Use of Mobile Phones for Exposure Assessment

Department of Epidemiology and Public Health
Use of Mobile Phones for Exposure
Assessment
Martin Röösli, PhD
Swiss TPH Spring Symposium
8 May 2012
Many thanks to Oliver Lauer (ETHZ), Nicolas Maire and Harish Phuleria, who
contributed slides to this talk.
Content
 What is a mobile phone?
 HERMES: Use of mobile phones for radiofrequency electromagnetic field
measurements
 Sensorscope/Climaps project
 NoiseTube: collecting noise data with mobile phones
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What does your smart phone?
from Lane et al.,
IEEE Com Mag, 2010
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And what may your mobile phone do in the future
 barometer, temperature, humidity sensors (Intel/University of Washington
Mobile Sensing Platform)
 air quality and pollution (Honicky et al., 2008)
 blood pressure monitoring using the earphone (Poh et al., 2009)
 commercially interesting: combine low-level censor data with context and
activity data. E.g. Sense Networks a U.S. start-up company which uses
millions of GPS estimates from mobile phones to predict which
subpopulation is interested in a specific type of event (Lane et al., 2010).
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Mobile phone sensing on different scales
from Lane
et al., IEEE
Com Mag, 2010
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Other conceptual issues
 passive vs active recording
 smart phone vs. smart sensors
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Benefits of using mobile phones for exposure assessment
 Two types of information is obtained: exposure and behaviour.
 Many data can be collected
 Continuous measurements -> directly sent to the server
 Apps can be developed to facilitate data collection (e.g. reminder,
questionnaires)
 Appstores and similar can be used for apps distribution
 E.g. collect noise data, apply learning algorithms to identity classes of
behaviour:
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HERMES: A Swiss TPH project
 Health Effects Related to Mobile phone usE in adolescentS
 Is radiofrequency electromagnetic fields (RF-EMF) from using mobile phones
related to behaviour, symptoms and cognitive function
 Funding: SNF
 Cohort study in Central Switzerland:
1.
baseline investigation in 2012 in 8th grade students
2.
follow-up investigation in 2013 in the same students (9th grade)
 Use of mobile phone operator recorded data
 Modelling of environmental RF-EMF
 Collecting personal RF-EMF from 100 study participants
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Measurement Concept: combine exposimeter with smart phone
 Exposimeter
1. RF-EMF measurements
Exposimeter
Smartphone
 Smartphone
1. Position
GPS
Bluetooth
Activity diary
health
2. (Use of mobile phone)
GPS
Diary
In
te
rn
et
Server
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Calibration
1. 2JMAS01 antenna
2. 824 MHz- 5.9 GHz
Frequency Band
Min. Field [V/m]
Max. Field [V/m]
GSM900TX
0.0008
1.4778
GSM900RX
0.0031
3.9388
GSM1800TX
0.0073
6.6912
GSM1800RX
0.0243
>12.5601
DECT
0.0037
>12.4465
UMTSTX
0.0053
6.7472
UMTSRX
0.0052
9.1096
ISM2.4
0.0117
>10.0520
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Smartphone
 Requirements
1. Bluetooth Interface
2. GPS module
3. Open source OS
 Concept
1. Data Logging / Display
Data storage
Tracking GPS location
Tracking transmission power (NF)
Data distribution using 2G/3G backbone network
2. Active diary/questionnaire
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Test case-study
 Measurement location
1. Zürich
 Measurement settings
1. sampling period: 1.6 s
2. measurement duration: 15 min
3. linear antenna polarization
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Measurement Results
GSM 1800rx
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Sensorscope/Climaps project:
Real-time environmental monitoring
• Partnership EPFL (lead) &
SwissTPH (Harish
Phuleria)
• Real-time monitoring of
various air pollutants and
weather parameters in
Lausanne
•http://www.climaps.com/
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Sensorscope/Climaps project:
Sensor evaluation
• Precision high in most sensors
• Both sensitivity as well as specificity
are still problems for some sensors,
and accuracy still not enough to be
used for community AQ monitoring
• Most AQ sensors are still under
development and not available
commercially
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NoiseTube: citizen scientists for distributed sensing
 Participatory noise pollution monitoring using mobile phones. Maisonneuve
et al., Information Polity, 15(1-2):51-71, Aug 2010.
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Noisetube architecture
NoiseTube
App (Android)
NoiseTube
server
Upload data
Store locally
Android
file system
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Noisetube limitations
 Experimental state after re-implementation for Android
 Calibration issues
Basel Summer (filtered SLM data)
75
Smartphone, LAeq (dBA)
70
65
y = 0.71x + 5.1
60
2
R = 0.954 (w/o outlier)
55
y = 0.64x + 9.6
2
R = 0.867 (all data)
50
45
40
35
35
40
45
50
55
60
65
70
75
Sound Level Meter, LAeq (dBA)
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Challenges with mobile phone exposure data
 Technical issues
 Selection of participants: random sample difficult to achieve
If interested in a specific population: not everybody will have a suitable smart
phone
 Operating system of Iphone is not open
 Data quality
 Amount of data
 Post processing
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http://villevivante.ch/
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