Building Interactive Systems for Social Good [Job Talk]

Building Interactive Systems for Social Good
Matthew Louis Mauriello | Postdoctoral Scholar | Stanford University
makeability lab
University of Delaware | February 19, 2020

Pervasive
Wellbeing
Technology
Lab

Over 30 Billion Internet of
Things (IoT) connected
devices on the planet
(Statista, 2016)

How can we use IoT for Social Good?

How can we use IoT for Social Good?
Providing some benefit to the general public often
in areas like environmental sustainability, education,
and healthcare…

An approach to interactive systems
development that aims to make systems
usable and useful by focusing on the
users, their needs and requirements, and
by applying human factors, usability
knowledge, and design techniques.
This approach enhances effectiveness
and efficiency, improves human well-
being, user satisfaction, accessibility and
sustainability; and counteracts possible
adverse effects of use on e.g. human
health, safety and performance.
HUMAN-CENTERED DESIGN

ISO 9241-210:2010(E)

Design
Build
Evaluate
ISO 9241-210:2010(E)

Formative
Studies
Prototyping User
Studies
Refinement Field
Deployments
ITERATIVE RESEARCH

BUILDING INTERACTIVE SYSTEMS
Energy Efficiency
(Surfacing Information)
Indoor Navigation
(Services On-demand)

An Easy-to-Deploy Temporal Thermography System
Energy Efficiency
[UbiComp‘14, CHI’15 *Honorable Mention*, CHI’16, CHI’17, UbiComp’17, MobileHCI’18, CHI’19]

Common reasons for building inefficiencies include their design,
materials, and age.
To address these issues, renovations and retrofits of existing
building stock has become a pressing need.
The US Department of Energy (DOE), for example, has set a goal
of reducing housing energy use by up to 70%.
Norberg-Bohm, V. and White, C. Building America Program Evaluation. 2004

1-3% of residential buildings are
audited each year.
Common recommendations:
• Sealing air leaks
• Adding insulation
• Improving lighting
• Increasing efficiency of appliances
Including thermal imagery in
reports increases the likelihood of
implementing recommendations.
The Energy Efficiency
Information Gap
Palmer et al. 2013

Energy audits and
thermographic surveying
are time and labor intensive
21

Novice Thermography Studies
CHI’16, CHI’17
Professional Thermography Studies
UbiComp’14, CHI’15
Temporal Thermography
EXAMINING BUILDING THERMOGRAPHY
Energy Efficiency

Energy Efficiency
CHI’16, CHI’17

What challenges do novice users encounter and what
benefits do they perceive about using thermal cameras
for DIY audits?
NOVICE SMARTPHONE FIELD STUDY: RESEARCH QUESTION
Mauriello, M.L., Saha, M., Brown, E., and Froehlich, J.E., (2017).
"Exploring novice approaches to smartphone-based thermographic
energy auditing: a field study." In Proceedings of ACM CHI 2017
Conference on Human Factors in Computing Systems

NOVICE STUDY METHOD: PARTICIPANTS
10 Participants (5 Female)
Avg. Age: 37.7 Years
Avg. Green: 6.7 (7pt Likert)

NOVICE STUDY METHOD: AUDIT TASKS (MISSIONS)
“Investigate your home with your thermal
camera for signs of energy inefficiencies;
collect at least 25 photos that highlight
aspects of your investigation.”

Pre-Study
Questionnaire
Thermography
“Mission”
Introduction
Meeting
Survey
NOVICE STUDY METHOD: FIELD STUDY PROCEDURE

Pre-Study
Questionnaire
Thermography
“Mission”
Survey
Introduction
Meeting
4 Weeks

Pre-Study
Questionnaire
Thermography
“Mission”
Semi-Structured
Interview
Survey
Introduction
Meeting

NOVICE STUDY METHOD: SEMI-STRUCTURED INTERVIEW + PHOTO-ELICITATION
“It was pretty clear to me that
the air seals around this door
were not doing a very good job
of preventing cold air from
leaking into this room.” -P3

I qualitatively coded the survey, interview, and
image data to uncover themes.
NOVICE STUDY METHOD: DATA ANALYSIS

NOVICE STUDY FINDINGS: FIELD ACTIVITIES

“I was stunned to realize that my monitor doesn't
completely turn off when it goes to sleep. It was
unused for the weekend but still appeared hot
[when I came back to work]. So I turned it off
when I went to lunch…” –P4

NOVICE STUDY FINDINGS: SEMI-STRUCTURED INTERVIEWS
Participants thought that the benefits of their application
of thermography included:
• All participants (10) considered the thermal camera a
valuable investigative tool.
Potential Benefits

Participants thought that the benefits of their application
of thermography included:
• All participants (10) considered the thermal camera a
valuable investigative tool.
• Most (8) suggested that thermal imagery could
provide supporting evidence for decisions makers with
respect to making retrofit decisions.
Potential Benefits

“I’ve been meaning to contact my landlord
with these images and say, look, there
seems to be a clear issue here that I think
you should address.” -P7

Participants frequently discussed challenges associated with their
ability to interpret issues they discovered:
• All participants (10) described imagery they did not understand.
Interpretative Issues

• Most (8) believed that their ability to interpret thermographic images was
limited by lack of knowledge (e.g., unfamiliar building systems).

• Most (8) believed that their ability to interpret thermographic images was
limited by lack of knowledge (e.g., unfamiliar building systems).
• Over half (6) found it difficult to determine the significance of issue.

“I don't know how much this really affects the
energy use of my apartment.” -P2

NOVICE STUDY CONCLUSION: RESEARCH OUTCOMES
Characterize novice use of thermal cameras for building energy
auditing activities
Highlights perceived benefits—such as being able to investigate
and collect supporting evidence about efficiency issues
Outlines primary barriers including difficulty determining:
• Missing areas of knowledge
• Severity of problems found
• What actions to take

CHI’16, CHI’17
Energy Efficiency

Mauriello, M.L., and Froehlich, J.E., (2014). "Towards automated
thermal profiling of buildings at scale using unmanned aerial
vehicles and 3D-reconstruction." In Proceedings of the 2014
ACM international Joint Conference on Pervasive and
Ubiquitous Computing. Adjunct Publication.

How can we scale thermographic assessments?
Data Collection from Unmanned Aerial Vehicles (Laguela et. al, 2009)

Energy Auditing Backpack (Oreifej et al. 2014)

Car Mounted Thermographic Cameras (Essess Inc., 2013)

High Fidelity Model Generation

Previtali et al.,Applied Geomatics’14 Bormann et al., Adv. Eng. Informatics’14 Laguela et al., Q. Infrared Thermography’14 Laguela et al., Energy and Buidlings’14 Previtali et al., J. Mobile Multimedia’14
Hamet al., Adv. Eng. Informatics’13 Vidas et al., IEEE Sensors’14 Wang et al., J. Comp. Civil Engineering’13 Ham et al., J. Comp. Civil Engineering’14 Demisse et al., Intl. Conf. Adv. Robotics’13
NO HUMAN PERSPECTIVE IN AUTOMATED THERMOGRAPHY LITERATURE
Reviewed over 30 papers in ‘automated thermography.’ No user studies, no investigations of how human
auditors may use or perceive emerging systems, no discussions of human-centered design, etc.

How is thermography currently being used by
professional energy auditors? And, what benefits and
drawbacks do they identify with automated
thermographic data collection?
PROFESSIONAL THERMOGRAPHY STUDY: RESEARCH QUESTIONS
Mauriello, M.L., Norooz, L., and Froehlich, J.E., (2015). "Understanding
the role of thermography in energy auditing: current practices and the
potential for automated solutions." In Proceedings of ACM CHI 2015
Conference on Human Factors in Computing Systems.
Best Paper Honorable Mention

10 Participants (1 Female)
Average Age: 44.8 Years
Average Exp.: 6.7 Years
PROFESSIONAL STUDY METHOD: PARTICIPANTS

Part 1:
Semi-Structured Interviews
~50 Minutes
Part 2:
Presentation of Design Probes
~40 Minutes
PROFESSIONAL STUDY METHOD: OVERVIEW
Part 3:
Observational Case Study
~3 Hours

Scenario 1
(Text)
Scenario 2
(Text)
Scenario 3
(Text)
Scenario 4
(Video)
Scenario 5
(Mid-Fi Prototype)
PROFESSIONAL STUDY METHOD: PRESENTATION OF DESIGN PROBES

Scenario 1
(Text)
Scenario 2
(Text)
Scenario 3
(Text)
Scenario 4
(Video)
Scenario 5
(Mid-Fi Prototype)
“You are responsible for a small fleet of thermography UAVs. The UAVs fly around semi-autonomously collecting
thermal data about each building on your campus. When abnormalities are detected, the UAVs are programmed to
more closely examine these areas and provide high resolution reports of potential problems. The UAVs reduce labor
costs compared with manual assessments, can investigate otherwise inaccessible areas of buildings (e.g., high
exterior floors), and enable historical reports showing thermal performance over time.”
PROFESSIONAL STUDY METHOD: PRESENTATION OF DESIGN PROBES

Scenario 5 (Mid-Fi Prototype)
3D Reconstruction, Anomaly Detection, Temporal Analysis

PROFESSIONAL STUDY METHOD: DATA ANALYSIS
I qualitatively coded the interview and design
probe data to uncover themes.

Required Knowledge
Client Interactions
Challenges
PROFESSIONAL STUDY FINDINGS: SEMI-STRUCTURED INTERVIEWS

“The thing that is most critical to
understand is how heat behaves and
interacts with different materials.” –P1

social process:
energy auditing is a
socio-technical process

CHALLENGES
All of our energy auditors brought up challenges related to the
practice of thermography, especially related to:
• Weather

CHALLENGES
• Weather
• Untrained or undereducated practitioners

CHALLENGES
• Weather
• Untrained or undereducated practitioners
• Subjectivity in interpreting results

“The reality is that you can have three
guys with the same camera, looking
at the same thing, and have three
totally different reports.” -P2

Automation Benefits
Concerns
PROFESSIONAL STUDY FINDINGS: DESIGN PROBES

Saving time and money Assessing inaccessible areas Scaling up data collection
Automatic anomaly detection Model generation
New types of analyses
PROFESSIONAL STUDY FINDINGS: AUTOMATION BENEFITS

data quality:
automated approaches lack
control of environment

data overload:
how to manage orders of
magnitude more data?

PROFESSIONAL STUDY CONCLUSION: RESEARCH OUTCOMES
An assessment of professional energy auditing and thermography’s
role therein.
A critical examination of emerging automated thermographic
solutions to data collection and analysis.
A set of design recommendations for future energy auditing and
thermographic tools intended for professional use including:
• Obtain environmental data
• Integrate quantitative assessment
• Minimize analysis time

Designing Building Thermography Systems
Energy Efficiency
[UbiComp‘17, CHI’19 ]

TEMPORAL THERMOGRAPHY STUDY: RESEARCH QUESTIONS
How does using the temporal thermography
system influence homeowner’s confidence in
their assessments?
And, how might the temporal thermography
system fit with current professional practices?
Mauriello, M.L., McNally, B., and Froehlich, J.E. (2019). “Thermporal:
An Easy-to-Deploy Temporal Thermographic Sensor System to
Support Residential Energy Audits.” In Proceedings of ACM CHI
2019 Conference on Human Factors in Computing Systems

Easy-to-Deploy
Non-intrusive
Provide Rapid Analysis
Help with Severity Estimation
Holistic Report
TEMPORAL THERMOGRAPHY DEVELOPMENT: DESIGN GOALS

TEMPORAL THERMOGRAPHY RELATED WORK: TEMPORAL DATA COLLECTION
Fox et al., Energy and Buildings ‘14

TEMPORAL THERMOGRAPAHY RELATED WORK: TEMPORAL DATA COLLECTION

TEMPORAL THERMOGRAPHY RELATED WORK: TEMPORAL VISUALIZATIONS

BuildAX Environmental Sensor Toolkit
Finnigan et al. 2017
PHYSIKIT
HOUBEN ET AL., 2016

Thermal
Camera
Motion
Sensor
Pan
Unit
Humidity/Temperature
Sensor
Raspberry Pi
Interchangeable
Mounting Plate
GPS Unit & High Capacity
Battery
Mauriello, M.L., Chazan, J.,
Gilkeson, J., and Froehlich, J.E.,
(2017). "A temporal thermography
system for supporting longitudinal
building energy audits." In
Proceedings of the 2017 ACM
international Joint Conference on
Pervasive and Ubiquitous
Computing. Adjunct Publication.
EASY-TO-DEPLOY
THERMOGRAPHIC
SENSOR SYSTEM
(V3.0)

TEMPORAL THERMOGRAPHY USER STUDY: FINDINGS
Results
Based on user study sessions (n=5):
• Temporal thermography is helpful for evaluating the impact of
environmental conditions
• Deploying hardware remains challenging (e.g., no screen on device for
ensuring connection to Wi-Fi)
• Assessment of temporal issues remains subjective.

TEMPORAL THERMOGRAPY RELATED WORK: QUANTITATIVE THERMOGRAPHY
Madding, 2008

Nardi et al., 2016
Fokaides & Kalogirou, 2011 Dall’O et al., 2013 Albatici et al., 2015

Direct Contact Methods:
Heat Flux Sensors and Thermocouples

FLIR One
Air Quality
Motion
Temp/Humidity

TEMPORAL THERMOGRAPHY DEVELOPMENT: SYSTEM OVERVIEW

Sensing
Calibration,
Processing,
Reporting
TEMPORAL THERMOGRAPHY DEVELOPMENT: SYSTEM OVERVIEW

TEMPORAL THERMOGRAPHY DEVELOPMENT: REVISED VISUALIZATION

TEMPORAL THERMOGRAPHY DEVELOPMENT: VALIDATION EXPERIMENTS

Data Segment Notional THM (deviation) IRT (deviation) Average Temp. Delta
Day 1 R-6.50 R-7.54 (16.00%) R-7.67 (18.00%) 27.47°C
Day 2 R-6.50 R-6.67 (2.61%) R-6.29 (3.23%) 20.96°C
Full Campaign R-6.50 R-6.30 (3.07%) R-6.39 (1.69%) 22.85°C
TEMPORAL THERMOGRAPHY DEVELOPMENT: VALIDATION RESULTS

Study 1:
Novice Field Deployment
1 Week
Part 2:
Expert Review
~90 Minutes
TEMPORAL THERMOGRAPHY STUDY METHOD: TWO-PARTS

TEMPORAL THERMOGRAPHY FIELD DEPLOYMENT: PARTICIPANTS
5 Participants
(3 Male, 1 Female, 1 Prefer Not to Answer)

Pre-Study
Questionnaire
Thermal
Camera
“Mission”
Survey
Introduction
Meeting
TEMPORAL THERMOGRAPHY FIELD DEPLOYMENT: PROCEDURE

Pre-Study
Questionnaire
Thermal
Camera
“Mission”
Survey
Introduction
Meeting
Sensor
System
“Mission”

Pre-Study
Questionnaire
Thermal
Camera
“Mission”
Survey
Introduction
Meeting
Sensor
System
“Mission”
1 Week

Pre-Study
Questionnaire
Thermal
Camera
“Mission”
Semi-Structured
Interview
Post-Study
Follow-up
(~45 days)
Survey
Introduction
Meeting
Sensor
System
“Mission”

I qualitatively coded the survey and interview
data to uncover themes.
TEMPORAL THERMOGRAPHY FIELD DEPLOYMENT: ANALYSIS

Thermal
Camera
“Mission”
Survey
TEMPORAL THERMOGRAPHY FIELD DEPLOYMENT: RESULTS

TEMPORAL THERMOGRAPHY FIELD DEPLOYMENT: THERMAL CAMERA RESULTS

TEMPORAL THERMOGRAPHY FIELD DEPLOYMENT: THERMAL CAMERA RESULTS
“There are some very cold spots in the office, but it’s
hard to tell if they are just because it's unheated or
that there's some big gaps in the insulation.” –NS2

Sensor
System
“Mission”
Survey
TEMPORAL THERMOGRAPHY FIELD DEPLOYMENT: RESULTS

Participant ID Sensor Kit Aimed at Suspected Issue Issue was Found
P1 No No
P2 Yes Yes
Less severe than anticipated
P3 Yes Yes
P4 No Yes
P5 Yes
Based on intuition, not thermal camera
No
TEMPORAL THERMOGRAPHY FIELD DEPLOYMENT: SENSOR SYSTEM RESULTS

P1 No No
P2 Yes Yes
P3 Yes Yes
P4 No Yes
P5 Yes
Based on intuition, not thermal camera mission
No
“It kind of gave me a why. It's
real cold here and it is below
code. Here's some further
information you can look at.
That was super helpful. I can
decide if I agree that this is a
problem, and it’s telling me
something I can do.” –NI2

P1 No No
P2 Yes Yes
P3 Yes Yes
P4 No Yes
P5 Yes
Based on intuition, not thermal camera mission
No
“My reports were negative, so I am not sure what
else to glean from them.” –NS5

Semi-Structured
Interview
TEMPORAL THERMOGRAPHY FIELD DEPLOYMENT: INTERVIEW RESULTS

Interactive Reporting
Data Privacy
Assessment Confidence
Post-Mission Attitudes

Participants described the interactive report in several ways:
• 4 of 5 were positive about receiving the easy-to-read, automatically
generated report.

generated report.
• 4 of 5 liked having temporal data and the additional depth the report
provided by comparison to thermograms alone.

• 4 of 5 were positive about receiving the easy-to-read, automatically generated report.
• 4 of 5 liked having longitudinal data and the additional depth the report provided by
comparison to thermograms alone.
“I like the idea of having a report that I can refer
to again afterward. You get that with pictures
too, obviously. But the reporting aspect gives
you more detail, […] the fact that you had the
environmental and air quality readings also
gave you something more to look at.” –NI3

generated report.
• 4 of 5 liked having longitudinal data and the additional depth the report
provided by comparison to thermograms alone.
• 3 of 5 envisioned using this data as a tool to communicate with
professionals

• 4 of 5 were positive about receiving the easy-to-read, automatically generated report.
• 4 of 5 liked having longitudinal data and the additional depth the report provided by
comparison to thermograms alone.
• 3 of 5 envisioned using this data as a tool to communicate with professionals
“If there's a big problem, that's the thing I want
to fix, but I don't trust that some guy is coming
in and not trying to sell me [on repairs].” –NI2

P1 No No
P2 Yes Yes
P3 Yes Yes
P4 No Yes
P5 Yes
Based on intuition, not thermal camera
No
TEMPORAL THERMOGRAPHY FIELD DEPLOYMENT: CONFIDENCE IN ASSESSMENTS

Post-Study
Follow-up
(~45 days)
TEMPORAL THERMOGRAPHY FIELD DEPLOYMENT: FOLLOW-UP RESULTS

Follow-up Findings
After 45 Days, participants reported:
• 5 of 5 reported thinking more about energy efficiency issues in their
home since participation had ended.

Follow-up Findings
• 5 of 5 reported thinking more about energy efficiency issues in their home
since participation had ended.
“It has made me generally more aware of
where there might be issues and why.” –NS3

Follow-up Findings
• 5 of 5 reported thinking more about energy efficiency issues in their
home since participation had ended.
• 2 of 5 reported making some repairs for air leakage issues; however, all
reported that insulation issues required more savings and planning.

Follow-up Findings
• 5 of 5 reported thinking more about energy efficiency issues in their home
since participation had ended.
• 2 of 5 reported making some repairs for air leakage issues; however, all
reported that insulation issues required more savings and planning.
“I'd say it's kind of too late for a homeowner,
unless you're about to do a renovation.” –NI3

TEMPORAL THERMOGRAPHY EXPERT REVIEW: PARTICIPANTS
5 Participants
(All male)

Scenario 1
(Text)
Scenario 3
(Text)
Interactive
Demo
TEMPORAL THERMOGRAPHY EXPERT REVIEW: PRESENTATION OF DESIGN PROBES

Scenario 1
(Text)
Scenario 3
(Text)
Interactive
Demo
Hardware & Procedure
Study Data & Reports
TEMPORAL THERMOGRAPHY EXPERT REVIEW: PRESENTATION OF DESIGN PROBES

TEMPORAL THERMOGRAPHY EXPERT REVIEW: DESIGN PROBE RESULTS

Raising Awareness

Raising Awareness
Providing Reliable Data

Raising Awareness
Providing Reliable Data
Relationship Building

Installation and Coverage

Motivating Action
Installation and Coverage

DISSERTATION SUMMARY
TEMPORAL THERMOGRAPHY CONCLUSION: CONTRIBUTIONS
Integrating Temporal/Quantitative
analysis will likely provide more specific
insights in the case of insulation
performance.
Increasing homeowner agency may
open new opportunities for professional
auditor and homeowner relations.
While we saw DIY solutions enacted,
motivating larger-scale structural
changes remains challenging. Mauriello, M.L., McNally, B., and Froehlich, J.E. (2019).
“Thermporal: An Easy-to-Deploy Temporal
Thermographic Sensor System to Support Residential
Energy Audits.” In Proceedings of ACM CHI 2019
Conference on Human Factors in Computing Systems

Pervasive Thermography Team
Jon E.
Froehlich
Noa
Chazan
Leyla
Norooz
Erica
Brown
Manaswi
Saha
Matthew
Dahlhausen
Jamie H.
Gilkeson
Julie
Zheng
Matthew
Brady
Anthony
Castrio
Brenna
McNally
Sapna
Bagalkotkar
Cody
Buntain
Samuel
Kushnir
Simran
Chawla

Multi-Sensor Deployments Standard for Temporal Thermography
CONCLUSION: FUTURE WORK

[IJHCS ‘21 To Appear]
Designing Indoor Navigation Systems
Smart Environments

INDOOR NAVIGATION: RESEARCH QUESTIONS
Can we design a low-cost, scalable system that
improves indoor navigational performance and
keeps users engaged in their environment?

Part 1:
Formative Survey
Part 2:
System Design
INDOOR NAVIGATION: OVERVIEW
Part 3:
Evaluation

Part 1:
Formative Survey
Part 2:
System Design
Part 3:
Evaluation
INDOOR NAVIGATION: OVERVIEW

INDOOR NAVIGATION: FORMATIVE SURVEY
Method
We recruited 58 participants and asked about their:
• Experience navigating unfamiliar indoor spaces (generally)
• Experience navigating indoor office environments
• Perceptions of different technology interventions (e.g., AR)

Findings
Based on survey responses:
• Most common reasons for navigational challenges included:
• Confusing layouts or floorplans
• Difficulty orienting on posted maps
• Lack of general signage
• Confusing naming or numbering conventions

Findings
Based on survey responses:
• Perceptions of technology solutions were often mixed:
• Participants were split about mobile applications, with about half viewing these
solution positively and the remaining considered it a hassle.
• Participants were more positive about large screen displays but considered
them to expensive and impractical for most use cases.
• Solutions involving AR were also viewed as problematic because most were
unlikely to carry additional (heavy) technology

ESP8266 Dev Board Digital RGB LED Strip Battery Pack
INDOOR NAVIGATION: SYSTEM DESIGN

INDOOR NAVIGATION: SYSTEM DESIGN

Evaluation n = 18
INDOOR NAVIGATION: EVALUATION

• Does our system lead to performance improvements over
posted maps? Does route complexity or display density impact
performance? And, what might that mean for scalability?

• Does our system lead to performance improvements over
posted maps? Does route complexity or display density impact
performance? And, what might that mean for scalability?
• We used a 3 x 2 within-subjects design with factors for:
• Indicator density (None, Low, and High)
• Route complexity (Simple and Complex).

• Data collected:
• Height and gait information
• Timing and task load (NASA TLX) data on the individual tasks
• Post-study Interview and System Usability Scale (SUS) data
INDOOR NAVIGATION: MEASURES

INDOOR NAVIGATION: TIMING DATA

Findings
Timing data (mph) analyzed using a two-way, repeated
measures ANOVA. Results indicate participants were:
• On average, participants were fastest in:
• High indicator density conditions (m = 1.9;sd = 0.42) versus
• Low (m = 1.81;sd = 0.54) and
• None (m = 1.61;sd = 0.56)
• Density had a significant effect (F(2,34)=3.61, p<0.05).
INDOOR NAVIGATION: DENSITY

Findings
• High indicator density conditions (m = 1.9;sd = 0.42) versus
• Low (m = 1.81;sd = 0.54) and
• None (m = 1.61;sd = 0.56)
• Density had a significant effect (F(2,34)=3.61, p<0.05).
• Post-hoc analysis (H1 = None < High) was also found to be significant (p<0.01)
INDOOR NAVIGATION: DENSITY

Findings
• Complex route conditions (M = 1.93; SD = 0.51) versus
• Simple route conditions (M = 1.62; SD = 0.48).
• Complexity had a significant effect (F(1,17)=14.95, p<0.01).
INDOOR NAVIGATION: ROUTE COMPLEXITY

Findings
• Between both factors, there was also significant interaction effect
(F(2,34)=5.53, p<0.01).
INDOOR NAVIGATION: DENSITY X ROUTE COMPLEXITY

Conclusions
• The system offers speed-ups over posted maps.
• Task complexity is likely to determine the number of display
units that are active but the most complex tasks will likely
influence the total number of units needed to be installed.
INDOOR NAVIGATION: DENSITY X ROUTE COMPLEXITY

INDOOR NAVIGATION: TASK LOAD DATA

Findings
Qualitative feedback on the system indicated that participants:
• Enjoyed using the system
INDOOR NAVIGATION: PARTICIPANT FEEDBACK

Findings
• Reported improved confidence during navigation tasks

Findings
“You feel more confident when you’re
working with it. Using the maps give you a
rough idea, but not a sense of direction.
This fills that gap and helps a lot.”(P55)

Findings
• Appreciated the aesthetics of the system

Findings
• Appreciated the aesthetics of the system
“It was just a part of the environment
and visually pleasing” (P40)

Low-cost, LED Matrices deliver similar benefits ascribed to high-
cost solutions
Offers avenues for improving indoor navigational experiences
Potential for synergies with other technologies to create mixed-
fidelity display ecosystems
INDOOR NAVIGATION: CONCLUSION

Environmental
Sustainability
Smart
Environments
FOCUS AREAS & FUTURE WORK

Environmental
Sustainability
Smart
Environments
Health
& Wellness

[CHI’14]
Health & Wellness
Designing Fitness Support Systems
Health & Wellness
Mauriello, M.L., Gubbels, M., Froehlich, J.E., (2014). "Social Fabric
Fitness: The design and evaluation of wearable e-textile displays to
support group running." In Proceedings of ACM CHI 2014
Conference on Human Factors in Computing Systems.

Affective Navigation Team
Philip
Dasler
Sana
Malik
Jennifer
Healey
Pablo
Paredes

How flexible are users with
respect to path optimization?

Can we use this low-cost wayfinding
system for behavioral nudges?

Environmental
Sustainability
Health
& Wellness
Smart
Environments

Environmental
Sustainability
Smart
Environments
Health
& Wellness
Early
Education

[CHI’12, IJCCI’14]
Health & Wellness
Early Education
Games for Computational Thinking (CTArcade)
Lee, T. Y., Mauriello, M. L., Ahn, J., and Bederson,
B.B., (2014). "CTArcade: Computational thinking with
games in school age children." International Journal
of Child-Computer Interaction, v2.1 (2014): 26-33.

• KYEN Program
STEM Education
An Energy Lifestyle Program for Tweens
[CHI’19]
Mauriello, M.L., Zanocco, C., Stelmach, G., Flora, J., Boudet, H., and
Rajagopal, R., (2019). “An Energy Lifestyles Program for Tweens: A
Pilot Study”. In Proceedings of ACM CHI 2019 Conference on Human
Factors in Computing Systems. Extended Abstract.

Could we including real-time building
informatics data into simulation games
that educate users?

Affective Built Environments for
Health & Well-being

Building Informatics for Real-time
Energy Education Games
Affective Built Environments for
Health & Well-being

makeability lab
@mattm401 | mattm401@stanford.edu
http://web.stanford.edu/~mattm401/
Building Interactive Systems for Social Good
Matthew Louis Mauriello | Postdoctoral Scholar | Stanford University

Building Interactive Systems for Social Good [Job Talk]

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Building Interactive Systems for Social Good [Job Talk]