Bo Adler - Measurement Error


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At a "Quantified Self and Science" meetup, Bo Adler talks about the issue of measurement error.

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  • Notes from Eri Gentry watching Bo's presentation at QS:
    Started self tracking because of sleep apnea. Devices can screw up, eg runkeeper drawing path in a straight line. Withings scale: bounces a lot from day to day (+/- 5lbs) but if you subtract morning readings from night readings, a lot of the variability drops out. Device errors due to how it reads data. Zeo: only access to sleep stage, not what is literally being measured.
    Fujitsu: wore many devices, learned a lot about avoiding measurement errors
    What to do?
    - improve the sensor or the conditions. Eg blood pressure should be taken when person is still or will “try harder” when good reading not taken. Ouch for the arm unless you give in
    - discard data that you “know” is wrong. Look at values beforehand to compare. Important to actually read data before throwing it out
    - take “average” over multiple readings. Eg withings scale’s moving average. Be careful about how you’re taking the average
    - add more information

    Google and Wikipedia do the same thing, in a different domain. The world is a messy place. You have to embrace that it’s noisy and improve your model by gathering more data.
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  • Some causes of measurement error:device malfunctionfaulty assumptionsnot measuring what you think you’re measuring
  • improve conditions: while measuring blood pressure, I learned to stand still, otherwise machine would keep trying.discard data: the GPS picture on the previous page is a classic example. Notice that the error is at the very beginning, so using the past as a guide doesn’t always work.averaging: a moving average is a great way to filter out “noise”, but we’re usually making the hidden assumption that our noise has a normal distribution. Hardest problem was pulse oximeter timings, which showed two distinct intervals (mixed with noise!).more info: pulse oximeter very sensitive to movement, so we added an accelerometer. Another instance is the Withings scale, which asks for age and sex to use better model.Taking a step back, these are all variations of “improve your model”. (Using raw data from a sensor has a model that the sensor provides accurate data, so improving the sensor is improving the model.)The world is a messy place, so can we learn to embrace it? At a very small scale, Google or Wikipedia can be wildly inaccurate – but they both apply these same ideas in very different domains.
  • Bo Adler - Measurement Error

    1. 1. Measurement Error
    2. 2. What to do?Improve the sensor or the conditions.Discard data that you “know” is wrong.Take “average” over multiple readings.Add more information.