Dave Marvit - Normalizing Data

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At a "Quantified Self and Science" meetup, Dave Marvit talks about the issue of normalizing data.

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  • Notes from Eri Gentry watching Dave's presentation at QS:
    Why normalize? You’ve got a bunch of data you want to make sense of – you need to normalize it to compare it.
    Comparison: me vs time, me vs circumstance, me vs others
    Automate analysis: let the machine deal with the data
    Combine + replace sensors: take your sensor with non-standard output. Encapsulate it. Make it give standard output.
    Important if you want to combine sensors. Many sensors -> encapsulate -> analysis across all sensors to compute. Eg heartrate. there are many types of heartrate sensors. You have to normalize to compare them
    Fujitsu built a platform … e.g. how do soldiers drive upon returning to civilian life? Soldiers tracking environment couldn’t make sense of data because it wasn’t normalized, too many variables. They normalized the data to iphone app that goes to sprout…
    Gary: What can we use? Synchronize to get deeper understanding
    E.g. stress mapped against location. Normalizing over time
    Gary: Quite in depth for us. We don’t know when our devices aren’t calibrated.
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Dave Marvit - Normalizing Data

  1. 1. QS #27 – Discussion slidesNormalizing Data /common sensors Dave Marvit
  2. 2. Why normalize?• Comparison – Me vs. time – Me vs. circumstance – Me vs. others• Automate analysis• Combine + replace sensors Encapsulation Non-standard output Standard output Sensor
  3. 3. Combining sensors Non-standard output Standard outputSensor New standard Non-standard output Standard output outputSensor Non-standard output Standard outputSensor
  4. 4. Applying a platform
  5. 5. Normalization against timeSensors Drive events Copyright 2012 FUJITSU
  6. 6. Stress map: Stress vs. GPS location
  7. 7. Discussion

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