Since the introduction of application stores for mobile devices there has been an increasing interest to use this distribution platform to collect user feedback. Mobile application stores can make research prototypes widely available and enable to conduct user studies “in the wild” with participants from all over the world. Using apps as an apparatus goes beyond just distributing research prototypes. Consider apps as a tool for research means distributing specifically designed prototypes in order to extend our understanding of mobile HCI. In this tutorial we will provide an overview about recent research in this domain. It will be shown that stringent tasks and users’ motivation are crucial aspects. We will discuss how to design app-based experiments, what kind of users one can expect, and how to avoid ethical and legal issues.

1. How to do Mobile
HCI Research in
the large?
Niels Henze
University of Stuttgart
Visualization and Interactive Systems
Institute
Martin Pielot
Telefónica I+D
HCI and Mobile Computing Group

2. … but lets start with a question:
Who of you ever participated in a user study?

3. do you think that any of these guys
ever did?
Photo by Robertobra,
http://en.wikipedia.org/wiki/File:Guarani_Family.JPG (GFDL)

4. Outline
1. Limitations of common studies
2. Into the large
3. Types of studies
4. What is so special?
5. What works for us
6. Wrap up

5. Outline
1. Limitations of common studies
2. Into the large
3. Types of studies
4. What is so special?
5. What works for us
6. Wrap up

6. User studies at
MobileHCI 2010
20% acceptance rate
43 short+long papers

7. User studies at
MobileHCI 2010
20% acceptance rate
43 short+long papers
subjects per paper
http://nhenze.net/?p=810

8. User studies at
MobileHCI 2010
20% acceptance rate
43 short+long papers
subjects per paper
subject’s gender
http://nhenze.net/?p=810

9. all with a university degree, recruited in
the Institute community
students or employees at our
university User studies at
recruited through flyers, posters and
various mailing lists at the university MobileHCI 2010
20% acceptance rate
10 university students and 2 participants
43 short+long papers
are marketing professionals
subjects per paper
undergraduate or graduate students at
subject’s gender
the local university studying a variety of
majors often a biased sample
university students
most subjects were students with a
background in computer sciences
most participants were students
studying or working in the University of
Glasgow
members in a joint research project
http://nhenze.net/?p=810

10. small samples

11. artificial context

12. artificial task

13. convenient samples

14. Some male students from the lab
took part in our study...
Small sample size isn’t necessarily an issue for a
study
Not every study needs a perfect sample of the
population
Focussing on studies with few subjects prevents
many findings
We stew in our own juices if using our own
students by default

15. User studies at
MobileHCI 2011
22.8% acceptance rate
63 short+long papers
subjects per paper
http://nhenze.net/?p=865

16. Some motivation
Large numbers are expensive in the lab
– 1,000 subjects for an hour -> 10,000€
– 1,000 subjects for an hour -> 6 month
– 1,000 subjects from around the world -> impossible
Different contexts are hard to address
– We have no airplane in our lab
– Don’t want to train ticket for my participant
– And what are the relevant contexts anyway?

17. Outline
1. Limitations of common studies
2. Into the large
3. Types of studies
4. What is so special?
5. What works for us
6. Wrap up

18. Example of getting large… Target selection on
mobile phones
thirty right-handed
subjects
different target locations
and sizes
[Park2008MobileHCI]

19. Target selection on
mobile phones
thirty right-handed
subjects
different target locations
and sizes
Taps are skewed
fixed posture
single device
Korean students
vague results
[Park2008MobileHCI]

20. …same thing in the
large
game published on the
Android Market
we inform the player
about the study
just looks like an ordinary
game
participants get some
introduction
they tap the targets
We vary targets’ size and
position
there is even a high score
list

21. published on the
Android Market
100,000 installations in
three months
120 million touch events
more than hundred
different devices
players from all over the
world

22. [Park2008MobileHCI]

23. [Henze2011MobileHCI]

24. Outline
1. Limitations of common studies
2. Into the large
3. Types of studies
4. What is so special?
5. What works for us
6. Wrap up

25. Types of work
Proof of concept
– Showing that an idea/concept/product works
– Lots of users, good ratings, positive comments, ...
App stores as research tool
– Experience report
– Ethical and legal issues
Investigating app-specific aspects
– How a specific app is used
– Compare different visualizations
Observing general aspects
– Learn about how people and devices behave
– How are apps how, how people touch the screen, ...

26. Proof of concept

27. Smule’s iPhone
Ocarina
music instrument for the
iPhone
million installations
[Wang2009NIME]

28. Shapewriter
developed gesture-based
keyboard + notepad
qualitative feedback from
App Store comments
[Zhai2009CHI]

29. App stores as research tool

30. Into the wild with
Hungry Yoshi
location based game for
the iPhone
94,642 unique downloader
investigated how to get
subjective feedback
[McMillan2010Pervasive]

31. 100%
83.68% 81.31%
80%
60% 54.76%
Experience from
5 Studies
40% compare amount of
collected data
20% experience with collecting
7.32% qualitative data
0.46%
0% discuss internal and
external validity
[Henze2011IJMHCI]

32. Local vs. wild
locale study with 11
participants
wild study with over
10,000 users
combine the findings of
both approaches
[Morrison 2012CHI]

33. Investigating
app-specific
aspects

34. Ratings for Mobile
Applications
compare amount of
collected data
experience with collecting
qualitative data
discuss internal and
external validity
[Girardello2010DSZ]

35. Compare off-screen
visualisations
using repeated measures
using a tutorial for a map
application
and using a simple game
[Henze2010MobileHCI] [Henze2010MobileHCI]

36. Observing general aspects

37. Falling Asleep with … appazaar
[Böhmer2011MobileHCI]

38. A Study of Battery Life
[Ferreira2011Pervasive]

39. app stores as a investigating app- investigating
proof of concept
research tool specific aspects general aspects
[Wang2009NIME] [McMillan2010RiL] [Girardello2010DSZ] [Hood2011IJTR]
[McMillan2010Pervasive]
[Zhai2009CHI] [Riccamboni2010IB] [Henze2011MobileHCIa]
[Henze2011IJMHCI]
[Gilbertson2008CiE] [Kuhn2010MM] [Henze2011MobileHCIb]
[Miluzzo2010RiL]
[Watzdorf2010LocWeb]
[Poppinga2010OMUE] [Yan2011MobiSys]
[Ferreira2011Pervasive]
[Oliver2010HotPlanet] [Budde2010IoT]
[Morrison2010RiL] [Buddharaju2010CHI]
[Karpischek2011RiL]
[Sahami2011CHI]
[Henze2010MobileHCI]
[Verkasalo2010MB]
[Pielot2011ELV] [Henze2010NordiCHI]
[Böhmer2011MobileHCI]
[Cramer2010UbiComp]
[Morrison2011CHI]
[Henderson2009HotPlanet]
[Norcie2011ELV]
Ethics and legal issues

40. Outline
1. Limitations of common studies
2. Into the large
3. Types of studies
4. What is so special?
5. What works for us
6. Wrap up

41. but what is special about app store
studies?

42. App-based vs. other studies
Common con- Mining existing App-based
trolled studies data studies
Few participants Many participants Many participants
Artificial context Natural context Natural context
Defined tasks
Defined task No tasks
(if needed)
Total control over Weak control over
No control
participants participants
Heavily biased Biased to unbiased
Unbiased sample
sample sample

43. You have to “sell” your study
The study has a goal
– Collect information about specific behaviour
– Performance for a specific task
Users have to install the app on their own will
– App needs a purpose
– Good ratings, high ranking
Find a compromise
– Maintain the goals of the study
– Attract sufficient participants

44. Types of apps
Applications Games Widgets

45. 100,000
90,000
80,000 Participants
70,000 How do we count the
60,000 number of participant?
50,000
40,000
30,000
20,000
10,000
0
installations opt-in active users
[McMillan2010Pervasive] [Morrison2010RiL]

46. US Android users US population
60%
Participants
50%
How do we count the
40% number of participant?
A good sample of the
30% population?
20%
10%
0%
18-34 35-44 45-54 55-64 65+
[Nielsen2011] [USCensusBureau2008]

47. Collecting information
Objective data
– As early as possible [Henze2011IJMHCI]
– More than just the task performance
• All aspects that affect the results
• E.g. device type, local, time, screen size, resolution, ...
• In particular: a version number
– Compromise between permissions and data to
collect

48. Collecting information
Subjective data
– App Store comments can provide information
• but usually don't [Henze2011IJMHCI]
• Might help to claim an app is great (e.g. [Zhai2009CHI])
• Ratings without baseline are meaningless
– Investigated how to get subjective feedback
[McMillan2010Pervasive]
• In-game “tasks” with dynamically loaded questions
• Integration with Facebook
• Interviewed 10 people over VoIP for $25

49. Collecting information
You have to measure what you intend to
measure!
Case Study: Pocket Navigator [Pielot2012CHI]

50. motivation:
distraction
one in six (17%) cell-toting
adults say they have been
so distracted while talking
or texting that they have
physically bumped into
another person or an
object
Madden and Rainie, 2010,
http://pewinternet.org/Reports/2010/Cell-Phone-Distractions.aspx

51. pocketnavigator
navigation system similar
to Google Maps
runs on OpenStreet Maps

52. pocketnavigator
navigation system similar
to Google Maps
runs on OpenStreet Maps
key innovation: convey
navigation information in
vibration patterns

54. evaluated in a
field study
vibration patterns found to
be effective
they reduce level of
distraction

55. evaluated in
field study
vibration patterns found to
be effective
they reduce level of
distraction
but, users were no experts
and did not use navigation
support out of a necessity

56. evaluated in
field study
vibration patterns found to
be effective
they reduce level of
distraction
but, users were no experts
and did not use navigation
Instead of bringing the user into the “lab” of a necessity
support out
we bring the lab to the user’s daily life

57. Collecting data,
Feb – Dec 2011

58. quick facts
18,000 downloads
mostly US and Europe

59. quick facts
18,000 downloads
mostly US and Europe
Between Feb – Dec 2011
8,187 routes calculated
34,035,316 log entries
9,400 hours of usage

60. quick facts
18,000 downloads
mostly US and Europe
Between Feb – Dec 2011
8,187 routes calculated
34,035,316 log entries
9,400 hours of usage
a lot of data! But …

61. pedestrian
navigation?

62. pedestrian
navigation?
we cannot prevent people
from using the app
anywhere, e.g. in cars

63. pedestrian
navigation?
we cannot prevent people
from using the app
anywhere, e.g. in cars
in fact, 87% of all log data
are from indoor use 

64. pedestrian
navigation?
we cannot prevent people
from using the app
anywhere, e.g. in cars
in fact, 87% of all log data
are from indoor use 
hence filtering (route
length, travel
time, movement speed)
required

65. lessons learned
double-check that you
measure the intended use!
filter data might be
necessary
acknowledge the fact that
there is always uncertainty
[Pielot2012CHI]

66. Collecting information
You have to measure what you intend to
measure!
Another Example: TypeIt

67. TypeIt
compare approaches to
improve text entry
people play as along as
they want
[Henze2012CHIa, Henze2012CHIb, Henze2012Text]

68. TypeIt
condition affects the
number of played levels
4 conditions

69. TypeIt
condition affects the
An ANOVA shows that the number of played levels
feedback has a significant
effect on the total number of
levels played (p<.01).

70. TypeIt
condition affects the
Analysis of covariance number of played levels
(ANCOVA) is a general linear Factor the number of
played levels out using an
model which blends ANOVA ANCOVA
and regression. (Wikipedia)

71. Realy stupid
hope Stupid waste of time!!!
cailan
FC the rabbit.... uninstalled
Godimus Prime
Ready for prime
Its ok Stupid waste of time.
erika lance time
boring and dumb. Users don’t care if it’s a
Beba research prototype
Stupid and offincive
to my pet rabbit bayleigh
Logan 1 word...... dumb!
josue
5 stars if there is a way to turn the music off.
Doesnt go to well with slipknot
Allen
What the hell is this?? Boo!
Luci Cullen Girl

72. Ready for prime
time
Users don’t care if it’s a
research prototype
Low quality results in low
ratings

73. Ready for prime
time
users don’t care if it’s a
research prototype
low quality results in low
ratings
and few install installations

74. Ethical and legal issues
“One should treat others as one would like
others to treat oneself” [Flew1979Dictionary]
“Primum non nocere”/”First, do no harm”
(Thomas Sydenham)

75. 6.96% 57.28%
Informed consent
Presentation highly affects
the conversion rate
67.42% 87.57%
[Pielot2011ELV]

76. Informed consent
Presentation highly affects
the conversion rate
Participants aren't aware
what data is collected
[Morrison2011CHI]

77. Regulations
Which rules to follow?

78. “any information relating to an identified
or identifiable natural person” Regulations
• Transparency: the persons whose data
Which rules to follow?
are being collected or accessed have the
right to be informed when such data e.g. EU Data Protection
processing is taking place. Directive
• Legitimate purpose: data can only be
collected for specific purposes
• Proportionality: data should be
processed in a fashion that is not
excessive beyond the purposes for which
they were collected
[Henderson2009HotPlanet]

79. Outline
1. Limitations of common studies
2. Into the large
3. Types of studies
4. What is so special?
5. What works for us
6. Wrap up

80. … or what works for us

81. number of installations
400
350
Games vs. Apps
Thousands
300
250 our games are more
successful
200
150
100
50
0

82. games
15.6%
Games vs. Apps
our games are more
successful
there are more apps than
games
apps
84.4%
available in the Android Market
http://www.androlib.com/appstatstype.aspx

83. Games vs. Apps
our games are more
successful
there are more apps than
games
players execute the
strangest tasks

84. Games vs. Apps
our games are more
successful
there are more apps than
games
players execute the
strangest tasks
widgets and background
services are perfect for
longitudinal observations

85. Games vs. Apps
our games are more
successful
there are more apps than
games
players execute the
strangest tasks
widgets and background
services are perfect for
longitudinal observations
but sometimes an app is
just the only option

86. Informing the user
provide information in the
Market

87. Informing the user
provide information in the
Market
show a modal dialog at the
first start

88. Informing the user
provide information in the
Market
show a modal dialog at the
first start
provide more information
and a link to an about
page

89. Publishing
fancy screenshots and icon
(that’s the first thing
someone sees)
title & description contain
words users search for
of course I don’t want to
miss a single user
prepare a dedicated
webpage for each app

90. Playing with the
market
frequent updates

91. Playing with the
market
frequent updates
rate your app as soon as it
becomes available

92. Keep it simple
focused and specialized
studies

93. Keep it simple
focused and specialized
studies
learning by doing

94. Keep it simple
focused and specialized
studies
learning by doing
release early, often, and
try it again if it doesn’t
work

95. Logging
use http and port 80
to transmit data

96. Logging
use http and port 80
to transmit data
store unaggregated
measures
[Henze2012CHI]

97. CSV files from ~400,000 users
Logging
use http and port 80
to transmit data
store unaggregated
measures
consider limited resources
in total:
392,401 files
27,331,383,646 bytes

98. Compressed binary data from
less than 3,000 users Logging
use http and port 80
to transmit data
store unaggregated
measures
consider limited resources
seriously!

99. 200$ for AdMob over a couple of days
Advertisements
does not work!
TapSnap: http://tiny.cc/tapsnap

100. 100$ for AppBrain on a single day
Advertisements
does not work!
well sometimes it does!
TypeIt II: http://tiny.cc/TypeIt2

101. 100$ for AppBrain on a single day
Advertisements
does not work!
well sometimes it does!
focus all your efforts on a
very short time
get additional users
naturally
TypeIt II: http://tiny.cc/TypeIt2

102. What do?
No harm! Release
Inform the user Keywords, description, ...
Don't store data you don't want Rate and comment
Focus your advertisement efforts
Choose a type of app
Games worked for me Test it
But if you have a great system Well I don't do that
anyway... At least fix it
Sell you study Think about the data
You compete with commercial apps Do you store everything interesting
Graphics, design, ... Can you store data from 10,000
users?
Can you analyse it?

103. small samples

104. large
small samples

105. artificial context

106. natural?
artificial context

107. artificial task

108. artificial task?

109. convenient samples

110. very
convenient samples
but how
bad is it?

111. How to do Mobile
HCI Research in
ethnography, controlled the large?
experiments, observations,
… can all work in the large
collect data Niels Henze
University of Stuttgart
early, release often, be Visualization and Interactive Systems
flexible Institute
respect
ethics, consider
Martin Pielot
Telefónica I+D
regulations HCI and Mobile Computing Group

112. References
[Morrison 2012CHI] Alistair Morrison, Donald McMillan, Stuart Reeves, Scott Sherwood, Matthew Chalmers: A Hybrid
Mass Participation Approach to Mobile Software Trials. Proceedings of CHI, 2012.
[Wang2009NIME] Ge Wang: Designing Smule’s iPhone Ocarina. Proc. NIME, 2009.
[Zhai2009CHI] Zhai, S., Kristensson, P.O., Gong, P., Greiner, M., Peng, S., Liu, L. Dunnigan, A., Shapewriter on the iPhone:
from the laboratory to the real world. Adjunct Proc. CHI, 2009.
[Gilbertson2008CiE] Paul Gilbertson, Paul Coulton, Fadi Chehimi, Tamas Vajk: Using 'Tilt' as an Interface to control 'No
Button' 3-D Mobile Games. ACM Computers in Entertainment, 2008.
[Oliver2010HotPlanet] Earl Oliver. The Challenges in Large-Scale Smartphone User Studies. Invited talk @
HotPlanet, 2010.
[McMillan2010RiL] Donald McMillan: iPhone Software Distribution for Mass Participation. Proc. Research in the Large
Workshop @ UbiComp, 2010.
[Miluzzo2010RiL] Emiliano Miluzzo, Nicholas D. Lane, Hong Lu, Andrew T. Campbell: Research in the App Store Era:
Experiences from the CenceMe App Deployment on the iPhone. Proc. Research in the Large Workshop @
UbiComp, 2010.
[Henze2011IJMHCI] Niels Henze, Martin Pielot, Benjamin Poppinga, Torben Schinke, Susanne Boll: My App is an
Experiment: Experience from User Studies in Mobile App Stores, accepted by the International Journal of
Mobile Human Computer Interaction (IJMHCI), 2011
[McMillan2010Pervasive] Donald McMillan, Alistair Morrison, Owain Brown, Malcolm Hall & Matthew Chalmers:
Further into the Wild: Running Worldwide Trials of Mobile Systems, Proc. Pervasive 2010.
[Cramer2010UbiComp] Henriette Cramer, Mattias Rost, Nicolas Belloni, Didier Chincholle, Frank Bentley: Research in
the Large. Using App Stores, Markets, and Other Wide Distribution Channels in Ubicomp Research. Adjunct
Proc. Ubicomp, 2010.
[Morrison2010RiL] Alistair Morrison, Stuart Reeves, Donald McMillan, Matthew Chalmers: Experiences of Mass
Participation in Ubicomp Research, Proc. Research In The Large Workshop at Ubicomp, 2010.
[Poppinga2010OMUE] Benjamin Poppinga, Martin Pielot, Niels Henze, Susanne Boll: Unsupervised User Observation in
the App Store: Experiences with the Sensor-based Evaluation of a Mobile Pedestrian Navigation Application.
Proc. OMUE in conjunction with NordiCHI, 2010.

113. References
[Pielot2011ELV] Martin Pielot, Niels Henze, Susanne Boll: Experiments in App Stores – How to Ask Users for their
Consent?, Proceedings of the CHI workshop on Ethics, logs & videotape, 2011.
[Henderson2009HotPlanet] Tristan Henderson, Fehmi Ben Abdesslem: Scaling Measurement Experiments to Planet-
Scale: Ethical, Regulatory and Cultural Considerations. Proc. HotPlanet, 2009.
[Morrison2011CHI] Alistair Morrison, Owain Brown, Donald McMillan, Matthew Chalmers: Informed Consent and
Users' Attitudes to Logging in Large Scale Trials. Adjunct Proc. CHI, 2011.
[Norcie2011ELV] Greg Norcie: Ethical and Practical Considerations For Compensation of Crowdsourced Research
Participants, Proc. ETHICS, LOGS and VIDEOTAPE @ CHI, 2011.
[Girardello2010DSZ] A. Girardello, F. Michahelles, Explicit and Implicit Ratings for Mobile Applications. In 3. Workshop
“Digitale Soziale Netze” and der 40. Jahrestagung der Gesellshaft für Informatik, September 2010, Leipzig.
[Riccamboni2010IB] Rodolfo Riccamboni, Alessio Mereu, Chiara Boscarol: Keys to Nature: A test on the iPhone market.
Tools for Identifying Biodiversity: Progress and Problems, 2010.
[Kuhn2010MM] Michael Kuhn, Roger Wattenhofer, Samuel Welten: Social Audio Features for Advanced Music Retrieval
Interfaces. Proc. MM, 2010.
[Yan2011MobiSys]Bo Yan, Guanling Chen: AppJoy: Personalized Mobile Application Discovery. Proc. MobiSys, 2011.
[Budde2010IoT] Andreas Budde, Florian Michahelles: Product Empire - Serious play with barcodes. Proc. IoT, 2010.
[Karpischek2011RiL] Stephan Karpischek, Geron Gilad, Florian Michahelles: Towards a Better Understanding of Mobile
Shopping Assistants - A Large Scale Usage Analysis of a Mobile Bargain Finder Application. Workshop on
Research in the Large @ UbiComp, 2011.
[Henze2010MobileHCI] Niels Henze, Susanne Boll: Push the Study to the App Store: Evaluating Off-Screen Visualizations
for Maps in the Android Market, Proc. MobileHCI, 2010
[Henze2010NordiCHI] Niels Henze, Benjamin Poppinga, Susanne Boll: Experiments in the Wild: Public Evaluation of Off-
Screen Visualizations in the Android Market, Proc. NordiCHI, 2010.

114. References
[Hood2011IJTR] Jeffrey Hood, Elizabeth Sall, Billy Charlton: A GPS-based Bicycle Route Choice Model for San Francisco, California.
Transportation Letters: The International Journal of Transportation Research, 2011
[Henze2011MobileHCIa] Niels Henze, Enrico Rukzio, Susanne Boll: 100,000,000 Taps: Analysis and Improvement of Touch
Performance in the Large, Proceedings of MobileHCI, 2011
[Henze2011MobileHCIb ] Niels Henze, Susanne Boll: Release Your App on Sunday Eve: Finding the Best Time to Deploy
Apps, Adjunct proceedings of MobileHCI, 2011
[Henze2012CHIa] Niels Henze, Enrico Rukzio, Susanne Boll: Observational and Experimental Investigation of Typing Behaviour
using Virtual Keyboards on Mobile Devices, Proceedings of CHI 2012.
[Henze2012CHIb] Niels Henze: Hit it!: an apparatus for upscaling mobile HCI studies. Proceeding of CHI Extended Abstracts, 2012.
[Henze2012Text] Niels Henze: Ten male colleagues took part in our lab-study about mobile texting, Proceedings of the Workshop
on Designing and Evaluating Text Entry Methods in conjunction with CHI, 2012.
[Watzdorf2010LocWeb] Stephan von Watzdorf, Florian Michahelles: Accuracy of Positioning Data on Smartphones. Proc.
LocWeb, 2010.
[Ferreira2011Pervasive] Denzil Ferreira, Anind K. Dey, Vassilis Kostakos: Understanding Human-Smartphone Concerns: A Study of
Battery Life. Proc. Pervasive, 2011.
[Buddharaju2010CHI] Pradeep Buddharaju, Yuichi Fujiki, Ioannis Pavlidis, Ergun Akleman: A Novel Way to Conduct Human Studies
and Do Some Good. Adcunct Proc. CHI, 2010.
[Sahami2011CHI] Alireza Sahami, Michael Rohs, Robert Schleicher, Sven Kratz, Alexander Müller, Albrecht Schmidt: Real-Time
Nonverbal Opinion Sharing through Mobile Phones during Sports Events, Proc. CHI 2011.
[Verkasalo2010MB] Hannu Verkasalo: Analysis of Smartphone User Behavior, Proc. Ninth International Conference on Mobile
Business, 2010.
[Böhmer2011MobileHCI] Matthias Böhmer, Brent Hecht, Johannes Schöning, Antonio Krüger, Gernot Bauer: Falling Asleep with
Angry Birds, Facebook and Kindle – A Large Scale Study on Mobile Application Usage. Proc. MobileHCI, 2011.
[Agarwal2010HotNets] Sharad Agarwal, Ratul Mahajan, Alice Zheng, Victor Bahl: There’s an app for that, but it doesn’t work.
Diagnosing Mobile Applications in the Wild. Proc. Hotnets, 2010.
[Morrison2010RiL] Alistair Morrison, Matthew Chalmers: SGVis: Analysis of Mass Participation Trial Data. Proc. Research In The
Large Workshop at Ubicomp, 2010.
[Lane2010CM] Nicholas D. Lane, Emiliano Miluzzo, Hong Lu, Daniel Peebles, Tanzeem Choudhury, Andrew T. Campbell: A Survey
of Mobile Phone Sensing. IEEE Communications Magazine, 2010.