Participatory Design for Mental Health Data Visualization on a Social Robot Raida Karim

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Participatory Design for Mental Health Data

Visualization on a Social Robot

Raida Karim

University of Washington

Seattle, Washington, United States

rk1997@cs.washington.edu

Edgar Lopez

University of Washington

Seattle, Washington, United States

lopeze7@uw.edu

Elin A. Bj¨

orling

University of Washington

Seattle, Washington, United States

bjorling@uw.edu

Maya Cakmak

University of Washington

Seattle, Washington, United States

mcakmak@cs.washington.edu

Abstract—The intersection of data visualization and human-

robot interaction (HRI) is a burgeoning ﬁeld. Understanding,

communicating, and processing different kinds of data for

creating versatile visualizations can beneﬁt HRI. Conversely,

expressing different kinds of data generated from HRI through

effective visualizations can provide interesting insights. Our work

adds to the literature of this growing domain. In this paper, we

present our exploratory work on visualizing mental health data

on a social robot. Particularly, we discuss development of mental

health data visualizations using a participatory design (PD)

approach. As a ﬁrst step with mental health data visualization

on a social robot, this work paves the way for relevant further

work and using social robots as data visualization tools.

Index Terms—Participatory design, mental health, community,

data visualization, social robots, human-robot interaction

I. INTRODUCTION AND BACKGROUND

Despite many opportunities for collaborative research in

data visualization and HRI, not much work has been con-

tributed to this intersection [6]. Some existing works of this

area include visualizing data of children’s touch patterns on

a social robot [5]. Contributing to this promising domain’s

literature, we worked on developing visualizations of mental

health data for a social robot. Social robots have been used

to support mental health in various ways such as to help

children with autism improve on their social skills [1]. They

have been used to help older adults by reducing feelings of

loneliness [3], and other populations. However, existing work

only shows support for mental health through social robots by

responding interactively to human activity to help them learn

relevant skills. No work has shown the use of social robots

as a means of visualizing mental health data. Therefore, our

work is novel or ﬁrst of its kind.

II. A SOCIAL ROBOT & MENTAL HEALTH DATA

We detail here the procedure of collecting and visualizing

mental health data in these two respective stages:

A. Data Collection

We conducted a ﬁve-weekdays HRI study in an American

university campus with a total of ﬁfty-ﬁve (n=55) participants

sharing their in-the-moment mood and stress levels with a so-

cial robot. Our previous work [2] showed using an emoji likert

scale can enhance coherence and accessibility in portraying

different levels of mood or stress data, which is what we used.

The users’ shared data were stored in a secured ﬁrebase 1.

B. Data Visualization

We developed data visualization software with the updated

static data visualization template from [2] in our social robot’s

software platform. These visualizations are shown in Fig. 1,

and were implemented in JavaScript using AnyChart library

2. When the visualization program is run on the robot,

mood/stress data from ﬁrebase is sent to visualization software

and data visualizations are created in real-time.

III. DISCUSSION

Mental health data visualizations with a social robot can

potentially improve mental health [2]. To the best of our

knowledge, this is the ﬁrst work rendering mental health data

visualizations on a social robot seeking to alleviate mental

health issues among users. Although this work has been con-

ducted with data collected from people of a university campus,

collecting and visualizing other community’s data can inform

about distinct mental health needs of each community. Moving

forward, we plan to expand this work to other community

spaces such as high school, and public library. As mentioned

earlier, PD method was used to ﬁnalize design of data visual-

izations [2]. PD helped us to get inputs from the community

members in designing, developing and reﬁning the features of

the robot-rendered data visualizations for mental well-being.

We have been using PD in our work for more than two years.

We choose to use PD methodology, because PD considers the

intended users’ and stakeholders’ participation throughout the

design process and can result in well-informed and usability-

tested features of these data visualizations rendered by a social

robot. This can eventually help ensure the success of such

robotic technologies in supporting mental health. In [2], we

used qualitative analysis method to derive common themes in

1Firebase: https://ﬁrebase.google.com/

2AnyChart: https://www.anychart.com/

arXiv:2210.06469v1 [cs.HC] 20 Aug 2022

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ParticipatoryDesignforMentalHealthDataVisualizationonaSocialRobotRaidaKarimUniversityofWashingtonSeattle,Washington,UnitedStatesrk1997@cs.washington.eduEdgarLopezUniversityofWashingtonSeattle,Washington,UnitedStateslopeze7@uw.eduElinA.Bj¨orlingUniversityofWashingtonSeattle,Washington,UnitedStatesbjo...

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Participatory Design for Mental Health Data Visualization on a Social Robot Raida Karim

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