Mobile Sensing
VIII – Social and
Psychological Sensing
Spring 2015
Petteri Nurmi
Matemaattis-luonnontieteellinen tiedekunta /
Henkilön nimi / Esityksen nimi
www.helsinki.fi/yliopisto
17.4.2015
1
Learning Objectives
• How social and psychological sensing operate?
What are the main sensors? What are thin slices?
• How co-location can be characterized? What are
proxemics? How can co-location be determined?
• What is prosodic sensing? Why is it important?
• What are F-formations? Why are they important for
social/psychological sensing?
• What other sensors can be used and how?
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Social and Psychological
Sensing?
• Social and psychological sensing refer to the
extraction of information about
• Characteristics of social phenomena, including social
interactions, group behavior, and so forth
• Psychological states, such as personality, mood, etc.
• Phenomena seldom directly observable
• Several so-called “weak cues” need to be combined
• Ground truth typically collected through subjective
responses (standardized questionnaires)
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Thin Slices
• Thin slicing refers to the ability of finding patterns
based on short measurement windows
• Outcomes of many social psychology situations can be
detected using short amount of measurements
‒ Accuracy of predicting the outcome correctly higher than
chance, improves up until around 5 minute mark
• Not only tells about the social dynamics of situations,
but also about the judgments made by individuals
‒ E.g., all people make assumptions about personality
èSensing need not run continuously, as long as start
point of interactions can be identified reliably
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Information Types
• Co-location patterns a key characteristics for
extracting social information
• Without co-location (physical or virtual), no social
behavior possible
• Spatial and orientational characteristics relate to
social norms and characterize social interactions
• Psychological sensing relies on indirect behavioral
variations – both between users and within a user
• Speech variations: prosodic sensing
• Behavior variations: changes in activity patterns,
including changes in physical activity and in device
interaction patterns
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Co-location
• Co-location refers to people (or objects) being
together in the same location
• Prerequisite for social interactions and hence an
important part of social sensing
‒ Mapping social networks
‒ Estimating spread of diseases, team performance,
dissemination of ideas, etc.
‒ Flocking, “follow-leader” detection, etc.
• Essential for any type of application that assumes
cooperation between users
‒ Authentication / device association
‒ Cooperative work applications
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Proxemics and Social Sensing
• Co-location relates to social situations
through theory of proxemics
• Interpersonal distances characterize social
distances
• Four categories: intimate, personal, social,
and public
‒ Typical distances: <1m, 1-2m, 2-5m, 5-10m
• Subject to cultural variations
• In social interactions also take into account
the way people are facing (e.g., queuing vs.
talking to people)
Source:
http://en.wikipedia.org/wiki/Proxemi
cs/
• Hence meaning of co-location dependent on
physical distance as well
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Co-location sensing
• Co-location sensing refers to the detection of people that
are co-located
• Several different technologies can be used:
• Positioning: co-location can be determined by comparing
locations of individuals
• Proximity sensors: infrared, Bluetooth, WiFi direct
‒ Different ranges and applicability requirements with each
technology, not necessarily supported on all devices
• Distance based
‒ Ultrasound and radio signals decay as function of distance
• Ambient fingerprint
‒ Comparing environmental parameters, e.g., audio, lightning,
and other factors
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Co-location Sensing:
System Level View
• Co-location detection requires careful system design
• Resolution best when multiple sensing technologies
combined together è high energy footprint
• Need to compare measurements across devices è
increased energy drain from sharing measurements
• Potential privacy concerns in sharing measurements
• Core design considerations from system point:
1. Accurate activity/inactivity detection to focus the sensing
on periods where co-location patterns changing
2. Rely on privacy sensitive features where possible, e.g.,
compare spectral audio features instead of raw signals
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Example: Mapping Social
Networks
• Mapping social networks one of the most important
applications of co-location detection
• On an individual level, characterize extent and nature of
social interactions
‒ In turn provides cues about personality, mood, etc.
• On an aggregate level, characterize dissemination
patterns and provide indications of cohesion
‒ Team cohesion important for group performance
‒ Dissemination patterns relate to speed of exchange:
including, ideas, diseases, and so forth
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Example: Authentication
• Zero-Interaction Authentication: user can be
authenticated with a device if (s)he is within close
proximity of the target device
• Typically requires users to carry a security token
‒ Mobile phone, car key, work badge or other token
• Vulnerable to so-called ghost and leech attacks
• One attacker close to token, other close to device,
messages relayed between the token and target device
• Co-location sensing one of the most promising
countermeasures
‒ Require token can be observed AND that environments
sufficiently similar
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Co-location sensing: Wearable
vs. Mobile Devices
• Co-location sensing extensively studied in wearable
computing using so-called sociometric badges
• Wearable badge that integrates several sensors
• Proximity typically detected using a combination of position
related sensors (Bluetooth, IR, WiFi) and analysis of audio (coconversation)
• (Much) more difficult on mobile devices
• Devices in arbitrary placements and often obstructed
• Hardware variations influence sensing accuracy
• Sensors may be turned off or not available on all participating
devices
‒ Former problem particularly for Bluetooth, latter for any other sensor,
especially on “feature phones”
• Badges typically ultimately provide line-of-sight distance è
can capture co-location at fine proxemics levels
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Co-location sensing baselines:
Positioning and Infrastructure Sensing
• Comparing location estimates of two (or more)
people provides a baseline for co-location detection
Co-located
Distant
• Indoor positioning (WiFi and other RF technologies,
magnetometer): requires mapping the environment
• Outdoor positioning: GPS
• Infrastructure-based sensing
• Proximity sensing, e.g., WiFi sniffing (detecting beacon
scan messages), Apple iBeacon, etc.
‒ Performance determined by range of technology, widely
used for coarse level customer analytics
• Computer vision, including depth-camera sensing
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Proximity Sensors
• The simplest case of detecting (some form of) co-location
is to use proximity sensing
• Devices are assumed to be co-located if they are within the
transmitting range of a sensor
• Resolution of co-location detection depends on the range
of the transmitting technology
• WiFi: up to 30 meters, Bluetooth: around 10-15 meters
• Thus, e.g., people in nearby office rooms are detected as
co-located even if not really there
• Transmitters need to be turned on: Bluetooth automatically
goes into discoverable mode after 2 minutes
• Highly useful as an infrastructure-based approach
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Coarse-scale Proximity Sensing
• The coarsest granularity of proximity sensing is
provided by GSM signals
• Range can be up to 30km
• But also highly energy-efficient as GSM signals “free”
as the phone requires them anyway
• Granularity not sufficient for detecting whether two
devices are currently co-located
• However, can be used to map social networks, and
hence also the extent of social interactions
• The more locations and times of day two devices are in
the same GSM cell, the higher the likelihood of some
form of social relationship between owners of devices
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Radio Frequency Based
Co-Location Sensing
• Similarity of radio frequency (RF) environment widely used
technique for co-location determination
• Attenuation: radio (and sound) waves decrease as a
function of distance
• Also obstacles and environmental conditions affect radio
propagation è exact distance cannot be determined
• Radio frequency similarity measures instead compare
“distances” through similarity of radio environment
• The higher the similarity, the closer the devices
• Operate on measurements consisting of reference point
identifiers and measurements of signal characteristics
‒ E.g., Bluetooth & WiFi: MAC addresses of transmitting devices
and their received signal strength (RSS) values
‒ GPS: satellite identifiers, estimated signal-to-noise ratio (SNR)
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RF-Based Co-Location Sensing
• Formally, RF sensing considers measurements of
the form: {(mx1,sx1),...(mxn,sxn)} where
• mxi is (some form of) identifier of ith transmitter
• sxi is (some form of) signal characteristics of ith
transmitter as observed by device x
• Mx and My defined as the sets of transmitters devices x
and y can observe
• Proximity sensing simplest way to adopt RF
measurements for co-location sensing
• If at least one shared transmitter, they are necessarily
within distance 2r, where r is the range of the transmitter
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RF-Based Co-Location Sensing
• The resolution of RF-based co-location sensing can be
increased by adopting a feature-based approach
• Co-location detection seen as a classification problem where
feature vectors derived from measurements of two devices
1. Set-based metrics:
• Subset count N∩(x,y) = |Mx ∩ My |
• Number of unaccounted transmitters:
Nx (x,y) = |Mx| + |My| - 2 N∩(x,y)
• Jaccard-similarity: J(x,y) = |Mx ∩ My| / |Mx ∪ My |
• Generally, the more overlap in the set of transmitters, the
closer the devices
• Respectively, the more unaccounted transmitters, the further
apart the devices are
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RF-Based Co-Location Sensing
2. Signal characteristics similarity measures
•
Basic idea to estimate attenuation by comparing
similarity of signal characteristics
•
‒
Require shared ordering of transmitters
‒
Potentially need signal preprocessing, e.g., normalizing
values and/or filling in missing/unaccounted values
•
•
Euclidean distance: √∑ (sxi- syi)2
Extended Tanimoto: (sx ∙ sy) / ( |sx |2 + |sy|2 - sx ∙ syi )
•
Hamming distance: ∑ M∪ |sxi- syi|
•
Exponential difference: ∑ M∪ exp(|sxi- syi|)
Most of these similarity measures dependent on the
number of transmitters è need normalization
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RF-Based Co-Location Sensing
3. Rank-based measures
•
When signal characteristics consist of signal strengths
(WiFi, Bluetooth), stronger implies closer
•
Hence, some transmitters more important than others
‒
•
Rank correlation (Spearman or Kendall):
‒
•
Measures level of agreement between rx and ry
Sum of square of ranks (Euclidean distance of ranks):
‒
•
We use rx and ry denote the ranking of transmitters for
devices x and y
SSR(x,y) = ∑ (rxi- ryi)2
Generally the more features can be meaningfully
compared, the better the resolution accuracy
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Challenges in RF-Based Sensing:
Device and Environment Variations
• Sensitivities of RF receivers on mobile devices vary
• Signal characteristics have different strengths across devices
• Obstacles and relative positions of devices can cause some
(weaker) measurements to be shielded
• Range of measurements may vary across devices
• Alternative signal representations can be used to mitigate
device-specific variations
• Response rate: replaces sxi with ratios rrxi which measure the
fraction of measurements where i can be observed
‒ Defined over a measurement window w, e.g., w = 10 seconds
• Hyperbolic: relative differences between transmitters typically
more invariant across devices:
‒ Compares measurements of the form hij= log sxi - log sxj
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Challenges in RF-Based Sensing:
Similarity Scaling
• Many of the listed similarity measures are sensitive to the
number of transmitters that can be observed
• However, relative distances not stable over time:
• Depending on environment, normalized or raw similarities
work better
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Ambient Fingerprinting
• Ambient fingerprinting estimates co-location by comparing
properties of current device environment
• Anything can be compared (if you are brave enough):
‒ GammaSense: coarse-grained localization
(house/neighbourhood level) using background radioactivity
• Audio, magnetometer, temperature, humidity, ...
• Characteristics that are being compared should be
1. Spatially distinctive
2. Invariant across devices
3. Sufficiently stable over time
• “Best-effort” fingerprinting: combine whatever information
is available at the given time
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Audio Ambient Fingerprinting
• Audio the most popular ambient characteristics for
ambient fingerprinting
• Recall (Lecture VII) that difference between detecting
whether audio environment same or similar
‒ Music fingerprinting and related techniques can be used for
detecting same kind of environment
• Time-domain audio similarity
• Maximum cross-correlation between two signals
• Voiced / unvoiced segmentation
‒ Using log-energy and/or spectral entropy
‒ Frame labelled as 0 if unvoiced, 1 otherwise
‒ Similarity can be determined by comparing the 0/1 vectors of
devices (e.g., Sound of Silence)
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Audio Ambient Fingerprinting
• Frequency-domain similarity
• Difference in FFT coefficients of selected
frequency bands and variations:
‒ Frame difference: mapping difference in
coefficients across two frames into a binary code
‒ “Hyperbolic” difference: comparing the ratio
between pairs of frequency bands
0
0
1
0
1
0
1
0
1
1
1
0
1
0
1
1
1
0
0
1
1
0
0
1
Similarity = 7
1
0
1
1
1
0
1
1
• Speech similarity
• Compare voiced/unvoiced status of frames and
the mutual information of frames
‒ Effective at detecting co-located conversations
when devices not obstructed
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Accuracy of Co-Location
Detection
• Proximity sensing
• Depends on application level requirements
• Cannot separate proxemics levels, and with most
technologies also proximity “too far”
• But works well in certain applications, e.g., in
authentication distances typically close or very far
• Ambient fingerprinting
• Highly dependent on the nature of activity and signal
characteristics
• E.g., accurate during speech activity without significant
background noise as well as in situations where clear
light/magnetic/temperature pattern
• But also easy to break
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Accuracy: Sensor Fusion
• Performance of co-location
detection can be improved
through sensor fusion
• Particularly important for ambient
fingerprinting
• But also can be used to improve
resolution of proximity sensing
• Also improves resilience of colocation sensing
• Typically fusion done through
boosting-based classifier design
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F-Formations and Pose Sensing
• F-Formation
• Refers to the spatial and orientational divisioning of
space between interacting people
• Two participants: face-to-face, L form, or side-by-side
• Three participants: typically circular formation
• User pose sensing can thus be used to support colocation sensing
• Recall that pose sensing consists of two tasks:
estimating device attitude and aligning device attitude
with use orientation
• Can be accomplished using a combination of
accelerometers, gyroscopes and magnetometers
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Prosody
• Prosody defined as:
• the stress and intonation patterns of utterances (dictionary.com)
• the rhythmic and intonational aspect of language
(merriam-webster)
• Accordingly, characterizes the way people speak
• Important in characterizing several social and psychological
factors, e.g., extroversion, mood, domination, etc.
• Together with speech recognition, forms the dominant
application area for audio in mobile sensing
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Pitch and Fundamental
Frequency
• Pitch
• Perceived tone frequency of a sound
• Fundamental frequency F0
• Inverse of the signal period of a periodic signal
• Most of the energy in a spectrum concentrated around
integer multiples of fundamental frequency
• Sinusoids above F0 referred to as harmonics
• In speech, the frequency at which vocal chords resonate
• Pitch not the same as fundamental frequency
• But closely related for most signals
• Pitch can be located at a higher harmonic than F0 or
combination of non-periodic signals can result in pitch
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Prosodic Sensing
• Refers to the extraction of prosodic features from
speech signals
• Common types of prosodic features
• Fundamental frequency, variations in pitch (so-called
spectral envelope), and spectral harmonics
• Intensity and rate of speech
• Mel frequency cepstral coefficients (MFCC)
• Most features can be estimated “on the side” during
pitch/F0 estimation
• Accuracy of the actual values typically less important
than the capability of capturing variations in the features
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Prosodic Sensing Pipeline
• Preprocessing
• Noise removal, framing, windowing
• “Activity” detection: significant audio level detection
and/or voiced/unvoiced detection
• Fundamental frequency estimation
• Autocorrelation or cepstral analysis together with
temporal tracking
• Feature extraction
• Power of voiced segments, ratio of voiced segments
compared to all (speech) segments, variation in F0 etc.
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Prosodic Sensing Pipeline
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Interactions and Application
Usage as Sensor
• Frequency and nature of interactions can also be used
characterize behaviors
• Distribution of application launch events
• Distribution of the duration of application usage events
• Extent of application usage, e.g., number of distinct application
categories considered
• Similarly frequency and extent of “checking” behaviors can be
considered
• How often screen turned on without unlocking/using device?
• VERY weak cue
• Frequency of application usage can relate to mood, stress,
personality, and many other phenomena
• Should be combined with other cues to increase discrimination
resolution of behavior
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Example: MoodScope
● Mood can be used as an input for recommendation and
context aware systems
● Mood inferred from communication and application usage
● SMS, email, location, web browsing, application usage, and
extent of phone calls made
● Initial accuracy of 66% but can be extended to 93% after
personalized training for two months
● Data gathered from 32 users for two months.
● Three models:
● general model for everyone
● personalized trained data (40% in 10 days)
● Hybrid, user can provide correct ground truth (72% in 10 days)
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Example: StressSense
•
The individual stress level is used as an input in health
applications to reduce anxiety
•
Uses the microphone as the only sensor for detecting individuals
stress level
•
Prosodic features: pitch range and mean, pitch jitter, spectral centroid,
high frequency ratio, MFCC, speaking range, and Teager Energy
Operator critical band autocorrelation envelope
•
Provides 81% accuracy for indoor environments and 76%
accuracy for outdoor environments
•
Experimented on 14 participants in three different tasks. Doing
and interview as an interviewee, conducting a marketing task, and
a natural stress free environment
•
GSR sensors which can measure stress from increased skin
conductance used for gathering ground truth
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Summary
• Social and psychological sensing refers to detection of
psychological and social phenomena from sensors
• Can be extracted even from “thin slices” of observations
• Requires detecting phenomena through the sensing of
multiple “weak cues”
• Co-location sensing essential enabler for social sensing
and many application domains
• Proximity sensing: detecting when devices are within
“sufficiently” close range
• Proxemics sensing: characterizing nature of closeness
according to distance
• Proximity sensing, RF similarity, ambient fingerprinting
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Summary
• Prosodic sensing another important class of features
for social and psychological sensing
• Characterizes how person speaks
• Most features relate to variations in pitch and spectral
characteristics during spoken segments
• Will be covered in detail during Lecture IX
• Other types of sensor information
• Pose and relative orientation of people, e.g., Fformations and posture
• Characteristics and extent of application usage
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References
•
Hall, E. T., Proxemics, Current Anthropology, 1968, 9, 83-108 erence (PerCom), 2012
•
Kendon, A., Spatial organization in social encounters: The F-formation system, Conducting
interaction: Patterns of behavior in focused encounters, Cambridge University Press Cambridge,
UK, 1990, 209-238
•
Wyatt, D.; Choudhury, T.; Bilmes, J. & Kitts, J. A., Inferring Colocation and Conversation
Networks from Privacy-Sensitive Audio with Implications for Computational Social
Science, ACM Transactions on Intelligent Systems and Technology (TIST), 2011, 2, 7:1-7:41
•
Eagle, N. & Pentland, A. S., Inferring friendship network structure by using mobile phone
data, Proceedings of the National Academy of Sciences (PNAS), National Academy of Sciences,
2009, 15274-15278
•
Tucker, S.; Bergman, O.; Ramamoorthy, A. & Whittaker, S., Catchup: A Useful Application of
Time-travel in Meetings, Proceedings of the 2010 ACM Conference on Computer Supported
Cooperative Work (CSCW), 2010
•
Corner, M. D. & Noble, B. D., Zero-interaction authentication, Proceedings of the 8th annual
international conference on Mobile computing and networking (MobiCom), 2002, 1-11
•
LiKamWa, R.; Liu, Y.; Lane, N. D. & Zhong, L., MoodScope: Building a Mood Sensor from
Smartphone Usage Patterns, Proceeding of the 11th Annual International Conference on Mobile
Systems, Applications, and Services (MobiSys), 2013
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References
•
Krumm, J. & Hinckley, K., The NearMe Wireless Proximity Server, Proceedings of the 6th
International Conference on Ubiquitous Computing (UbiComp), Springer, 2004
•
Varshavsky, A.; Scannell, A.; LaMarca, A. & de Lara, E., Amigo: Proximity-Based
Authentication of Mobile Devices, Proceedings of the 9th International Conference on
•
Mathur, S.; Miller, R. D.; Varshavsky, A.; Trappe, W. & Mandayam, N. B., ProxiMate: proximitybased secure pairing using ambient wireless signals,
Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services
(MobiSys), ACM, 2011, 211-224 Ubiquitous Computing (UbiComp), Springer, 2007, 253-270
•
Bucur, D. & Kjærgaard, M. B., GammaSense: Infrastructureless Positioning Using
Background Radioactivity, EuroSSC, 2008, 69-82
•
Kim, D. H.; Kim, Y.; Estrin, D. & Srivastava, M. B., SensLoc: sensing everyday places and
paths using less energy, Proceedings of the 8th ACM Conference on Embedded Networked
Sensor Systems (SenSys), ACM, 2010, 43-56
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