# Movement

Definition

A discrete (state event) variable, based on one of the body points, with two possible states, Moving and Not moving:

▪The state is Moving if the running average velocity of the body point exceeds the user-defined Start velocity.

▪The state remains Moving until the running average velocity drops below the user-defined Stop velocity.

▪The state then becomes Not moving until the running average velocity reaches the Start velocity again.

In order to reduce the sensitivity of this dependent variable to brief changes in velocity, the data can be smoothed by taking the running average of the last k samples. This number is referred to as the averaging interval.

When a body point is missing for more than three samples, the current Movement state ends and the remaining missing samples are ignored.

In the following example, the velocity initially lies between the Stop velocity and the Start velocity. Therefore, the Movement state is undefined. When velocity exceeds the Start value, Movement is given the value Moving. When velocity drops below the Stop value, Movement is given the value Not moving.

How to specify Movement

1.Click the Add button next to Movement.

2.In the Movement tab, enter the following:

▪Averaging interval: The number of samples over which the running average velocity is based. The default value is 1, that is, velocity is not smoothed before calculating the Movement variable.

▪Start velocity: The velocity above which the subject is considered to be moving.

▪Stop velocity: The velocity below which displacements of the subject’s body points are no longer attributed to locomotion but to system noise, body wobble or pivoting on the spot.

note The default values are an example and may not apply to your experiment. The threshold values also vary between species. If the subject is very slow, like a walking tick, you must reduce the two thresholds to detect true movement. See also a note below.

3.Under Calculate statistics for, select either one of them, or both.

▪Moving: To calculate statistics for when the subject is moving.

▪Not moving: To calculate statistics for when the subject is not moving.

4.Complete the procedure to add the variable See Calculate statistics: procedure.

Notes

▪If your experiment is set to Center-point, nose-point and tail-base detection, click the Body points tab and select the body points for which you want to calculate movement.

▪To find the optimal Start velocity, plot the values of Velocity and take note of the values when the animal moves in the video. Do this for a couple of videos. The Start velocity should be just below those values. Similarly, to find the optimal Stop velocity, take note of the values of velocity when the animal sits still. The Stop velocity should be just above those values. Obviously, a more objective evaluation could come from a statistical approach (e.g. to find cutoff values that discriminate between different behaviors, moving vs not moving).

▪By increasing the averaging interval, you can increase the reliability of movement detection. A running average velocity based on more samples diminishes the effect of random changes in velocity due to noise. However, a drawback of increasing the averaging interval is that it causes a delay in the determination of a state transition, proportional to the length of the interval. See Averaging interval

▪Values of velocity between Start velocity and Stop velocity result in no change in the current state of the subject (moving or not moving). The smaller the difference between the two threshold velocities, the more likely that transitions between the states Moving and Not moving are scored. By defining such a buffer, you prevent overestimation of transition rates because of a velocity joggling just around the movement threshold.

Application

Like Velocity, Movement provides information on the subject's locomotor activity.

In rodent models of aging, the total duration of Moving, both in seconds and as percentage of the trial time, are used to calculate the frailty index. See Parks et al. (2012) J. Gerontol. A 67(3): 217-227.