourages social interaction.
For example, Hyodol asks users to hold their hands after theytake their medication. By touching its ears and hands, users canchoose to interact with the robot in a variety of ways including
listening to popular songs, playing quiz games, and following
easy-to-follow exercises. Robot-assisted contents are designed
to improve the user’s daily living and to exercise cognitive
ability. This feature allows Hyodol to communicate healthhttps://formative.jmir.org/2024/1/e41093

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information, cue reminiscences, quote inspirational passages,
and tell stories.

Time Series Clustering and Profiling Analysis
Cluster analysis is an unsupervised learning method that groups
objects into clusters based on their similarity; it is an exploratory
analysis that identifies clusters within the sample based on ahigh level of homogeneity and similarity [16-18].
Identifying the similarity of time series data is a method usedin many different fields. Some examples include identifying
users with similar use histories [19]. The similarity or
dissimilarity between data points is quantified using “distance,”
which can be obtained using a wide range of distance scales
depending on the nature of data and analytical goals. However,when it comes to time series data, the concept of distance based
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on similarity cannot be easily applied due to the temporal
dynamic characteristics. Similarity measures used in existingcluster analyses have limitations. Mainly, the similarity measuredoes not consider the interdependent relationship between timeseries data. To overcome this limitation, a number of researchers
have tried various approaches to cluster by defining similarities
and differences between time series datasets. Two suchapproaches are the raw data approach and the feature-based
approach [20]. The feature-based approach proves effective
when digital signals with specific cycles are given as a dataset,
while the raw data approach is useful for calculating thesimilarity that reflects dynamic characteristics from time series
datasets [21].
Profiling analysis is a technique typically used to quantify
individual characteristics. It can be helpful to understanddifferent types of SAR users by generating descriptions andexplanations of data in this study [22]. Profiling the use pattern
can reveal new attributes underlying pattern differences that the
deductive approach has not been able to detect.

Research Questions
Previous studies have not addressed SAR use patterns andbehaviors for socially isolated older adults. This study aims to
explore the use patterns of SARs based on time series use log
data of 2 main features: interactive relationship androbot-assisted contents. The study will then cluster users withsimilar use patterns according to the time spent with SARs anddescribe characteristics from each cluster through profiling ofthese user groups.

Methods
Data Collection and Processing
The log data, collected from November 30, 2017, to February
1, 2019, consists of the frequency of users’ interaction with therobot and their use of robot-assisted contents. The data werecollected at 5-minute intervals for each participant for 60 days.
However, the data collection period exceeds 60 days because
participants did not start using SAR at the same time. We focus
on the first 60 days of use for all users. Logs that were deemed
as errors were excluded from the data, resulting in 870,000 valid
logs for analysis.
We first compiled time series datasets by reconstructing the log
data at an individual level and processing the use data according
to function and related use rates. To understand use patterns,
we analyzed daily interaction time by aggregating the 5-minutedata to represent the total interaction time for each day. We thenused time series clustering to categorize users into different
groups based on their SAR use patterns. For each cluster, we
optimized time series models to confirm the characteristics ofuse patterns at the individual level. In the second phase, we
conducted a profiling analysis to understand the differences in
the use pattern in relation to individuals’ demographic,
emotional, and behavioral traits.

Participants
The recruitment process was crucial for this study given that
Hyodol is designed specifically for the socially isolated
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Yoo et al
population of older people, not for the general public. Our
inclusion and exclusion criteria were carefully formulated toensure that participants were well-suited for interaction with
Hyodol and could meaningfully contribute to our understandingof SAR use among older adults. A video description of Hyodol
can be found in Multimedia Appendix 1.
This study focused on a specific group of older adults forparticipation. Inclusion criteria required participants to be 70
years of age or older with no noticeable vision or hearing
impairments. This ensured they could fully interact with thecontent and features offered by Hyodol. Additionally, sufficientmobility was necessary to use functions like exercise programs.
To avoid potential disruptions or negative reactions, those withpets were excluded from the study. Finally, participants wererecruited from community welfare centers to benefit from theoversight and support provided by social welfare organizations.
These recruitment restrictions, while potentially limiting thegeneralizability of our findings, allowed us to minimize theimpact of external variables such as disabilities among
participants. It is important to note that these criteria were
established not only to ensure the safety and well-being of theparticipants but also to enhance the reliability and validity of
our findings by selecting individuals who could fully engage
with the SAR’s capabilities. We have clarified this in theParticipants section within the Results section.
We surveyed and collected use data from 80 older adults who
used SAR. We excluded users who experienced the SAR’s
sensor errors and therefore lacked use log data, as well as
participants who did not complete surveys. The final dataset
consisted of 64 older adults who responded to both pretest andposttest surveys with enough use data. This study focuses on
the longitudinal datasets collected through the functions of“Interactive Relationship” and “Robot-assisted Contents.” We
selected these two functions since they can effectively reflect
the user’s behaviors that are related to their physical andpsychological satisfaction. Informed consent was obtained foreach participant for the survey data.
The appropriate sample size was estimated using GPower 3.1[23] software for an independent sample 2-tailed t test.
Assuming a significance level of α=.05, an expected effect sizeof 0.88, and statistical power of 0.79, the minimum sample size
was estimated to be 64 for clusters A and B. Similarly, for an
independent sample 2-tailed t test using GPower 3.1 software,with a significance level of α=.05, an expected effect size of
0.88, and statistical power of 0.8, the minimum sample size wasestimated to be 60 for clusters C and D. Therefore, a sample
size of 64 appears to be adequate.

Survey Measures
Overview
To profile the respondents, demographic survey questions wereasked including gender, age, and socioeconomic status.
Respondents were also asked if they received public welfare
assistance from the local government, and whether they usedemergency monitoring services.

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Physical HealthParticipants rated their health on a 3-point scale (0=poor,
0.5=same as before, and 1=great). Using the 3-point scale, they
rated the frequency of spending time alone (0=little,
0.5=somewhat, 1=a lot). We also asked participants to report
the number of chronic diseases and the number of daily
medications.

Lifestyle Management
On a 3-point scale (0, 0.5, and 1), each participant was asked
about their daily activities. These included the completion ofeight daily activities such as (1) wake-up, (2) daily ventilation,(3) medication intake, (4) eating meals, (5) taking a walkoutside, (6) exercise or fitness, (7) thinking positively, and (8)initiating social connection.

Mental Health—Depression
The Geriatric Depression Scale was used [24]. Each GeriatricDepression Scale item was rated as either yes=1 or no=0 by the
respondents; the total scores (ranging from 0 to 15) were
calculated. A score of 5 or higher is regarded as symptomaticof depression.

Performance Evaluation
Participants were asked to evaluate Hyodol’s performance with
its functionality on a 5-point Likert scale. The 11 functions forlifestyle management were listed including wake-up, bedtime,
daily inspirational passage, weather, meal time, time for
medication, exercise or fitness, brain training games, songs,
responsiveness, safety monitoring, and voice message fromcaregivers. The average score was used for our analysis.

algorithms for measuring pattern similarity between two
temporal sequences, compare two time series regardless of thetime of observation by calculating the fit between them [25].
This method enables measurements or calculations even when
there are different time points of observation or different sample
sizes in the two series.
After experimenting with GAK and dynamic time warping, wechose GAK for similarity scaling [25]. We specifically used the“dtwclust” package provided by the R software [26] to conduct
time series clustering. The package is optimally designed fortime series data clustering [26]. For the package-based timeseries clustering, we used GAK for the similarity scale andpartitioning around medoids for the center extraction as theparameters for clustering. Following the time series clustering,
we described user types based on use patterns, lifestyle, andrelated health behaviors via profiling analysis. Use patterns
include interaction (eg, stroking, handholding, and petting) andcontent use (eg, exercise or fitness and brain training).

Ethical Considerations
This project has been reviewed by the Kookmin University
Institutional Review Board (KMUIRB; KMU-202304-HR-348)
for the secondary analysis of users’ log data. Informed consent
was read to each participant, and signatures were obtained fromeach research participant. Participant identities were protected
through the complete anonymization of all data used in thisresearch. To reduce bias, participants were not compensatedfinancially for their participation.

Results

Satisfaction

Time Series Clustering

Participants were asked to rate their satisfaction with Hyodol
on a 5-point scale. The following 5 aspects were assessed
including usability, preference for the SAR appearance, accuracyof voice, content, and touch. The questionnaires used in thisstudy can be found in Multimedia Appendix 2.

Table 1 summarizes the amount of interaction and content use
for each participant. The participants were divided into 2 clusters
for each type of use (interaction and content) based on theamount of use. We chose to compare 2 specific clusters with
sufficient data for robust analysis. Based on the use patterns forinteraction, users were categorized into two clusters of 51 and
13 individuals. Users were classified into two clusters of 49 and
15 individuals based on the use patterns for contents. It is
important to note that only clusters A and B within interaction
use and clusters C and D within content use will be comparedwith each other. This is because the study was designed tocompare use patterns within each type of use (interaction andcontent), rather than to compare use patterns across types ofuse.

Data Analysis
This study used log data from 2 main features—interactive
relationships and robot-assisted contents—to examine thecharacteristics and use patterns of SARs. The raw data approachof time series clustering was used to investigate individual
differences in use patterns. Specifically, the techniques ofdynamic time warping and global alignment kernel (GAK) wereconsidered and explored. These 2 techniques, which are
Table 1. Time series clustering results.
Interaction use

Contents use

A

B

C

D

Intracluster distance

0.010

0.017

0.000

0.000

Size, n (%)

51 (80)13 (20)49 (77)15 (23)In Table 2, we summarize overlapping membership to explore
connections between activities within clusters. Table 3 shows

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the general participant information regarding their age, gender,
health, and social tendencies.

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Table 2. Associations between activities within clusters.
Interaction use

Function use

Total, n (%)

C

D

A

45

6

51 (80)B4

913 (20)Total, n (%)

49 (77)15 (23)64 (100)

Table 3. General participant characteristics for each cluster.
Cluster name

Total

AC

AD

BC

BD

Participants, n

45

6

4

9

64

Sex (female), mean (SD)0.60 (0.40)

1.00 (0.000)

1.00 (0.000)

0.89 (0.33)

0.70 (0.46)

Average age (years), mean (SD)78.58 (8.07)

77.17 (10.52)

85.50 (3.70)

82.33 (6.63)

79.41 (8.07)

Subjective health, mean (SD)0.47 (0.42)

0.417 (0.38)

0.25 (0.50)

0.44 (0.46)

0.45 (0.42)

Alone time, mean (SD)0.43 (0.35)

0.583 (0.20)

0.75 (0.50)

0.72 (0.36)

0.51 (0.36)

Number of diseases, mean (SD)

2.16 (1.51)

2.17 (0.98)

2.00 (0.82)

2.00 (1.00)

2.13 (1.35)

Number of medicines, mean (SD)2.24 (1.32)

2.17 (0.75)

2.75 (0.50)

2.00 (0.87)

2.23 (1.18)

Daily ventilation

0.64 (0.38)

0.75 (0.27)

0.63 (0.48)

0.44 (0.39)

0.63 (0.38)

Regular meals

0.59 (0.42)

0.67 (0.41)

0.38 (0.25)

0.28 (0.36)

0.54 (0.41)

On-time medicine intake

0.54 (0.40)

0.75 (0.27)

0.50 (0.000)

0.39 (0.42)

0.54 (0.38)

Wake-up0.53 (0.42)

0.42 (0.49)

0.50 (0.0000)

0.39 (0.42)

0.50 (0.41)

Walks outside

0.47 (0.40)

0.67 (0.26)

0.50 (0.0000)

0.33 (0.43)

0.47 (0.39)

Exercise or fitness

0.42 (0.41)

0.42 (0.20)

0.63 (0.48)

0.22 (0.36)

0.41 (0.40)

Positive thinking

0.58 (0.43)

0.50 (0.32)

0.50 (0.41)

0.39 (0.42)

0.54 (0.41)

Social connection

0.61 (0.40)

0.75 (0.42)

0.75 (0.29)

0.28 (0.44)

0.59 (0.41)

Depression

5.90 (4.07)

5.00 (3.03)

8.25 (4.92)

6.11 (5.65)

6.00 (4.24)

Lifestyle management, mean (SD)Cluster AC, the largest group, consisted of 45 participants. The
average age of the participants in this cluster was 78.58 (SD8.07), and it had the lowest proportion of women (n=27, 60%).
On average, these participants reported 2.16 (SD 1.51) chronic
diseases and took 2.24 (SD 1.32) medications. They rarely spenttime alone, with an average of 0.433 (SD 0.35) hours per day
which indicates a moderate level of depression scores (mean5.90, SD 4.07).

Cluster BD consisted of 9 users, with 89% (n=8) being women.
The average age of this cluster was 82.33 (SD 6.63), making
them older than AC and AD, but younger than BD. They
reported a relatively low number of chronic diseases (mean2.00, SD 1.00) and daily medications (mean 2.00, SD 0.87) andspent many hours alone on a daily basis (mean 0.72, SD 0.36).
Furthermore, they exhibited a moderate level of depression
scores (mean 6.11, SD 5.65).

Cluster BC comprised 4 women, making it the smallest group.
This group is also the oldest, with an average age of 85.5 (SD3.70). These users spend the largest amount of time alone (mean0.750, SD 0.50). On average, they had two chronic diseases andtook 2.75 (SD 0.50) medications. Notably, they had the highest
depression score (mean 8.25, SD 4.92) among all the clusters.

Although the clustering results provide a general picture of eachcluster’s characteristics, specific differences in use traits are notknown. Thus, the centroids of use for each cluster were
calculated to identify clear patterns. The centroids of use andcorresponding graphs are shown in Figure 2.

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Figure 2. Cluster A, B: interaction use centroid.

Cluster analysis for interaction use shows that users grouped incluster A have a lower use level than users in cluster B. Theirinteraction use sharply declines within the first 21 days of use,
while the users in cluster B show a gradual decrease in use forthe entire period of 60 days. The average use of cluster B users
is also higher than that of cluster A users.
Content-based use clusters C and D show that users in cluster
C had less average use time than users in cluster D. However,both clusters display a very low average use after 41 days. Users
in cluster D used more content than users in cluster C. Content

use for cluster C users dramatically decreased after the initial
5 days of use and remained at a very low level. On the other
hand, content use for cluster D users gradually decreased for
45 days but remained higher than that of cluster C users.

Time Series Analysis
The use time steadily decreases for all 4 groups. With theassumption that users will eventually stop using SAR, we used
time series analysis to identify patterns and examine their traits
computationally. The results from applying a damped-trendmodel [27-29] to the four clusters are presented in Table 4.

Table 4. Applying damped-trend and Holt model result.
Model

Estimate, mean (SE)t test (df)

P valueα0.418 (0.103)

4.071 (57)

<.001

γ

0.129 (0.102)

1.267 (57).21π0.966 (0.033)

29.457 (57)

<.001

α

0.700 (.124)

5.626 (58)

<.001

γ

0.000 (.020)

0.003 (58).99

α0.500 (0.113)

4.423 (57)

<.001

γ

0.513 (0.390)

1.316 (57).19π0.835 (0.117)

7.113 (57)

<.001

α

0.067 (0.035)

1.925 (58).06

γ0.415 (0.223)

1.861 (58).07Interaction
Cluster A

Cluster B

Content
Cluster C

Cluster D

The damped-trend model was applied to clusters A and C. The
models for clusters B and D evolved from the primary modelto the Holt model consisting of α and γ. For cluster B, thesignificance probability of the trend factor of γ is P=.99,
meaning that the time series use data in the cluster follows
simple linear exponential smoothing. Finally, optimal models
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damped-trend model for cluster A, simple linear exponential
smoothing for cluster B, the damped-trend model for cluster C,
and the Holt model for cluster D. Each group’s specificcharacteristics are described in the following sections.
Cluster A has no seasonality, and its linear trend graduallydisappears; the temporal mean of the time series data follows
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the trend and ultimately approximates a constant. Based onparameter estimates, cluster A’s SAR use showed no trendchanges (γ, P=.21), and its amplitude declined over time. Cluster
A’s smoothing factor of α, estimated by the damped-trend, is0.418 with a t value of 4.423 and P<.001. This result indicatesthat more weight is placed on the values observed earlier thanthe most recently observed values. The estimated value of π,
the factor of decreasing amplitude trend, is 0.966 with a t valueof 29.457 and P<.001. When considering π’s maximum valueof 0.98 in real-world situations, the effect on amplitude reduction
was minimal, which translates into a gradually declining
amplitude of use, and ultimately, no use in the end.
Cluster B has no trend and seasonality in which temporalchanges in the average amount within a time series follow ahorizontal pattern. Therefore, as shown in Figure 2, it is assumed
that cluster B’s SAR use amount takes the form of a horizontal
line with few temporal changes. Cluster B’s smoothing factorof α, estimated by the simple exponential smoothing, is 0.700
with a t value of 5.988 and P<.001. This result indicates that
more weight is placed on the values observed earlier than themost recently observed values. Cluster B’s use graph may create
an illusion of a downward trend. However, with the selection

Yoo et alof simple exponential smoothing as an optimal model, no actual
trend cycle and seasonality were observed, and the average use
was shown to be linear. This indicates that while cluster B’s
use seems to follow a downward trend, it actually exhibits alinear horizontal line without a trend, which implies that SARs
are steadily used for a relatively longer time.
Cluster C’s SAR use displays no trend changes (γ, P=.19), andthe amplitude decreases over time. The cluster’s use shows an
exponential function and gradually approaches zero in relation
to the damped-trend, as shown in Figure 3. Cluster C’s
smoothing factor of α, estimated by the damped-trend, is 0.500,
with a t value of 4.423 and P<.001. This result indicates that
equal weight is placed on both the values observed earlier andthe most recently observed values. The estimated value of π,
the factor of the decreasing amplitude trend, is 0.835, with a tvalue of 7.113 and P<.001. Since π’s value is rarely below 0.8
in real-world situations, the effect on amplitude reduction issignificant. In other words, the use-related amplitude reduction
is immediately observed, with the use approximating a constant
thereafter. This indicates that the use sharply declines to almost
no use over a short period.

Figure 3. Cluster C, D: contents use centroid.

Cluster D’s use changes over time (Figure 3). Cluster D’s
estimated smoothing factor of α is 0.067, with a t value of 1.925
and P=.06. This result indicates that more weight is placed on
the values observed earlier than the most recently observed
values. Cluster D’s estimated trend factor of γ is 0.415 with a tvalue of 1.861 and P=.07, which indicates that Cluster D’s use
follows the Holt model with the acceptance level of α and γ at
P=.10. Due to the model’s nature, it is observed that the overall
trend is present with no cycle or seasonality. The Holt model
for cluster D confirms the decreasing trend, which appears on
the graph of SAR use, actually exists.

Profiling Analysis
Interaction Use
The demographic characteristics of clusters A and B users are
summarized in Table 5. To address multiple comparisons in

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profiling analysis, we applied P value adjustments using theHommel procedure [29] in Tables 5-8. Cluster A consists mainlyof men (n=18, 35%) and women (n=33, 65%) aged younger
than 80 years old. Those who receive public welfare assistancefrom the local government accounted for 43% (n=22). Their
perceived health status is somewhat negative, with 37% (n=19)
of them rating their health as “poor.” They also spend less timealone (n=14, 27%). Cluster A users lead relatively balanced
lives as evidenced by all the items of the life management
survey. These users ventilate their rooms daily, get up and eat
on a regular basis, take their medicine, walk and exercise more
frequently, have more positive thoughts, and have a low
dependence on SARs. Since they enjoy good mental health andhave less emotional interaction with the robots, for the rest of
the paper, we will refer to this group as “SARs as Helpers.”

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Table 5. Descriptive statistics of interaction use cluster.
Variables

Cluster A

Cluster B(n=51)(n=13)

t test (df)

P value

Adjusted P value

Sex (female), n (%)

33 (65)12 (92)

3.893 (62)

.05

.72

Public assistance, n (%)

22 (43)5 (38)

0.300 (62)

.77

.99

Average age, mean (SD)78.41 (8.283)

83.31 (5.921)

3.998 (62)

.05

.70

Subjective health, mean (SD)

0.46 (0.41)

0.39 (0.46)

0.339 (62)

.56

.99

Alone time, mean (SD)0.45 (0.33)

0.73 (0.39)

6.762 (62)

.01

.19

Number of diseases, mean (SD)

2.16 (1.4)

2 (0.9)0.371 (62)

.71

.99

Number of medicines, mean (SD)2.24 (1.2)

2.23 (0.8)

0.016 (62)

.99

.99

Daily ventilation

0.52 (0.42)

0.42 (0.34)

1.343 (62)

.18

.92

Regular meals

0.66 (0.37)

0.50 (0.41)

2.353 (62)

.02

.32

On-time medicine intake

0.57 (0.39)

0.42 (0.34)

1.234 (62)

.22

.96

Wake-up

0.60 (0.41)

0.31 (0.33)

0.758 (62)

.45

.99

Walks outside

0.49 (0.39)

0.39 (0.36)

0.876 (62)

.38

.99

Exercise or fitness

0.42 (0.39)

0.35 (0.43)

0.608 (62)

.55

.99

Positive thinking

0.57 (0.41)

0.42 (0.40)

1.142 (62)

.26

.99

Social connection

0.63 (0.40)

0.42 (0.45)

1.610 (62)

.11

.87

Depression

5.79 (3.93)

6.77 (5.32)–0.734 (62)

.47

.98

Lifestyle management, mean (SD)

Table 6. Subfunction use ratio by interaction use cluster.
Variables

Cluster A (n=51)

Cluster B (n=13)

t test (df)

P value

Adjusted P value

Use day, mean (SD)31.67 (24.98)

50.00 (12.75)

6.512 (62)

.01

.04

Use day or retention period, mean (SD)0.52 (0.27)

0.74 (0.16)

8.270 (62)

.006

.03

Stroking per day

32.5 (109.14)

26.23 (19.46)

0.042 (62)

.84

.84

Handholding per day

6.08 (12.58)

26.58 (38.2)

10.608 (62)

.002

.02

Petting per day

5.96 (9.14)

25.08 (48.37)

7.281 (62)

.009

.04

Exercise per day

0.25 (0.41)

0.88 (1.31)

8.605 (62)

.005

.03

Quiz game per day

0.52 (0.68)

1.39 (2.01)

6.761 (62)

.01

.04

Hyodol performance

4.37 (0.56)

4.78 (0.15)

5.798 (62)

.02

.05

Hyodol satisfaction

2.66 (0.36)

2.9 (0.14)

4.701 (62)

.04

.07

Interaction, mean (SD)Content , mean (SD)https://formative.jmir.org/2024/1/e41093

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Yoo et al

Table 7. Descriptive statistics of contents use cluster.
Variables

Cluster C (n=49)

Cluster D (n=15)

t test (df)

P value

Adjusted P value

Sex (female), n (%)

31 (63)14 (93)5.225 (62)

.03

.43

Public assistance, n (%)

20 (5)

7 (47)

0.396 (62)

.69

.98

Average age, mean (SD)79.14 (8.016)

8.27 (8.455)

0.220 (62)

.64

.98

Subjective health, mean (SD)0.45 (0.42)

0.43 (0.42)

0.016 (62)

.90

.98

Alone time, mean (SD)

0.46 (0.37)

0.67 (0.31)

3.95 (62)

.05

.77

Number of diseases, mean (SD)2.14 (1.45)

2.07 (0.96)

0.190 (62)

.85

.98

Number of medicines, mean (SD)2.29 (1.27)

2.07 (0.80)

0.796 (62)

.43

.98

Daily ventilation

0.53 (0.40)

0.40 (0.43)

0.680 (62)

.50

.98

Regular meals

0.64 (0.38)

0.57 (0.72)

1.141 (62)

.26

.98

On-time medicine intake

0.54 (0.38)

0.53 (0.40)

0.066 (62)

.95

.98

Wake-up

0.57 (0.41)

0.43 (0.42)

1.086 (62)

.28

.98

Walks outside

0.47 (0.39)

0.47 (0.40)

0.024 (62)

.98

.98

Exercise or fitness

0.44 (0.42)

0.30 (0.32)

1.188 (62)

.24

.98

Positive thinking

0.57 (0.42)

0.43 (0.37)

1.141 (62)

.26

.98

Social connection

0.62 (0.39)

0.47 (0.48)

1.283 (62)

.20

.98

Depression

6.11 (4.14)

5.67 (4.67)

0.348 (62)

.73

.98

Lifestyle management, mean (SD)

Table 8. Subfunction use ratio by contents use cluster.
Variables

Cluster C (n=49)

Cluster D (n=15)

t test (df)

P value

Adjusted P value

Use day34 (25.83)

39.93 (17.34)

0.692 (62)

.41

.67

Use day or retention period

0.51 (0.27)

0.73 (0.17)

8.911 (62)

.004

.02

Stroking per day

34.11 (111.15)

21.8 (2.25)

0.180 (62)

.67

.67

Handholding per day

4.76 (5.07)

28.15 (39.88)

16.583 (62)

<.001

<.001

Petting per day

4.98 (6.73)

25.71 (45.46)

9.832 (62)

.003

.02

Exercise per day

0.21 (0.34)

0.94 (1.24)

14.191 (62)

<.001

<.001

Quiz game per day

0.35 (0.41)

1.85 (1.8)

3.253 (62)

<.001

<.001

Hyodol performance

4.37 (0.57)

4.7 (0.23)

3.867 (62)

.05

.22

Hyodol satisfaction

2.68 (0.37)

2.8 (0.22)

1.197 (62)

.28

.61

Interaction

Content

Cluster B users are predominantly women older than 80 years(n=12, 92%). Slightly over half (n=7, 53%) rated their perceived
health status as poor, and many also spend a lot of time alone(n=8, 61%). The users in cluster B lead relatively unhealthy
lives, as evidenced by the life management survey items. Theseusers are healthier when it comes to the number of diseases.
These users have more and longer interactions with SARs andreport better physical health. Since they have a high level ofinteraction with robots throughout the study, we will refer tothis group as “SARs as Friends.” When compared to the peersin “SARs as Helpers,” the users in “SARs as Friends” weremore likely to be older women who spend more time alone andhave a regular meal schedule. However, these are not statisticallysignificant.
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As shown in Table 6, users in the “SARs as Friends” group used
SARs more frequently per day (t62=6.5; P=.01) and had a higher
retention rate (t62=8.2; P=.006). In terms of interaction, users
in the “SARs as Friends” group were more likely to hold handswith SARs (t62=10.6; P=.002) and pet them (t62=7.3; P=.009).
For robot-assisted content, users in the “SARs as Friends” groupalso used SARs for exercise (t62=8.6; P=.005) and brain training
game (t62=6.7; P=.02). However, SAR performance andsatisfaction with Hyodol were found statistically not significant.

Content Use
The profiles of clusters C and D users are summarized in Table
7. Cluster C users are welfare recipients, accounting for 59%(n=29). Many of them rated their perceived health status as poor
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JMIR FORMATIVE RESEARCH(n=20, 41%), and they spent less time alone. They exhibit
balanced lives according to life management surveys, yet withless reliance on SAR content. For the rest of the paper, we willrefer to this group as “Short-term Users.”
Cluster D users were mostly older women (n=14, 93%), withan average age of 80.2 (SD 8.4) years. In terms of theirperceived health status, fewer healthy users were reported (n=4,27%), but they have a good social life (n=14, 93%). Users incluster D are social support recipients (n=7, 47%). In otherwords, physical difficulties and disabilities translate into poor
physical health. To sum up, the users who use SAR contents
more and longer are physically unhealthy. For the rest of thepaper, we will refer to this group as “Long-term Users.”
In comparison to short-term users, long-term users also
demonstrated more frequent use (ie, use day or retention period)of the content feature during the study (t62=8.9; P=.004) asshown in Table 8. On average per day, long-term users used
SARs for exercise (t62=14.2; P<.001) and brain training features(t62=3.3; P<.001) more frequently. A higher level of interaction
was also observed in terms of handholding (t62=16.58; P<.001)
and petting (t62=9.83; P=.003).

Discussion
Principal Findings
SARs are being seen as a way to help health care professionals
improve the quality of care for socially isolated older adults.
However, there are challenges in meeting the needs of different
types of older adults. This study expands the scope of researchin SARs by demonstrating a set of processes through which use
data can be used to inform the most effective use for different
types of users. Specifically, we adopted time series clusteringand profiling to analyze SAR use patterns and highlight use
differences among user groups (eg, exercise or fitness and brain
training), despite statistically similar demographics,
environments, and psychological health.

Comparison to Prior Work
Several studies conducted in Korea have demonstrated asignificant decrease in depression scores among solo-living
older individuals who have used the Hyodol robot, suggesting
its potential as a supportive companion for solitary older adults[12,14,30]. While these studies have highlighted the potentialof SARs in elder care, some raised concerns about a lack ofdiversity in the participants within their research [31]. This canlimit the generalizability of findings and potentially hinder theeffectiveness of SARs for a wider range of older adults. Acrossvarious fields, data-driven approaches have proven successful
in revealing user psychological states [32] and various
challenges [33-35].
In recognition of this gap, this study aimed to expand on existing
SAR research by explicitly considering the diverse needs ofdifferent populations of older people. By incorporating
data-driven approaches, we develop a deeper understanding of
how SARs can be most effectively tailored to meet thepreferences of various user groups and how to identify theseusers.
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Yoo et alStrengths and Limitations
The findings from this study suggest the importance ofdifferentiating user groups in SAR development. Our finding
suggests that tailoring features based on use patterns has thepotential to significantly enhance the effectiveness of SARs.
Additionally, our experience emphasizes the need for training
and support programs for older adults and their caregivers. By
providing such resources, we can maximize the benefits that
SARs can offer.
We acknowledge the limitations of this study and recommend
further research. First, further cluster segmentation based onuser patterns can be studied. SAR user patterns can be either
cyclical or irregular, and a wide range of these patterns can befurther subdivided. Therefore, an increased number of clusters
and various classification techniques will facilitate the processof in-depth research on user attitudes and satisfaction dependingon use patterns and the effectiveness of SARs.
Second, multifaceted profiling is needed. Due to the small
sample size of participants, this study used profiling with a focus
on descriptive statistics on demographics and use patterns.
Follow-up studies with larger sample sizes can unlock detailsof use patterns with respect to the effective use of SARs using
diverse statistical techniques, including survival analysis.
Additionally, understanding outcomes based on different typesof SARs and user populations, such as individuals from different
cultures, can strengthen our findings.

Future Directions
These findings have important implications for the development
and use of SARs in the future. It is important to consider theneeds of different groups of older adults when developing SARs.
Effective SARs require tailoring features to specific older adultgroups, as use patterns vary dramatically as shown in this study.
Additionally, it is important to provide training and support toolder adults and their caregivers on how to use SARs effectively.
This will help to ensure that SARs are used in a way that meetsthe needs of older adults and their caregivers. Several studies
conducted in Korea have demonstrated public health social
workers who managed the Hyodol care system also highlighted
the positive impact of Hyodol in providing companionship,care, and emotional support, particularly in situations whereclients felt isolated or lacked regular family interactions [36].
Overall, this study suggests that SARs have the potential to bea valuable tool for meeting the needs of older adults. However,more research is needed to develop SARs that are tailored to
the needs of different groups of older adults and to address thepractical concerns that have been raised.

Conclusions
This study categorized users with similar characteristics into
clusters based on SAR use patterns. Then, profiling was used
to identify and define each cluster’s noteworthy features.
Specifically, we used time series SAR use to cluster“Interaction” and “Contents” into 2 use patterns, respectively.
We then explored the key characteristics of each use pattern byapplying profiling analysis. Despite statistically similar
demographics, environments, and psychological health, older
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JMIR FORMATIVE RESEARCH
adults interact with SARs differently. Finally, based on profiling,
we produced various insights into the use of SARs. This study
extends our understanding of the factors associated with the

Yoo et al
long-term use of SARs for geriatric care and makes
methodological contributions.

Acknowledgments
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2020R1A2C1006001).

Data Availability
The datasets generated during or analyzed during this study are not publicly available due to privacy concerns for the participants.

Authors' Contributions
IJY and DHP are co–first authors. IJY contributed to the data curation, formal analysis, investigation, methodology, validation,
visualization, and writing of the original draft. DHP contributed to the conceptualization, project administration, funding acquisition,and supervision. OEL contributed to the conceptualization, investigation, methodology, project administration, and supervision.
AP contributed to the conceptualization, project administration, supervision, and reviewing and editing.

Conflicts of Interest
None declared.

Multimedia Appendix 1
A TED Talk for a further exploration of Hyodol SAR and its applications.
[MP4 File (MP4 Video), 96510 KB-Multimedia Appendix 1]

Multimedia Appendix 2
Investigator-developed questionnaires and measurement instruments used in this study.
[DOCX File , 16 KB-Multimedia Appendix 2]

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Abbreviations
GAK: global alignment kernel
SAR: socially assistive robot

Edited by T Leung; submitted 14.07.22; peer-reviewed by E Yom-Tov, S-F Fung, G Makwana; comments to author 12.12.22; revised
version received 13.04.23; accepted 11.07.24; published 19.09.24
Please cite as:
Yoo I-J, Park D-H, Lee OE, Park A
Investigating Older Adults' Use of a Socially Assistive Robot via Time Series Clustering and User Profiling: Descriptive Analysis
Study
JMIR Form Res 2024;8:e41093
URL: https://formative.jmir.org/2024/1/e41093
doi: 10.2196/41093
PMID:©In-jin Yoo, Do-Hyung Park, Othelia EunKyoung Lee, Albert Park. Originally published in JMIR Formative Research(https://formative.jmir.org), 19.09.2024. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete
bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license
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