MIMSunit (version 0.11.2)

extrapolate: Extrapolate input multi-channel accelerometer data

Description

extrapolate applies the extrapolation algorithm to a multi-channel accelerometer data, trying to reconstruct the true movement from the maxed-out samples.

Usage

extrapolate(df, ...)
extrapolate_single_col(
  t,
  value,
  range,
  noise_level = 0.03,
  k = 0.05,
  spar = 0.6
)

Arguments

df

dataframe. Input multi-channel accelerometer data. Used in extrapolate. The first column should be the date/time

...

see following parameter list.

t

POSIXct or numeric vector. Input index or timestamp sequence Used in extrapolate_single_col.

value

numeric vector. Value vector used in extrapolate_single_col.

range

numeric vector. The dynamic ranges of the input signal. Should be a 2-element numeric vector. c(low, high), where low is the negative max value the device can reach and high is the positive max value the device can reach.

noise_level

number. The tolerable noise level in \(g\) unit, should be between 0 and 1. Default is 0.03, which applies to most devices.

k

number. Duration of neighborhood to be used in local spline regression for each side, in seconds. Default is 0.05, as optimized by MIMS-unit algorithm.

spar

number. Between 0 and 1, to control how smooth we want to fit local spline regression, 0 is linear and 1 matches all local points. Default is 0.6, as optimized by MIMS-unit algorithm.

Value

extraplate returns a dataframe with extrapolated multi-channel signal. extrapolate_single_col returns a dataframe with extrapolated single-channel signal, the timestamp col is in numeric values instead of POSIXct format.

How is it used in MIMS-unit algorithm?

This function is the first step during MIMS-unit algorithm, applied before filtering.

Details

This function first linearly interpolates the input signal to 100Hz, and then applies the extrapolation algorithm (see the manuscript) to recover the maxed-out samples. Maxed-out samples are samples that are cut off because the intensity of the underlying movement exceeds the dynamic range of the device.

extrapolate processes a dataframe of a multi-channel accelerometer signal. extrapolate_single_col processes a single-channel signal with its timestamps and values specified in the first and second arguments.

See Also

Other extrapolation related functions: extrapolate_rate()

Examples

# NOT RUN {
  # Use the maxed-out data for the conceptual diagram
  df = conceptual_diagram_data[
          conceptual_diagram_data['GRANGE'] == 4,
          c("HEADER_TIME_STAMP", "X")]

  # Plot input
  illustrate_signal(df, range=c(-4, 4))

  # Use the default parameter settings as in MIMunit algorithms
  # The dynamic range of the input data is -4g to 4g.
  output = extrapolate(df, range=c(-4, 4))

  # Plot output
  illustrate_signal(output, range=c(-4, 4))
# }