Gaining Insight from Physical Activity Data using a Similarity-based

Interactive Visualization

Arkaitz Artetxe, Gorka Epelde, Andoni Beristain, Ane Murua and Roberto Álvarez

eHealth & Biomedical Applications, Vicomtech-IK4, Donostia, San Sebastian, Spain

Keywords: Personal Visualization, Personal Visual Analytics, Visualization Insights, Personal INFOVIS, Personal

Informatics, Data Clustering, Time-series Data, Periodic Events, Physical Activity, Quantified-Self,

Lifelogging.

Abstract: This paper presents a new interactive visualization approach which aims to help and support the user in

gaining insight over his physical activity data. The main novelty of the proposed visualization approach is the

representation of similarities in the physical activity patterns in time using data clustering techniques, in

addition to the continuous physical activity representation over a circular chart. This grouping of similar

activity patterns helps identifying meaningful events or behaviors, combined with the periodicity highlighting

circular charts. The user is able to interact with the visualization during the knowledge discovery process by

changing the represented time-scale, time-frame and the number of clusters used for the user’s physical

activity pattern categorization. Additionally, the proposed visualization approach allows to easily report and

store the insights gained during the visual data analysis process, by adding a textual description linked to the

particular user tailored visualization configuration which led to that insight.

1 INTRODUCTION

Individuals have tracked their personal data

historically with the objective to measure and

improve specific behaviors (e.g. health, economy,

sport performance) (Marcengo and Rapp, 2013).

Traditionally, this personal data tracking has been

carried out manually, or at most, with the support of

basic informatics tool (e.g. spreadsheets) for storage

and visualization purposes.

In recent years, the advances in self-monitoring

technology, the availability of low cost and

unobtrusive monitoring sensors (implemented in

Smartphones or wearable devices), and the

widespread adoption of the Smartphone and

development of self-monitoring Smartphone Apps

have led to a growth of the people engaged into the

self-quantification through personal informatics

movement (i.e. lifelogging or quantified-self). The

research being carried out by quantified-self early

adopters is of particular interest in the context of

empowering people in the self-management of their

health.

Personal physical activity is usually quantified as

https://www.fitbit.com

energy expenditure (e.g. calories), or distance

covered (e.g. step count). Those measures are

obtained from different kinds of sensors, being the

current trend to use the smartphone built-in sensors

and commercial wearable devices (e.g. Fitbit

or Mi

Band

) either separately or in combination.

Self-quantifiers draw meaningful inferences and

realize causality through the collection and analysis

of their personal data, following a self-reflection

process (Huang et al., 2014). Self-reflection is

achieved through the analysis of personal data using

data visualization tools. However many Quantified-

Selfers, are not visualization experts or data scientists

(Choe et al., 2015).

The contribution of this publication is the

proposal of an interactive visualization method which

complements traditional chart visualizations with the

automatic categorization of physical activity data

patterns using clustering techniques. This interactive

visualization method aims to assist individuals in

their self-reflection process, by depicting visual cues

to spot similar patterns, complemented with a data

representation spatial layout which fosters periodic

pattern identification.

http://www.mi.com/in/miband/

Artetxe, A., Epelde, G., Beristain, A., Murua, A. and Álvarez, R.

Gaining Insight from Physical Activity Data using a Similarity-based Interactive Visualization.

DOI: 10.5220/0005675701150122

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 2: IVAPP, pages 117-124

ISBN: 978-989-758-175-5

117

In this paper, we first analyze the related work in

the field of self-tracked physical activity visualization

(Section 2). Section 3 presents our contributions, i.e.,

an approach for similarity based interactive

visualization of self-tracked physical activity. In

Section 4 we summarize the paper and present our

conclusions.

2 RELATED WORK

Huang et al. (Huang et al., 2014) define and classify

Personal Visualization (PV) as an interactive visual

data representation process for the personal context,

and Personal Visual Analytics (PVA), as

visualization that involves the computer assisted

analysis. Following this classification, in the first

subsection we will first analyze the evolution of the

personal visualization techniques considering the

scope of personal data. Next, in a second subsection,

PVA approaches, which integrate the computer

assisted analysis with the visualization of the self-

quantified data will be analyzed. The aim of the

computer assisted analysis is to facilitate the self-

reflection process and the identification of insights by

the user.

2.1 Personal Visualization Techniques

In the prior works in the field, various approaches

have been proposed for the visualization of self-

quantified data, ranging from traditional statistical

graphics, to representations in the form of abstract art.

Although proposals of new visualization paradigms

as informative art (Fan et al., 2012), living metaphors

(Consolvo et al., 2009; Khot et al., 2015; Lin et al.,

2006) or ambient display (Jafarinaimi et al., 2005;

Rogers et al., 2010) have been defined, most of the

tools for physical activity monitoring visualization at

the consumer user level (e.g. Endomondo,

MapMyFitness, ) still use charts (e.g. line, bar,

diagram scatter, pie charts) and most-self-quantifiers

use charts to represent and display their physical

activity (Choe et al., 2015).

Recently, new approaches to visualize self-

quantified data have emerged. A recent work (Larsen

et al., 2013), presents a visualization technique for

time series data in the form of a spiral structure

visualization. This visualization technique aims to

assist the user in identifying repeating patterns in self-

quantified data, such as geographical location or

physical activity.

The spiral-based representation is good at

discovering recurrent patterns over a period of time.

The spiral visualization represents events with similar

period aligned along similar angles of the arcs,

resulting in aligned sections of the spiral (Carlis and

Konstan, 1998; Weber et al., 2001).

A spiral is formed by a continuous line

representing the time, so that “time’s continuity”

concept is clearly represented. However, the

represented time unit (i.e. hours, days, weeks etc.) is

not always clearly identified. Say we have a

visualization formed by a spiral of Archimedes that

shows seven segments (representing days) per each

rotation of 360 degrees. The primary time unit -a day-

is obvious and easy to identify, nevertheless the

secondary time unit –a week- is not so obvious and its

boundaries are not always clear.

Our approach is based on circular maps. Spiral-

based visualization and circular visualizations are

similar in many ways, but the latter is unique in that

each ring represents a temporary unit. This concept is

similar to tree rings, where every ring represents a

year. The rings closer to the center are the oldest and

the external ones the newest. In addition, display can

also be seen as an analog clock, in which the clock

hands can have different number of positions

depending on the time scale. For a week scale, the

clock hands have 7 steps, one per day of the week.

For a day scale the hands may have 24 steps, one per

hour. And given position, all the rings crossed by the

clock hand correspond to the same step/phase in the

time scale (e.g. day of the week), that is, they are

visually aligned permitting a clear comparison.

2.2 Personal Visualization Analytics

Techniques

In the context of large data set exploration, it is

generally a difficult task to recognize patterns or

trends, even if a good visualization is provided.

Furthermore, regarding personal data visualization

and the tools available for that purpose, people may

not dedicate much effort exploring the possibilities

provided by those tools and may not have the skills or

experience required to identify such patterns.

In the non-personal context, computer algorithms

have been applied to visual analysis applications

successfully (Gotz et al., 2014; Van Wijk and Van

Selow, 1999). Here, Wijk et al. (Van Wijk and Van

Selow, 1999) presented a job that uses a combination

calendar and chart visualization combined with

clustering techniques to facilitate the identification of

patterns and trends in multivariate time series.

On a personal context, the use of computer

algorithms to recognize patterns and trends can help

users gaining insights into their behaviours and

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

118

reducing the attention required to explore personal

visualizations.

In the research works (Froehlich et al., 2009,

2012; McDuff et al., 2012) the authors apply

clustering techniques to PV. Froehlich et al. applied

computer-based classification to improve the

visualization of transportation usage (Froehlich et al.,

2009) and water consumption (Froehlich et al., 2012).

In the same way, Mcduff et al. (McDuff et al., 2012)

use a classification scheme to predict the emotional

state of the user and provide a user interface to capture

the user reflection.

Some other authors have investigated the use of

computer algorithms to optimize the design of

personal visualizations (Douma et al., 2009; Shen and

Ma, 2008). Douma et al., (2009) use computer

algorithms to maintain the design of integrated radial

tree structure (involving graph structures) with

balanced layout visualization. The development

presented in (Shen and Ma, 2008) supports the

temporal and semantic filtering through a time chart

diagram and an interactive ontology. The aim of this

development is to isolate subsets of data, for a

detailed analysis.

Another usage of computer algorithms in PVA

that has been identified is on texts analysis for

visualization (Dork et al., 2010; Marcus et al., 2011).

In the research of (Marcus et al., 2011),

computational algorithms are applied to text analysis

to automatically find peaks and high activity of

tweets, and to significantly label them using text from

the tweets themselves. Similarly, (Dork et al., 2010)

use text processing in the context of the evolution of

a conversation to visually represent what just

happened and what is happening now.

Other uses for computer algorithms in PVA

include the reduction of the data dimensions to allow

users to visualize and navigate through them

(Faridani et al., 2010; Preuveneers and Berbers,

2008). (Faridani et al., 2010) applied this approach to

navigation through online comments and

(Preuveneers and Berbers, 2008) did the same for the

recognition of health states, to help users with

diabetes, to take informed decisions about the daily

dose, to achieve and sustain adequate levels of

glucose in blood.

Finally, the last identified computer algorithms

usage in PVA are data-mining techniques.

Khovanskaya et al. used data-mining techniques to

identify patterns in the personal context

(Khovanskaya et al., 2013).

http://d3js.org/

https://code.google.com/p/figue/

http://getbootstrap.com/

Grouping different data samples by similarity

often facilitates the insight gaining process. This is

why clustering techniques have been applied in the

field of visual analytics as a way to emphasize

differences and similarities between events or

sequence of events (Van Wijk and Van Selow, 1999).

In Wijk et al.’s work energy expenditure data is

clustered in order to find similar day patterns along a

year. Each cluster, -representing a day pattern- is

assigned a color and, within the calendar, each day is

painted with its corresponding cluster’s color.

Our approach takes from (Van Wijk and Van

Selow, 1999) the idea of grouping time series

according to their similarity and identifying samples

as members of the cluster they belong to, using colors.

However, instead of using a hierarchical clustering

algorithm as in (Van Wijk and Van Selow, 1999), our

approach uses centroid-based clustering techniques.

In addition, we have introduced the circular chart

visualization instead of a calendar, thus providing

better PVA capabilities.

3 MATERIAL AND METHODS

An interactive web-based user interface has been

implemented enabling users to visually explore and

analyze their own physical activity data.

We have used d3.js

as the primary chart

generating tool of our interactive user interface. We

have selected and combined some different

visualizations such as circular chart, pie chart and

multi-series line chart.

For the implementation of the unsupervised

classification part we have utilized a clustering

algorithm collection called figue

, since it provides a

JavaScript implementation of algorithms such as k-

means and fuzzy c-means amongst others.

Interactive web user interfaces have been built on

top of Bootstrap

framework and jQueryUI

library.

Those technologies allow following a responsive web

design approach, thus providing an optimal viewing

and interaction experience across different devices

(e.g. smartphones, tablets, laptops).

3.1 Activity Data

Although our work is generalizable to virtually any

kind of univariate interval time series data, this work

focuses on the analysis of physical activity data. To

demonstrate the capabilities of our visualization tool,

https://jqueryui.com/

Gaining Insight from Physical Activity Data using a Similarity-based Interactive Visualization

119

we have built a dataset using raw physical activity

data acquired continuously from Mi Band

devices.

We have collected the raw data samples recorded by

5 different devices during a period of 4 months (over

170000 samples per person). The obtained raw data is

composed of a per-minute measure of the following

data:

 Timestamp

 Number of steps

 Activity (Sleep, Idle, Run, Walk)

 Walk Distance

 Run Distance

 Walk Calories

 Run Calories

Collected raw data has been processed in order to do

aggregations of different granularity.

3.2 Clustering

Our ultimate goal is to group similar day activity

patterns together, in order to show which group

belongs each day. We also aim to show the shape of

the average day of each cluster. For that purpose, we

use machine learning based clustering techniques.

To apply cluster analysis techniques we divide our

time series data into a collection of n elements, each

representing a day,









,…,





(1)

An element consists of a sequence of m elements,









,…,





(2)

Where y

denotes the value of the single activity

feature selected by the user (i.e. number of steps,

caloric expenditure, run/walk distance). Depending

on the time scale selected by the user, m can be either

1440 (per minute values) or 24 (per hour aggregation.

This value along with period of time (i.e. start and

ending dates), the clustering algorithm to be applied

and the number of clusters (k value) can be specified

by the user.

In this work we have used the well-known Fuzzy

c-means (Bezdek et al. , 1984) algorithm as well as

the widely used k-means algorithm, although other

clustering algorithms may be used.

In fuzzy clustering –unlike hard clustering

algorithms such as k-means- there is not a sharp

classification of data elements into non overlapping

groups. Instead, for every data sample the probability

of belonging to each cluster is provided.

Thus, after applying the algorithm, we obtain not

http://www.mi.com/in/miband/

only a list of c centroids,









,…,





(3)

But also a membership matrix,



,

∈



0,1



,1,…,,



1,…,

(4)

Where w

i,j

is a value between 0 and 1 which

represents the degree to which the element x

belongs

to cluster c

3.3 Similarity based Interactive

Visualization

During the clustering process we obtain a collection

of cluster centroids, which characterize each cluster

and can be regarded as the average of each day’s

activity pattern. We also get a membership matrix

where it is specified which cluster belongs each day.

Finding a proper way of displaying this information

could be a challenge.

As seen in

Figur, we have designed a web-based

user interface layout composed of 4 main elements,

namely a) parameter selector area, b) day pattern and

centroid area, c) circular map area and d) annotation

area.

In the parameter selector area users can select the

time range by specifying the starting and ending dates

in a calendar. Users may also select which activity

indicator should be used for the calculations. Choices

are: i) run caloric expenditure, ii) walk caloric

expenditure, iii) sum of run and walk caloric

expenditure, iv) walk distance, v) run distance, vi)

sum of walk and run distances and vii) number of

steps. The time scale can also be specified in minutes

or hours, along with the number of clusters for the

categorization.

Last but not least, users can select whether to

apply fuzziness or not. If fuzziness is set to false a

standard k-means algorithm is used, otherwise fuzzy

c-means algorithm is used and some additional

functionalities are available within the resulting

visualization (e.g. a pie chart showing the cluster-

membership distribution is shown when the cursor is

passed over a day).

Once the user has selected the values mentioned

above, data is processed and a clustering process is

triggered.

The patterns of resulting clusters are visualized in

a typical line chart, where a color is assigned to each

of them, as seen in Figure 1-b.

Days are represented using a circle chart and the

color assigned the cluster they belong to. When the

fuzzy c-means algorithm is utilized, the cluster with

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

120

Figure 1: Web-based interactive visualization. a) Parameter selector area. b) View of the line chart showing day patterns (gray

lines) and cluster centroids colored according to the legend. c) The user has placed the mouse over a certain cell of the circle

chart, thus the pattern of that day is highlighted in the multi-series line chart (red line in the upper chart). Simultaneously a

pie chart shows the membership distribution of the selected day. d) Insight annotation area.

the highest w value is selected for each day i,





max

,

,…,

,



(5)

Although the color assigned for each day

corresponds to the c

max

value, we consider that the

distribution of the cluster-belonging degrees provides

valuable information that can enhance the exploration

process. Hence, when the user selects a day by

placing the mouse over a certain segment of the circle

chart, a pie chart showing the cluster membership

distribution for that particular day is shown in the

upper right part of the canvas (see Figure 2). In

addition, the activity pattern of that day is highlighted

in the line chart. This makes it easy to compare a

given day’s activity pattern with its corresponding

cluster’s pattern.

The user can also have a detailed visualization of

a particular day by clicking the corresponding

segment of the circle chart.

Then, a new circular map is shown, where each

segment represents the value of the selected parame-

Figure 2: Visualization of 6-week caloric expenditure data.

It can be seen that Saturdays (and Thursdays to some

extent) are more likely to belong to cluster 1. This cluster

groups days with higher activity levels in the afternoon.

Gaining Insight from Physical Activity Data using a Similarity-based Interactive Visualization

121

ter of one minute (see Figure 3). The darker the color

of the segment is, the greater the value (e.g. caloric

expenditure) of that minute. Each ring represents a

day, where the selected day is placed in the middle,

surrounded by the three previous and next three days.

This visualization aims to show the activity patterns

in a more detailed way, as well as to better detect

repeating patterns among days. Figure 3 depicts the

circle heat chart showing the caloric expenditure

patterns of a user during one week.

Finally, the user interface provides a simple yet

useful tool for insight annotation. Users can annotate

the discovered insights by inserting a title and a

description. This self-reflection insight reporting

functionality was added following (Choe et al., 2015)

findings and their recommendation to support easy

capture of self-reflection insights against specific

elements of a dataset that may be associated with the

insight, and access them both for exploration and for

presentation purposes. Saved annotations are linked

to the user-selected parameters, so that the associated

visualization can be easily accessed in the future.

Figure 3: View of the circular heat chart. Each ring

represents a day and is composed of 1440 cells (one per

minute of a day) colored according to the calories

expended.

4 CONCLUSIONS AND FUTURE

WORK

We have presented a novel combination of well-

established automated data analysis and visualization

techniques which permits the user to interactively

identify similarities in time-oriented data. This

approach has been implemented in the practical

scenario of physical activity pattern visualization.

The proposed visualization method is based on

spatially arranging activity data in a given time period

scale (e.g. day, week), automatically categorizing

days into groups based on their activity pattern

similarity and depicting similar activity days with the

same visual representation. Thanks to this visual

highlighting, repeating patterns can be easily spotted

by the user. Moreover, combining this highlighting

with the periodic arrangement of data that circular

maps provide, periodic patterns can be easily

recognized in different time scales (e.g. week or

month). Both techniques are complementary in the

sense that circular maps themselves don’t permit a

quick way to present similar items with the same

visual representation, where each day’s data cannot

be summarized as a ordinal value, and on the other

hand grouping days based on multiple features (and

visually representing this categorization), without a

proper spatial arrangement, doesn’t permit an easy

way to identify periodic patterns. Our approach takes

advantage of the benefits of both visualization

techniques, while avoiding their drawbacks.

Nevertheless, the authors are aware of at least a

couple of limitations of this method: first, the day

clustering technique is currently almost completely

automatic, only the number of groups can be selected

by the user, therefore lacking a fine tuning possibility

for the visualization, considering for example

different day similarity metrics. And second, the

circular map is quite static, that is, information can

only be arranged at daily and weekly time periods.

Additionally, due to the characteristics of circular

maps, older information has less visual impact, which

for some scenarios might be useful, but not always.

Finally, this visualization method follows a top

down interaction model, with an initial overview

visualization combined with a following focused

visualization for more in detail data review.

This visualization method closes the interaction

loop by including the possibility to report insights and

to link them to the visualization configuration which

led to each of them.

We have performed some initial tests on a group

of 5 volunteer subjects for a period of 4 months,

regarding the usability of different visualization

options and the insights provided by them. The

subjects have shown interest in continuing with the

self-monitoring and visual analysis process, as they

perceive it as a quick yet useful tool to quantify their

progress as well as to correct harmful behaviors.

Future work will include the validation of

approach from the user interaction perspective, the

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

122

use of the clustering results to adapt the initial

overview visualization and the exploration of

hierarchical clustering methods, and alternatives such

as latent class analysis (LCA).

ACKNOWLEDGEMENTS

This work was partially funded by the Basque

Government ETORTEK 2012 Program

(ELDERBASK). The opinions herein are those of the

authors and not necessarily those of the funding

agencies.

REFERENCES

Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The

fuzzy c-means clustering algorithm. Computers &

Geosciences, 10(2), 191–203. http://doi.org/

10.1016/0098-3004(84)90020-7.

Carlis, J. V., & Konstan, J. A. (1998). Interactive

Visualization of Serial Periodic Data. In Proceedings of

the 11th Annual ACM Symposium on User Interface

Software and Technology (pp. 29–38). New York, NY,

USA: ACM. http://doi.org/10.1145/288392.288399.

Choe, E., Lee, B., & Schraefel, M. (2015). Revealing

Visualization Insights from Quantified-Selfers’

Personal Data Presentations. IEEE Computer Graphics

and Applications, PP(99), 1–1. http://doi.org/10.1109/

MCG.2015.51.

Consolvo, S., McDonald, D. W., & Landay, J. A. (2009).

Theory-driven Design Strategies for Technologies That

Support Behavior Change in Everyday Life. In

Proceedings of the SIGCHI Conference on Human

Factors in Computing Systems (pp. 405–414). New

York, NY, USA: ACM. http://doi.org/

10.1145/1518701.1518766.

Dork, M., Gruen, D., Williamson, C., & Carpendale, S.

(2010). A Visual Backchannel for Large-Scale Events.

IEEE Transactions on Visualization and Computer

Graphics, 16(6), 1129–1138. http://doi.org/

10.1109/TVCG.2010.129.

Douma, M., Ligierko, G., Ancuta, O., Gritsai, P., & Liu, S.

(2009). SpicyNodes: Radial Layout Authoring for the

General Public. IEEE Transactions on Visualization

and Computer Graphics, 15(6), 1089–1096.

http://doi.org/10.1109/TVCG.2009.183.

Fan, C., Forlizzi, J., & Dey, A. K. (2012). A Spark of

Activity: Exploring Informative Art As Visualization

for Physical Activity. In Proceedings of the 2012 ACM

Conference on Ubiquitous Computing (pp. 81–84).

New York, NY, USA: ACM. http://doi.org/

10.1145/2370216.2370229

Faridani, S., Bitton, E., Ryokai, K., & Goldberg, K. (2010).

Opinion Space: A Scalable Tool for Browsing Online

Comments. In Proceedings of the SIGCHI Conference

on Human Factors in Computing Systems (pp. 1175–

1184). New York, NY, USA: ACM.

http://doi.org/10.1145/1753326.1753502.

Froehlich, J., Dillahunt, T., Klasnja, P., Mankoff, J.,

Consolvo, S., Harrison, B., & Landay, J. A. (2009).

UbiGreen: Investigating a Mobile Tool for Tracking

and Supporting Green Transportation Habits. In

Proceedings of the SIGCHI Conference on Human

Factors in Computing Systems (pp. 1043–1052). New

York, NY, USA: ACM. http://doi.org/

10.1145/1518701.1518861.

Froehlich, J., Findlater, L., Ostergren, M., Ramanathan, S.,

Peterson, J., Wragg, I., … Landay, J. A. (2012). The

Design and Evaluation of Prototype Eco-feedback

Displays for Fixture-level Water Usage Data. In

Proceedings of the SIGCHI Conference on Human

Factors in Computing Systems (pp. 2367–2376). New

York, NY, USA: ACM. http://doi.org/

10.1145/2207676.2208397.

Gotz, D., Wang, F., & Perer, A. (2014). A methodology for

interactive mining and visual analysis of clinical event

patterns using electronic health record data. Journal of

Biomedical Informatics, 48, 148–159.

http://doi.org/10.1016/j.jbi.2014.01.007.

Huang, D., Tory, M., Aseniero, B., Bartram, L., Bateman,

S., Carpendale, S., … Woodbury, R. (2014). Personal

Visualization and Personal Visual Analytics.

IEEE

Transactions on Visualization and Computer Graphics,

Early Access Online. http://doi.org/10.1109/

TVCG.2014.2359887.

Jafarinaimi, N., Forlizzi, J., Hurst, A., & Zimmerman, J.

(2005). Breakaway: An Ambient Display Designed to

Change Human Behavior. In CHI ’05 Extended

Abstracts on Human Factors in Computing Systems

(pp. 1945–1948). New York, NY, USA: ACM.

http://doi.org/10.1145/1056808.1057063.

Khot, R. A., Pennings, R., & Mueller, F.. “Floyd.” (2015).

EdiPulse: Supporting Physical Activity with Chocolate

Printed Messages. In Proceedings of the 33rd Annual

ACM Conference Extended Abstracts on Human

Factors in Computing Systems (pp. 1391–1396). New

York, NY, USA: ACM.

http://doi.org/10.1145/2702613.2732761.

Khovanskaya, V., Baumer, E. P. S., Cosley, D., Voida, S.,

& Gay, G. (2013). “Everybody Knows What You’Re

Doing”: A Critical Design Approach to Personal

Informatics. In Proceedings of the SIGCHI Conference

on Human Factors in Computing Systems (pp. 3403–

3412). New York, NY, USA: ACM.

http://doi.org/10.1145/2470654.2466467.

Larsen, J. E., Cuttone, A., & Jørgensen, S. L. (2013). QS

Spiral: Visualizing Periodic Quantified Self Data. In

Proceedings of CHI 2013 Workshop on Personal

Informatics in the Wild: Hacking Habits for Health

& Happiness. Retrieved from http://orbit.dtu.dk/

fedora/objects/orbit:127504/datastreams/file_d58b9d7

9-608a-46b5-8bde-f250a1d0adcd/content.

Lin, J. J., Mamykina, L., Lindtner, S., Delajoux, G., &

Strub, H. B. (2006). Fish’n’Steps: Encouraging

Physical Activity with an Interactive Computer Game.

Gaining Insight from Physical Activity Data using a Similarity-based Interactive Visualization

123

In P. Dourish & A. Friday (Eds.), UbiComp 2006:

Ubiquitous Computing (pp. 261–278). Springer Berlin

Heidelberg. Retrieved from http://link.springer.com/

chapter/10.1007/11853565_16.

Marcengo, A., & Rapp, A. (2013). Visualization of Human

Behavior Data: The Quantified Self. In Huang, L.H. &

Huang, W. (Eds.), Inovative Approaches of Data

Visualization and Visual Analytics (pp. 236–265). IGI

Global, Hershey.

Marcus, A., Bernstein, M. S., Badar, O., Karger, D. R.,

Madden, S., & Miller, R. C. (2011). Twitinfo:

Aggregating and Visualizing Microblogs for Event

Exploration. In Proceedings of the SIGCHI Conference

on Human Factors in Computing Systems (pp. 227–

236). New York, NY, USA: ACM.

http://doi.org/10.1145/1978942.1978975.

McDuff, D., Karlson, A., Kapoor, A., Roseway, A., &

Czerwinski, M. (2012). AffectAura: An Intelligent

System for Emotional Memory. In Proceedings of the

SIGCHI Conference on Human Factors in Computing

Systems (pp. 849–858). New York, NY, USA: ACM.

http://doi.org/10.1145/2207676.2208525.

Preuveneers, D., & Berbers, Y. (2008). Mobile Phones

Assisting with Health Self-care: A Diabetes Case

Study. In Proceedings of the 10th International

Conference on Human Computer Interaction with

Mobile Devices and Services (pp. 177–186). New York,

NY, USA: ACM. http://doi.org/10.1145/

1409240.1409260.

Rogers, Y., Hazlewood, W. R., Marshall, P., Dalton, N., &

Hertrich, S. (2010). Ambient Influence: Can Twinkly

Lights Lure and Abstract Representations Trigger

Behavioral Change? In Proceedings of the 12th ACM

International Conference on Ubiquitous Computing

(pp. 261–270). New York, NY, USA: ACM.

http://doi.org/10.1145/1864349.1864372.

Shen, Z., & Ma, K.-L. (2008). MobiVis: A Visualization

System for Exploring Mobile Data. In Visualization

Symposium, 2008. PacificVIS ’08. IEEE Pacific (pp.

175–182).

http://doi.org/10.1109/PACIFICVIS.2008.4475474.

Van Wijk, J. J., & Van Selow, E. R. (1999). Cluster and

Calendar Based Visualization of Time Series Data. In

Proceedings of the 1999 IEEE Symposium on

Information Visualization (p. 4–). Washington, DC,

USA: IEEE Computer Society. Retrieved from

http://dl.acm.org/citation.cfm?id=857189.857665.

Weber, M., Alexa, M., & Muller, W. (2001). Visualizing

time-series on spirals. In IEEE Symposium on

Information Visualization, 2001. INFOVIS 2001 (pp. 7–

13). http://doi.org/10.1109/INFVIS.2001.963273.

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