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The ins and outs of your day

The ins and outs of your day. Preeti Bhargava, Nick Gramsky CMSC 838F – Tangible Interactive Computing Final Project Presentation 15 th December 2012. Motivation.

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The ins and outs of your day

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  1. The ins and outs of your day Preeti Bhargava, Nick Gramsky CMSC 838F – Tangible Interactive Computing Final Project Presentation 15th December 2012

  2. Motivation • This project aims to encourage people with sedentary lifestyles, and who spend a majority of time indoors, to spend time outdoors for exercise or leisure or work. • It also attempts to enable people to identify patterns in daily routine and determine ways to change them • It extends the vision of the “Quantified Self” - gathering and analyzing data about everyday activities that can help people improve their lives—an approach known as “self-tracking” [4]

  3. Related work • Several projects such as Fish’n’Steps[1], UbiFit[2] and BeWell[3] have addressed the research problem of identifying the amount of physical activity or socializing done by people and using persuasive technology for improving the health of people • None have looked at identifying how much time people spend indoors or outdoors and quantifying their actual locations for the whole day

  4. Initial vision • Use ubiquitous sensors such as Wi-Fi, GPS, barometer/thermometer, and accelerometer to localize and quantify a person’s whole day – where he has been in terms of actual locations, identify whether he was indoors or outdoors, how much physical activity he undertook in terms of movement and so on • “Your noise is my signal” – Gregory Abowd at UbiComp 2012 • Visualize this data to enable people to find patterns in their daily, weekly and monthly routines

  5. But …….. There were Challenges • Memory • Arduino has 2K RAM!!!! • Too many strings cause RAM to be used up fast and the Arduino sketch to crash and reboot • Limits the use of libraries such as TinyGPS, WiFi • Wifi Shield • Wifi library causes memory leak • Truncates long strings being sent over HTTP • Not robust enough for complex tasks such as connecting and disconnecting

  6. How the current system works • Had to abandon WiFi • The system follows a Fitbit model where it collects data throughout the day and offloads it • We use a push button • Sensors collect data and store on SD cards • Accelerometer dictates sampling rates • Sensors collect every 15-minutes of no motion • If motion is detected, sample every 2 minutes of motion • Leverage the server side to do end of day analysis using many API’s/databases/decision trees.

  7. Hardware • Arduino • Wi-Fi Shield • GPS • Barometer + Thermometer • Accelerometer

  8. software • Arduino • Locus Location Server • Abandoned • Weather Underground API • Google Maps API • Web service to determine if a lat/long is inside a building

  9. Server side Indoor outdoor Algorithm Data: Unique id + Set of GPS readings and temperature Result: client's approximate location and indoor/outdoor position Identify client using unique identifier; whilenot at end of data do parse GPS location; if (fixQuality!=0) reverse geocode latitude and longitude; else use last known latitude/longitude for reverse geocoding; classify indoor/outdoor positioning; write location and in/out classification to a file; end

  10. Server side Indoor outdoor Algorithm (contd.) InOut classification If ( #sat > 5 & locationError <= 1.0)) then position = outdoor; else if (#sat<=5 && 1.0 < locationError <= 2.0) then position = indoor else if (locationError > 2.0) then position = indoor else compare with outside temperature if outside temperature not in [15,27] else use web service to determine if lat/long is inside a building or not End

  11. Results ll=$GPGGA,074145.726,,,,,0,3,,,M,,M,,*&t=5 ll=$GPGGA,074158.019,,,,,0,5,,,M,,M,,*&t=5 ll=$GPGGA,074210.254,,,,,0,6,,,M,,M,,*&t=5 ll=$GPGGA,074222.600,3854.1064,N,07715.5004,W,1,6,3.59,141.3,M,-33.4,M,,*&t=5 ll=$GPGGA,074235.000,3854.1153,N,07715.5242,W,1,6,3.59,126.6,M,-33.4,M,,*&t=5 ll=$GPGGA,074247.400,3854.1120,N,07715.5307,W,1,6,1.27,89.0,M,-33.4,M,,*&t=5 ll=$GPGGA,074259.800,3854.1100,N,07715.5096,W,1,8,1.21,134.9,M,-33.4,M,,*&t=5 ll=$GPGGA,074312.200,3854.0989,N,07715.4841,W,1,6,1.31,133.4,M,-33.4,M,,*&t=5 Raw Data 15:37:33 Preeti <Washington and Old Dominion Trail, Vienna, VA 22180, USA> outdoor 15:39:34 Preeti <Washington and Old Dominion Trail, Vienna, VA 22180, USA> outdoor 15:41:36 Preeti <208 Park Terrace Ct SE, Vienna, VA 22180, USA> outdoor 15:43:37 Preeti <305 Glyndon St SE, Vienna, VA 22180, USA> indoor 15:45:39 Preeti <424 Dominion Dr SE, Vienna, VA 22180, USA> indoor 15:45:39 Preeti <424 Dominion Dr SE, Vienna, VA 22180, USA> indoor 15:59:10 Preeti <424 Dominion Dr SE, Vienna, VA 22180, USA> indoor 16:24:58 Preeti <208 Park Terrace Ct SE, Vienna, VA 22180, USA> indoor 16:26:59 Preeti <522 Spring St SE, Vienna, VA 22180, USA> indoor 16:29:00 Preeti <206 Park Terrace Ct SE, Vienna, VA 22180, USA> indoor 16:31:02 Preeti <206 Park Terrace Ct SE, Vienna, VA 22180, USA> indoor 16:33:03 Preeti <401 Glyndon St SE, Vienna, VA 22180, USA> indoor 16:35:05 Preeti <229 Locust St SE, Vienna, VA 22180, USA> indoor 16:37:06 Preeti <208 Park Terrace Ct SE, Vienna, VA 22180, USA> indoor 16:39:07 Preeti <208 Park Terrace Ct SE, Vienna, VA 22180, USA> indoor Processed Data

  12. Results Visualization: Possible visualization

  13. Future work • The same setup can be implemented on a smartphone which has more sensors • A client agent application on the smartphone can monitor a person’s context, activities, location, and behavior and learn patterns • The client agent application can predict user intent and behavior

  14. References • Lin, J.J., Mamykina, L., Lindtner, S., Delajoux, G., & Strub, H.B., “Fish‘n’Steps: Encouraging Physical Activity with an Interactive Computing Game,” Proceedings of UbiComp ’06, (Sep 2006), pp.261-78. • S. Consolvo, P. Klasnja, D.W. McDonald, D. Avrahami, J. Froehlich, L. LeGrand, R. Libby, K. Mosher, & J.A. Landay. “Flowers or a Robot Army? Encouraging Awareness & Activity with Personal, Mobile Displays,” Proceedings of the 10th International Conference on Ubiquitous Computing: UbiComp ’08, Seoul, Korea, (2008), pp. 54-63. • Nicholas D. Lane, Tanzeem Choudhury, Andrew Campbell, MashfiquiMohammod, Mu Lin, Xiaochao Yang, AfsanehDoryab, Hong Lu, Shahid Ali and Ethan Berke, BeWell: A Smartphone Application to Monitor, Model and Promote Wellbeing, (Pervasive Health 2011), 5th International ICST Conference on Pervasive Computing Technologies for Healthcare, Dublin, 23-26 May 2011 • http://www.economist.com/node/21548493

  15. Thank you Quantified Self Borrowed from [4]

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