# Averaging interval

Aim

To smooth the values of a dependent variable.

This topic applies to

Dependent variables: Acceleration state, Body angle, Activity state, Body angle state, Body elongation, Body elongation state, External data (resampled), External data - state, Rotation, Mobility state, Movement, Velocity.

The dependent variable can be in a condition defined in the Trial Control Settings, in the Analysis profile and in the Data profile. Setting the Averaging interval in one part of EthoVision XT does not influence the value of the same variable in the others.

How to access this option

In the Trial Control Settings, in the Analysis profile or in the Data profile select the dependent variable and locate Outlier filter.

How Averaging interval works

▪When Averaging interval is 1, the outlier filter is off, thus the values of the variable are not smoothed.

▪When Averaging interval is 2 or larger, EthoVision XT replaces the per-sample value of the dependent variable with the average calculated over the number of samples specified by the interval.

The table below shows how EthoVision XT re-calculates a variable V in a few samples when Averaging interval is set to 2. Note how the average (avg) is obtained when values of the dependent variable are missing. Remember that velocity V at time t is only calculated when there are valid samples at time t and t-1. See Velocity

Sample | Valid (•) or missing (-) | Original value | Smoothed value when averaging interval = 2
1 | • | V1 | V1
2 | • | V2 | avg (V1, V2)
3 | • | V3 | avg (V2, V3)
4 | - | not calculated | avg (V3, [no value]) = V3
5 | - | not calculated | avg ([no value], [no value]) = [no value]
6 | • | not calculated | avg ([no value], [no value]) = [no value]
7 | • | V7 | avg ([no value], V7) = V7

Notes

▪Note the difference between Track Smoothing and the Outlier filter (this topic):

▪With Track Smoothing, you smooth the raw x,y coordinates. This has also an effect on the dependent variables calculated based on those coordinates, for example Distance moved. See Smooth the Tracks

▪With Outlier filter, you smooth the values of the dependent variable, for example velocity or mobility, after they are calculated from the raw data. The Outlier filter is useful when you want to calculate state variables. Apply the Outlier filter, for example, when you want to smooth Velocity to calculate the Movement states, which are based on velocity; or smooth Mobility when you want to calculate Mobility state.

▪If you combine Track Smoothing with Outlier filter, the dependent variable is calculated with the Outlier filter after the raw coordinates are smoothed with Track Smoothing.