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ExposureSense Demo
 

ExposureSense Demo

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ExposureSense Demo interactive presentation for MOVE COST Final Conference

ExposureSense Demo interactive presentation for MOVE COST Final Conference

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ExposureSense Demo ExposureSense Demo Presentation Transcript

  • EXPOSURESENSE: INTEGRATING DAILY ACTIVITIES WITH AIR QUALITY USING MOBILE PARTICIPATORY SENSING Bratislav Predic*, Zhixian Yan† , Julien Eberle‡ , Dragan Stojanovic*, Karl Aberer‡ * University of Nis, Serbia † Samsung Research, USA ‡ EPFL, Switzerland MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013.
  • Sensors and smartphones MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 2  Modern smartphones accompany users 24/7 and have  Increasing number of integrated sensors (accelerometer, gyroscope, sound/light sensor, camera, compas,...)  Continuously increasing processing and storage capacity  Powerfull sensor platforms  Sensor commonly used in research  Accelerometer : detecting user activity accept/reject call, initiate file transfer, snooze alarm…  Using data mining techniques to infer more complex user physical activities
  • Smartphones and air quality monitoring MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 3  Air quality monitoring – traditional approach  Fixed or mobile sensing nodes  OpenSense project in Switzerland - sensors on top of public transport vehicles  Smartphones and pollution sensing  Integrated audio analysis as noise pollution indicator  Beyond embedded sensors USB pluggable air quality sensors (ozone O3 sensor)
  • Activity/air quality correlation MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 4  User’s activity and air quality measurements  Usually treated as fairly independent  ExposureSense  Correlation of activity and air quality data  Bridges the gap and estimates user’s exposure to air pollution  Combination of air quality sensing modes  PM10 monitoring stations on public transport vehicles  Pluggable O3 sensor for smartphones
  • ExposureSense MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 5  Provides additional knowledge from correlation of data from different sensors  Technical challenges  Develop uniform interface for sensor access  Important for “virtual” sensors capturing phone states  Sensor adapter/wrapper middle layer
  • System architecture MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 6  The abstraction layer was implemented using sensor probe approach and software components of the Funf – open sensing framework  Main components  User activities recognition  Acquisition of air quality from pluggable sensors  Acquisition of air quality from external sensor network  Daily exposure estimation  Mobile front-end interface
  • User activities recognition MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 7  Implemented by extended accelerometer probe  Encapsulates inference engine for activity recognition through:  Sampling  Extracting features  Building classification model  Classifying unknown accelerometer streams  J48 classification decision tree used in experiments
  • User activities recognition MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 8  Accelerometer data features used  Mean value  Standard deviation  Correlation  Acceleration vector intensity mean value  Energy  Entropy  Time and frequency domain
  • User activities recognition engine MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 9  Features calculated per accelerometer axis in time and frequency domain  J48 decision tree classifier || || || 1     i ix Energy   n i ii xpxpEntropy 1 2 )(log)(
  • Acquisition of air quality from pluggable sensors MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 10  Participatory air quality sensing complements external sensor networks  Example: OpenSense deploys rich set of air quality sensors on top of public transport vehicles  Smartphones can act as both consumer and contributor to sensing network  As a client of Global Sensor Network
  • Acquisition of air quality from external sensor network MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 11  Smartphone acts as a contributor to Global Sensor Network (GSN)  Current smartphones lack integrated air quality sensors  USB pluggable sensor platform  Local storage and publish data to GSN  Interpolation with external sensor nodes network data to estimate exposure
  • Daily exposure estimation MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 12  Correlating inferred activity data with air quality data acquired from pluggable and external sensors  Exposure intensity is estimated based on activity type detected and burned calories per acitivity according to MET research
  • Mobile client front-end MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 13  Tabbed view system  Raw sensor data view  Frequency domain accelerometer data view  Activity/air quality timeline data view  Map data view  Android broadcast communication mechanism  Service front-end
  • Mobile client demonstration MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 14  Calories burn MET based calculation  Activity history on timeline and map view CB = (BMR/24) * MET * T BMRmale = (13.75*WKG) + (5*HC) - 6.76*AGE + 66 BMRfemale = (9.56*WKG) + (1.85*HC) - 4.68*AGE +655 CB - calories burnt BMR - basic metabological rate WKG - weight in kg HC - height in cm T - time in h
  • Mobile client demonstration MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 15  Air quality parameter chooser  Timeline view of chosen air quality parameter  Air quality readings map view  Diary-type calendar history overview  Daily activity and estimated exposure
  • Video demonstrations MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 16 ExposureSense Android Client Demo
  • Conclusion and future work MOVE COST Final Conference, Technical University of Vienna, September 30th – October 1st, 2013. 17  Personalized daily diary integrating user activities and air quality  A building block for next generation personalized healthcare applications based on smartphones  Future research directions  Analysis and mining of stored data about user activities, calories burnt and pollution exposure, detecting interesting patterns, providing recommendations  Integrate more sensor inputs and virtual sensors: user interaction, profile, social network activities, etc.