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With our use of accelerometers, we are able to measure activity types and measure arm angles and upper body inclination.
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Therefore, mutual information can be used to measure the information concerning the activity types implied by the feature set.
The entropy (H(C_{activity})) is used to measure the uncertainty about the activity types, while (H(C_{activity}|F)) evaluates the uncertainty about the activity types if the set of features is known.
From Eq. (1), it can be inferred that mutual information (I(F,C_{activity})) actually measures the reduced uncertainty of the activity types if the feature set F has been provided.
Accelerometers, or actigraphs, measure activity patterns across multiple days.
This paper focuses on how visitor use distribution (measured through GPS tracking), activity type, and trail design influence the impacts to trail conditions.
For those who aren't familiar with the device, it's designed to measure a wearer's daily activity and produce a score, called NikeFuel, that represents how much they've accomplished throughout the day, regardless of activity type.
Let (C_{activity}) denote the activity type.
measured activity.
To measure ATPase activity, wild-type or mutant Hsp104 proteins (0.2 μM monomer) were incubated at 25°C in 40 mM HEPES-KOH (pH 7.4), 150 mM KCl, 20 mM MgCl2, 10% glycerol, 1 mM DTT, and 1 mM ATP.
In general, though, they're moving beyond the simple page view model to measure different types of activities.
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