=Paper=
{{Paper
|id=Vol-1618/FuturePD_paper2
|storemode=property
|title= JogChalking: Capturing and Visualizing Affective Experience for
Recreational Runners
|pdfUrl=https://ceur-ws.org/Vol-1618/FuturePD_paper2.pdf
|volume=Vol-1618
|authors=Nabil Bin Hannan,Felwah Alqahtani,Derek Reilly
|dblpUrl=https://dblp.org/rec/conf/um/HannanAR16
}}
== JogChalking: Capturing and Visualizing Affective Experience for
Recreational Runners==
https://ceur-ws.org/Vol-1618/FuturePD_paper2.pdf
JogChalking: Capturing and Visualizing Affective
Experience for Recreational Runners
Nabil Bin Hannan Felwah Alqahtani Derek Reilly
Faculty of Computer Science Faculty of Computer Science Faculty of Computer Science
Dalhousie University Dalhousie University Dalhousie University
nabil@dal.ca fl823899@dal.ca reilly@cs.dal.ca
ABSTRACT 2. PROTOTYPE
We present JogChalker, a system that allows recreational runners The current prototype consists of a capture application and a
to capture their affective experience while running using touch visualization tool. The capture application is written in Java and
gestures. Using a small set of simple gestures, a runner can record runs on Android devices (Figure 1(a)), and provides a full screen
affect while running without looking at a screen or entering into a gesture capture interface. A yellow trace line shows a gesture as it
multi-step interaction. Gestures are recognized, but also recorded is being made, and it is recorded in real time. A standard Android
at high fidelity, as we believe how the gesture is made may itself gesture recognition library classifies the gesture once completed.
be expressive and useful for runners to review. We present our In addition to the time and location in which a gesture is made, we
initial prototype, describe the goals, structure, and outcomes of a record the traversal of the drawn gesture in terms of elapsed time
four-week participatory design session, and discuss the consequent and screen coordinates, as well as the width of the touch area and
capture and visualization implications for JogChalker that we are the device pressure (if supported by the hardware) throughout the
currently exploring. JogChalker provides new opportunities for gesture. Using this we generate an SVG animation so that the
self-tracking affective experience during running and for helping gesture can be replayed on the visualization interface. From the low
runners recall and interpret their runs. level data a number of higher-level attributes of the gesture can be
determined, including repetition, total area, average speed, and total
Keywords time taken. After initial testing we identified five candidate
Gesture; visualization; design; running; emotion. running-related emotional states to support (bored, tired, mellow,
euphoric, exhilarated), and developed simple candidate gestures for
1. INTRODUCTION evaluation (Figure 1(b)). These were initial gesture sets, we chose
Running is a physical activity enjoyed by many. It has low barriers gestures as we didn't want to focus on gesture elicitation during the
to participation, and for most, running is an active pastime rather design process, but rather the design of the mobile interface and
than a competitive sport. However not every run is as enjoyable as visualization dashboard.
the next, and personal preferences for runners vary: weather
conditions, location, terrain, music, time of day, etc. Popular
mobile applications such as Runkeeper, Runtastic, and
Endomondo track running data and let runners visualize and share
their runs. Aside from freeform annotation at the end of a run, such
applications don’t currently provide a means of capturing the
affective experience of a run. Consequently, their visualization
interfaces emphasize physical performance over the qualitative but
critically important notion of enjoyment. Without a means of
capturing emotion or affective experience during runs, runners
don’t have a way of tracking and identifying patterns that correlate
with a positive running experience. Such a feature would enable
runners to better choose the time, place, and circumstances of their (a) (b) (c)
leisure runs. Manual tracking tools like Moodmap [1] and Emotion
Map [2] allow users to tag locations and times with emotions, and
present these on a map. Typical widget-based interfaces can be Figure 1: (a) mobile screen for gesture application (b) armband
difficult or impossible to use when physically active, however [3]. with gesture list (c) initial map-based visualization
In this paper we present JogChalker, a system that allows
.
recreational runners to capture their affective experience while
running using touch gestures.
Figure 2: Sample visualization after session 1
single affective experience visualization interface. We additionally
gave them some scenarios to consider when refining their design
(e.g., running on a rainy day, running in a crowded area). Session 4
JogChalker’s visualization tool is written in JavaScript, and was was conducted in the style of a Future Technology Workshop [4].
built using Mapbox Studio (Figure 1(c)). Currently the tool The group brainstormed about alternative methods that recreational
displays a single running route (obtained using a manual export runners could use to capture and visualize affective experience.
from a running tracker on the mobile device). A list of the gestural
annotations made during the run is provided, and these are also
marked on the route itself using teardrop markers, above which are 4. OUTCOMES
the SVG gesture images. Recorded emotions varied; 1 participant drew gestures for mellow,
exhilarated and bored, 3 others used tired and mellow mainly. We
3. PARTICIPATORY DESIGN annotated designs and identified themes that emerged in the designs
Informal testing of the initial JogChalker prototype generated many and participant comments across the 4 design sessions. Due to
questions, including: Is it comfortable to make gestures while space constraints we briefly discuss some highlights here.
running? Is making a gesture emotionally expressive? Will runners Participants liked that gesture capture was automatic. They found
use the candidate states and gestures? How should affective recording gestures tricky when in full run, but didn’t mind slowing
experience be visualized and queried after a run, or after many down to do so. All participants wanted to define their own gestures,
runs? How could JogChalker be integrated with existing running and found it difficult to distinguish between euphoric and
data capture tools?
We wanted to further develop the prototype for a field study to
explore some of these questions. We employed a participatory
design approach with recreational runners. After pilot testing with
two lab colleagues we arrived at the methodology summarized
here. We recruited 4 recreational runners (one female and three
male, age 25-35), who each participated in 4 design sessions
distributed over a 4 week period. Each session was divided into two
parts – capture interface design and visualization interface design. Figure 3: Sample visualization after session 2
After first receiving training on making the 5 gestures (for bored,
mellow, tired, euphoric, exhilarated), participants ran for 30-60
mins using the capture tool prior to each session. Participants were
asked simply to run a familiar route. They used an Android
smartphone with a pressure-sensitive screen worn on an armband,
and a Mio heart rate wristband. Gestures were displayed on the side
of the armband for quick reference (Figure 1(b)). Since participants
were not used to recording emotions while running, the mobile
device would vibrate if no gesture was recorded over a 10 minute Figure 4: group designs, session 3
interval; otherwise participants were not prompted to record exhilarated. They all wanted to be able to record voice annotations,
gestures. The Runkeeper application was also launched on the instead of or in addition to gestures. Integration with Runkeeper
phone, and we preloaded the phone with a personal playlist if they was refined toward a simple interface to enter recording modes, and
preferred to listen to music while running. Participants also wore a a screen for reviewing and deleting annotations (see Figure 4) on
GoPro camera while running. This was to generate a video stream the mobile. The group also suggested that recording an emotion
that we provided as a potential element to include in the could immediately trigger a change in music playlist. Our
visualization interface, and to get a record of whether they slowed participants did not mention discomfort with the armband but did
down or stopped, and whether they looked at the screen when discuss using a smart watch as an alternative.
making a gesture.
The first two PD sessions were done individually. In session 1
participants sketched potential modifications to the capture
application using pen, paper, and post-its (Figure 2). They were Visualization interface designs maintained a simple map-based run
then shown the visualization prototype, Runkeeper’s visualization plot; most debate centered around whether data other than route and
interface, and the GoPro video feed. They were provided with pen, gesture location should always be visible or only after a selection
paper, and a set of paper widgets (including elements from the two interaction. When a gesture location is selected in the group design,
visualizations and others not presented on either visualization a synchronized video stream would play the corresponding
including video, music, and weather data) and sketched a single segment, and biometric data, music, weather, and the gesture itself
visualization interface that would integrate the captured gestures would be displayed in a popup. (see Figure 5) Despite prompting,
with other data they deemed relevant for visualizing their the notion of visualizing long term data patterns was not explored
experience (Figure 2). In session 2, participants used the same tools, in detail by the group.
to envision how to integrate gesture capture into Runkeeper, and
work on their visualization design after viewing those made by the
other runners (Figure 3).
The last two sessions were conducted as a group. In Session 3, the
group presented and discussed each member’s designs, then
worked to create a single integrated gesture capture design, and a
[3] Florian Mueller, Joe Marshall, Rohit Ashok Khot, Stina
Nylander, and Jakob Tholander. 2014. Jogging with technology:
interaction design supporting sport activities. In CHI 2014
Extended Abstracts. ACM, New York, NY, USA, 1131-1134.
[4] Giasemi N. Vavoula, Mike Sharples, Paul D. Rudman. 2002.
Developing the 'Future technology workshop' method. In
Proceedings of the International Workshop on Interaction Design
and Children (pp. 65–72). Eindhoven: The Netherlands
Figure 4: group designs, session 3
5. DISCUSSION
Despite some difficulties, our participants were satisfied with
gesture as a means of recording affective experience when running,
although they all felt that options for audio and custom gesture
should be available. It is important to note that participants did not
use audio annotations during the runs, and it may have its own
issues (background noise, feeling awkward, breathlessness).
Our PD approach may have limited novelty and variety in the
visualization interface; participants were primed by the initial
prototype and Runkeeper’s visualization, and the final result was a
fairly straightforward “mashup” of the 2 interfaces. Showing the
sketches of other users did encourage participants to think about
their decisions, however the designs were very similar to begin
with.
Our PD methodology also did not allow us to explore more nuanced
aspects of gestural affect capture and visualization, including
whether animating gestures supports inference and recall of
affective experience, and whether and how long-term use of the
interface supports discovery of running patterns leading to
enjoyment. Our future work will explore both questions.
6. FUTURE WORK
We are further investigating how collecting and visualizing
affective experience alongside traditional running biometric and
geospatial data can be used to generate richer insights into what can
influence a runner’s performance. One focus of this work is to
determine effective visual representations of gesture data, including
an assessment of whether an individual can interpret emotional
intensity by viewing an animation showing how a gesture was
made. We are also exploring how JogChalking might encourage
richer, more subjective recollections of running experiences. Over
the long term this may help runners to discover the running patterns
which lead to enjoyment for them, and for supporting tools to
provide recommendations based on this data.
7. REFERENCES
[1] Angela Fessl, Verónica Rivera-Pelayo, Viktoria Pammer, and
Simone Braun. 2012. Mood tracking in virtual meetings.
In Proceedings of the 7th European conference on Technology
Enhanced Learning (EC-TEL'12). Springer-Verlag, Berlin,
Heidelberg, 377-382.
[2] Yun Huang, Ying Tang, and Yang Wang. 2015. Emotion
Map: A Location-based Mobile Social System for Improving
Emotion Awareness and Regulation. In Proceedings of CSCW
2015. ACM, New York, NY, USA, 130-142.