Smooth Movement Data

Applies smoothing filters to movement tracking data to reduce noise.

Usage

filter_movement(
  data,
  method = c("rollmedian", "rollmean", "kalman", "sgolay", "lowpass", "highpass",
    "lowpass_fft", "highpass_fft"),
  use_derivatives = FALSE,
  ...
)

Arguments

data

A data frame containing movement tracking data with the following required columns:

• individual: Identifier for each tracked subject

• keypoint: Identifier for each tracked point

• x: x-coordinates

• y: y-coordinates

• time: Time values Optional columns:

• z: z-coordinates

method

Character string specifying the smoothing method. Options:

• "kalman": Kalman filter (see filter_kalman())

• "sgolay": Savitzky-Golay filter (see filter_sgolay())

• "lowpass": Low-pass filter (see filter_lowpass())

• "highpass": High-pass filter (see filter_highpass())

• "lowpass_fft": FFT-based low-pass filter (see filter_lowpass_fft())

• "highpass_fft": FFT-based high-pass filter (see filter_highpass_fft())

• "rollmean": Rolling mean filter (see filter_rollmean())

• "rollmedian": Rolling median filter (see filter_rollmedian())

use_derivatives

Filter on the derivative values instead of coordinates (important for e.g. trackball or accelerometer data)

...

Additional arguments passed to the specific filter function

Value

A data frame with the same structure as the input, but with smoothed coordinates.

Details

This function is a wrapper that applies various filtering methods to x and y (and z if present) coordinates. Each filtering method has its own specific parameters - see the documentation of individual filter functions for details:

• filter_kalman(): Kalman filter parameters

• filter_sgolay(): Savitzky-Golay filter parameters

• filter_lowpass(): Low-pass filter parameters

• filter_highpass(): High-pass filter parameters

• filter_lowpass_fft(): FFT-based low-pass filter parameters

• filter_highpass_fft(): FFT-based high-pass filter parameters

• filter_rollmean(): Rolling mean parameters (window_width, min_obs)

• filter_rollmedian(): Rolling median parameters (window_width, min_obs)

See also

• filter_kalman()

• filter_sgolay()

• filter_lowpass()

• filter_highpass()

• filter_lowpass_fft()

• filter_highpass_fft()

• filter_rollmean()

• filter_rollmedian()

Examples

if (FALSE) { # \dontrun{
# Apply rolling median with window of 5
filter_movement(tracking_data, "rollmedian", window_width = 5, min_obs = 1)
} # }