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Sensor Data Wrangling: From Metal to Cloud

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Presenter: Sameera Poduri, Jawbone

Sensors are getting cheaper and are now deployed all around us, and the data streams from them can reveal patterns in behavior and a rich understanding of context to enable truly intelligent services. But where and how should we process this data -- close to the sensor hardware to minimize latency and communication costs while maintaining user privacy, or in the cloud, where combining data from multiple devices over long time periods can lead to richer context and deeper insights? In this talk, I will draw three examples from my experience working on robotics, mobile devices, and wearables to contrast the design of sensor processing algorithms at different points in this spectrum between device and cloud.

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Sensor Data Wrangling: From Metal to Cloud

  1. 1. SENSOR DATA WRANGLING: FROM METAL TO CLOUD SAMEERA PODURI @sameerapoduri
  2. 2. SENSOR SURROUND
  3. 3. SENSE MAKING
  4. 4. Context awareness + personalization for better health, well being, productivity, & safety CONTEXT AWARENESS PERSONALIZATION
  5. 5. DISTRIBUTED INTELLIGENCE
  6. 6. Sept 3, 2012 4:00 pm 6:30 am 8 Unread $36 26.3 miles 102° 70° 362 Calories
  7. 7. LAYERED INTELLIGENCE
  8. 8. THE JAWBONE UP
  9. 9. BEAUTY + ENGINEERING IN SERVICE OF A BETTER LIFE
  10. 10. Bio-impedence Accelerometer Temperature Motor Battery Processor Bluetooth Steps Heart rate Sleep Skin temperature Ambient temperature Respiration Galvanic Skin Response
  11. 11. Event Logs Data Products Sensor Data Data Archive Data Science Warehouse Production Platform Redshift Dynamo
  12. 12. DATA PRODUCTS
  13. 13. compressed signals historic + population scale context weather, census, etc limited context compressed signals new raw signals rich signal rich context single user single user DATA raw timeseries limited context all users
  14. 14. latency: ALGORITHMS seconds hours minutes minutes network delays compute storage: low medium high deployment: daysmonths
  15. 15. compressed sensor data compressed sensor + app data insights configuration, user-specific priors
  16. 16. HEART RATE DATA
  17. 17. RAW DATA “Your heart rate is 95 bpm.”
  18. 18. CONTEXT MATTERS
  19. 19. heart rate estimation + user context + insight “Your heart rate is 95 bpm.” “Your heart rate is 95 bpm and you have been sitting in a meeting for an hour.” “Your heart rate is unusually high today at 95 bpm. Consider skipping the coffee before your meeting.”
  20. 20. This morning’s resting heart rate was higher than 61bpm, your 30-day average. Dehydration may be the cause. If you think you were dehydrated last night, make up for it today with 8 glasses of water. Start with Hydration
  21. 21. SENSOR DATA SCIENCE WHAT’S NEXT?
  22. 22. VOLUME & VARIETY OF SENSOR DATA IS GROWING NEED BETTER DATA TOOLS AND ARCHITECTURES SENSOR FUSION IS KEY AN EXCITING FUTURE OF TRULY SMART DEVICES

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