Package 'accelEE' reference manual

Title:	Predict Energy Expenditure from Accelerometer Data
Description:	Simplifies the application of various energy expenditure models. The package is intended as a hub that brings together methods from a variety of other, themed packages such as 'Sojourn' and 'TwoRegression'. Several methods are supported locally as well, including the linear methods of Hildebrand et al. (2014) <doi:10.1249/MSS.0000000000000289> and the non-linear adaptation by Ellingson et al. (2017) <doi:10.1088/1361-6579/aa6d00>. The package can combine output from different methods and produce standardized output in a range of units.
Authors:	Paul R. Hibbing [aut, cre], Children's Mercy Kansas City [cph]
Maintainer:	Paul R. Hibbing <[email protected]>
License:	MIT + file LICENSE
Version:	0.3.1
Built:	2026-05-13 05:15:34 UTC
Source:	https://github.com/cran/accelEE

Predict energy expenditure for accelerometry data

Description

Predict energy expenditure for accelerometry data

Usage

## Wrapper function:

  accelEE(
    d, method = c(
      "Crouter 2006", "Crouter 2010", "Crouter 2012", "Crouter 2015",
      "Hibbing 2018", "Hildebrand Linear", "Hildebrand Non-Linear",
      "Montoye 2017", "SIP", "Sojourn 1x", "Sojourn 3x",
      "Staudenmayer Linear", "Staudenmayer Random Forest"
    ), time_var = "Timestamp", output_epoch = "default",
    warn_high_low = TRUE, met_mlkgmin = 3.5, RER = 0.85,
    feature_calc = TRUE, shrink_output = TRUE, combine = TRUE,
    ee_vars = c("METs", "VO2", "kcal"), verbose = FALSE, ...
  )


## Internal applicator functions called by the wrapper, based on
## the value of the `method` argument (external functions listed
## under 'See Also'):

  wrap_2RM(
    d, time_var = "Timestamp", output_epoch = "default",
    max_mets = 20, warn_high_low = TRUE,
    met_mlkgmin = 3.5, RER = 0.85,
    feature_calc = TRUE, shrink_output = TRUE, verbose = FALSE,
    method = c(
      "Crouter 2006", "Crouter 2010",
      "Crouter 2012", "Hibbing 2018"
    ), ..., met_name = "METs", tag = ""
  )

  crouter15(
    d, time_var = "Timestamp", output_epoch = "default",
    min_mets = 1, max_mets = 20, warn_high_low = TRUE,
    met_mlkgmin = 3.5, RER = 0.85,
    shrink_output = TRUE, verbose = FALSE,
    model = c("VA", "VM"), movement_var = "Axis1",
    ...
  )

  hildebrand_linear(
    d, time_var = "Timestamp", output_epoch = "default",
    min_vo2_mlkgmin = 3, max_vo2_mlkgmin = 70, warn_high_low = TRUE,
    met_mlkgmin = 3.5, RER = 0.85,
    feature_calc = TRUE, shrink_output = TRUE, verbose = FALSE,
    age = c("youth", "adult"), monitor = c("ActiGraph", "GENEActiv"),
    location = c("hip", "wrist"), enmo_name = "ENMO", ...
  )

  hildebrand_nonlinear(
    d, time_var = "Timestamp", output_epoch = "default",
    min_vo2_mlkgmin = 3, max_vo2_mlkgmin = 70, warn_high_low = TRUE,
    met_mlkgmin = 3.5, RER = 0.85,
    feature_calc = TRUE, shrink_output = TRUE,
    verbose = FALSE, enmo_name = "ENMO", ...
  )

  montoye(
    d, time_var = "Timestamp", output_epoch = "default",
    min_mets = 1, max_mets = 20, warn_high_low = TRUE,
    met_mlkgmin = 3.5, RER = 0.85,
    feature_calc = TRUE, shrink_output = TRUE,
    verbose = FALSE, side = c("left", "right"),
    ...
  )

  sojourn(
    d,  time_var = "Timestamp", output_epoch = "default",
    min_mets = 1, max_mets = 20, warn_high_low = TRUE,
    met_mlkgmin = 3.5, RER = 0.85,
    shrink_output = TRUE, verbose = FALSE,
    axis1 = "Axis1", axis2 = "Axis2", axis3 = "Axis3",
    vector.magnitude = "Vector.Magnitude",
    method = c("SIP", "Sojourn 1x", "Sojourn 3x"),
    ..., met_name = "METs", tag = ""
  )

  staudenmayer(
    d, time_var = "Timestamp", output_epoch = "default",
    min_mets = 1, max_mets = 20, warn_high_low = TRUE,
    met_mlkgmin = 3.5, RER = 0.85,
    feature_calc = TRUE, shrink_output = TRUE,
    verbose = FALSE, ..., select = c("METs_lm", "METs_rf")
  )
## Wrapper function:

  accelEE(
    d, method = c(
      "Crouter 2006", "Crouter 2010", "Crouter 2012", "Crouter 2015",
      "Hibbing 2018", "Hildebrand Linear", "Hildebrand Non-Linear",
      "Montoye 2017", "SIP", "Sojourn 1x", "Sojourn 3x",
      "Staudenmayer Linear", "Staudenmayer Random Forest"
    ), time_var = "Timestamp", output_epoch = "default",
    warn_high_low = TRUE, met_mlkgmin = 3.5, RER = 0.85,
    feature_calc = TRUE, shrink_output = TRUE, combine = TRUE,
    ee_vars = c("METs", "VO2", "kcal"), verbose = FALSE, ...
  )


## Internal applicator functions called by the wrapper, based on
## the value of the `method` argument (external functions listed
## under 'See Also'):

  wrap_2RM(
    d, time_var = "Timestamp", output_epoch = "default",
    max_mets = 20, warn_high_low = TRUE,
    met_mlkgmin = 3.5, RER = 0.85,
    feature_calc = TRUE, shrink_output = TRUE, verbose = FALSE,
    method = c(
      "Crouter 2006", "Crouter 2010",
      "Crouter 2012", "Hibbing 2018"
    ), ..., met_name = "METs", tag = ""
  )

  crouter15(
    d, time_var = "Timestamp", output_epoch = "default",
    min_mets = 1, max_mets = 20, warn_high_low = TRUE,
    met_mlkgmin = 3.5, RER = 0.85,
    shrink_output = TRUE, verbose = FALSE,
    model = c("VA", "VM"), movement_var = "Axis1",
    ...
  )

  hildebrand_linear(
    d, time_var = "Timestamp", output_epoch = "default",
    min_vo2_mlkgmin = 3, max_vo2_mlkgmin = 70, warn_high_low = TRUE,
    met_mlkgmin = 3.5, RER = 0.85,
    feature_calc = TRUE, shrink_output = TRUE, verbose = FALSE,
    age = c("youth", "adult"), monitor = c("ActiGraph", "GENEActiv"),
    location = c("hip", "wrist"), enmo_name = "ENMO", ...
  )

  hildebrand_nonlinear(
    d, time_var = "Timestamp", output_epoch = "default",
    min_vo2_mlkgmin = 3, max_vo2_mlkgmin = 70, warn_high_low = TRUE,
    met_mlkgmin = 3.5, RER = 0.85,
    feature_calc = TRUE, shrink_output = TRUE,
    verbose = FALSE, enmo_name = "ENMO", ...
  )

  montoye(
    d, time_var = "Timestamp", output_epoch = "default",
    min_mets = 1, max_mets = 20, warn_high_low = TRUE,
    met_mlkgmin = 3.5, RER = 0.85,
    feature_calc = TRUE, shrink_output = TRUE,
    verbose = FALSE, side = c("left", "right"),
    ...
  )

  sojourn(
    d,  time_var = "Timestamp", output_epoch = "default",
    min_mets = 1, max_mets = 20, warn_high_low = TRUE,
    met_mlkgmin = 3.5, RER = 0.85,
    shrink_output = TRUE, verbose = FALSE,
    axis1 = "Axis1", axis2 = "Axis2", axis3 = "Axis3",
    vector.magnitude = "Vector.Magnitude",
    method = c("SIP", "Sojourn 1x", "Sojourn 3x"),
    ..., met_name = "METs", tag = ""
  )

  staudenmayer(
    d, time_var = "Timestamp", output_epoch = "default",
    min_mets = 1, max_mets = 20, warn_high_low = TRUE,
    met_mlkgmin = 3.5, RER = 0.85,
    feature_calc = TRUE, shrink_output = TRUE,
    verbose = FALSE, ..., select = c("METs_lm", "METs_rf")
  )

Arguments

d

data frame of data to use for generating predictions

method

the method(s) to use

time_var

character. Name of the column containing POSIX-formatted timestamps

output_epoch

character. The desired epoch length of output. Acceptable options are "default" or else a setting appropriate for the unit argument of lubridate::floor_date()

warn_high_low

logical. Issue warnings when corrections are applied to values that are out of range?

met_mlkgmin

conversion factor for transforming oxygen consumption (in ml/kg/min) into metabolic equivalents (METs)

RER

the respiratory exchange ratio. Used for determining conversion factors when calculating caloric expenditure from oxygen consumption

feature_calc

logical. Calculate features for the selected method(s)? If FALSE, the assumption is that features have already been calculated

shrink_output

logical. Reduce the number of columns in output by removing calculated feature columns? Default is TRUE. May only have an impact on output in certain cases, particularly when setting
output_epoch = "default" ⁠ ⁠ and/or ⁠ ⁠ method = c("Montoye 2017", ...)

combine

logical. Combine results from each method into a single data frame? If TRUE (the default), the results will all be collapsed to a commonly-compatible epoch length, which may override output_epoch if a suitable selection is not given

ee_vars

character vector indicating which energy expenditure variables to return. Choose one or more of "METs", "VO2", and "kcal" (case insensitive)

verbose

logical. Print updates to console?

...

arguments passed to specific applicators and beyond. See details

min_mets

minimum allowable metabolic equivalent (MET) value. Values lower than this (if any) will be rounded up to it

max_mets

maximum allowable metabolic equivalent (MET) value. Values higher than this (if any) will be rounded down to it

min_vo2_mlkgmin

minimum allowable oxygen consumption value (in ml/kg/min). Values lower than this (if any) will be rounded up to it

max_vo2_mlkgmin

maximum allowable oxygen consumption value (in ml/kg/min). Values higher than this (if any) will be rounded down to it

model

character. Can be "VA" and/or "VM", specifying which of the Crouter 2015 model(s) to use

movement_var

character. name of the variable(s) on which the Crouter 2015 method should be applied. Length must match the length of model. It is assumed that the first elements of movement_var and model will correspond with one another, and the same for the second elements (if applicable)

age

the age group(s) of desired Hildebrand equation(s) to apply

monitor

the monitor being worn by the participant

location

the placement of the monitor on the body

enmo_name

name of the variable containing Euclidian Norm Minus One (ENMO) values

side

character vector or scalar indicating which side-specific wrist model(s) to implement for "Montoye 2017". Can be "left", "right", or c("left", "right")

axis1

for Sojourn 1x and Sojourn 3x, the name of the variable in d containing vertical axis activity counts

axis2

for Sojourn 3x, the name of the variable in d containing horizontal axis activity counts

axis3

for Sojourn 3x, the name of the variable in d containing lateral axis activity counts

vector.magnitude

for Sojourn 3x, the name of the variable in d containing vector magnitude activity counts

met_name

[for internal use] A character scalar giving the name of the column containing metabolic equivalent values (METs)

tag

[for internal use] A character scalar giving an informative tag to add when naming variables

select

[for internal use] A character scalar or vector indicating which Staudenmayer model(s) to run

Details

This is a wrapper and aggregator for applying different energy expenditure prediction methods. Depending on the value(s) specified in the method argument, calls are made to applicator functions (one per method). Most applicators require values to be passed in for additional variables. Thus, the signature for each applicator function is included above, in the usage section.

For TwoRegression methods, a customized internal wrapper (wrap_2RM) is used around TwoRegression. Additional arguments can be passed to that function directly through this one. Similarly for Sojourn methods, additional arguments can be passed directly to the corresponding functions from the Sojourn package. Links to those are below.

For Staudenmayer and Montoye methods, values can be passed directly to staudenmayer_features and montoye_features, respectively (if feature calculation is requested via the feature_calc argument).

Value

A data frame appended with new columns containing energy expenditure predictions

Note

Some things to be aware of:

1. Oxygen consumption values are converted to kcal using factors from the Lusk table (by default, 4.862 kcal/L, corresponding to RER of 0.85; see 'References' below).

2. Not all methods can necessarily be combined through a single call. This capability is dependent on the desired settings and format of the output. There are too many possibilities and contingencies to list in a single documentation file. Options and adaptations can be discussed on GitHub.

3. The wrap_2RM applicator does not have a formal min_mets argument because the default minima differ depending on the method being implemented. However, you can still pass a value for min_mets, and it will get forwarded to the TwoRegression package and applied as expected.

References

Crouter et al. (2006). doi:10.1152/japplphysiol.00818.2005

Crouter et al. (2010). doi:10.1249/MSS.0b013e3181c37458

Crouter et al. (2012), doi:10.1249/MSS.0b013e3182447825

Crouter et al. (2015). doi:10.1249/MSS.0000000000000502

Hibbing et al. (2018). doi:10.1249/MSS.0000000000001532

Hildebrand et al. (2014). doi:10.1249/MSS.0000000000000289

Hildebrand et al. (2017). doi:10.1111/sms.12795

Ellingson et al. (2017). doi:10.1088/1361-6579/aa6d00

Montoye et al. (2017). doi:10.1080/1091367X.2017.1337638

Lyden et al. (2014). doi:10.1249/MSS.0b013e3182a42a2d

Ellingson et al. (2016). doi:10.1249/MSS.0000000000000915

Staudenmayer et al. (2015). doi:10.1152/japplphysiol.00026.2015

Examples


#### Below, note the variations throughout the examples,
#### showing different ways you can customize the output



## Raw acceleration examples:

if (isTRUE(requireNamespace("read.gt3x", quietly = TRUE))) {

  f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")
  d <- stats::setNames(
    read.gt3x::read.gt3x(f, asDataFrame = TRUE, imputeZeroes = TRUE),
    c("Timestamp", "Accelerometer_X", "Accelerometer_Y", "Accelerometer_Z")
  )[1:30000, ]

  utils::head(
    accelEE(
      d, "Hibbing 2018", algorithm = 1,
      site = c("Left Wrist", "Right Wrist"),
      warn_high_low = FALSE, shrink_output = FALSE
    )
  )

  utils::head(
    accelEE(
      d, c("Hildebrand Linear", "Hildebrand Non-Linear"), age = "adult",
      monitor = "ActiGraph", location = "Wrist", warn_high_low = FALSE,
      ee_vars = c("METs", "kcal"), output_epoch = "60 sec"
    )
  )

  accelEE(
    d, c(
      "Montoye 2017", "Staudenmayer Linear",
      "Staudenmayer Random Forest"
    ), side = "left", ee_vars = "VO2", combine = FALSE
  )

}


## Activity count examples:

if (isTRUE(requireNamespace("TwoRegression", quietly = TRUE))) {

  data(count_data, package = "TwoRegression")

  utils::head(
    accelEE(
      count_data, c("Crouter 2006", "Crouter 2010"),
      movement_var = "Axis1", time_var = "time"
    )
  )

}


if (isTRUE(requireNamespace("Sojourn", quietly = TRUE))) {

  # Sojourn methods can't be implemented in a single call,
  # but you can chain them together, particularly with
  # `magrittr` piping although that is not shown below

  data(SIP_ag, package = "Sojourn")
  data(SIP_ap, package = "Sojourn")
  d <- Sojourn::enhance_actigraph(SIP_ag, SIP_ap)

  soj_results <- accelEE(d, "SIP", time_var = "Time", warn_high_low = FALSE)
  #^^Note that the SIP method causes a `Timestamp` variable to be
  #  silently populated, whereas the input data frame must have a column
  #  named `Time`. In general, the Sojourn methods (especially SIP) are
  #  currently coded somewhat inflexibly, often requiring specific
  #  variable names for the input. Your best bet for getting them to run
  #  is to copy, paste, and execute the package examples, then format your
  #  data to match the example data exactly.

  soj_results <- accelEE(
    soj_results, "Sojourn 1x", axis1 = "counts", time_var = "Time"
  )

  soj_results <- accelEE(
    soj_results, "Sojourn 3x", axis1 = "counts", axis2 = "axis2",
    axis3 = "axis3", vector.magnitude = "vm", output_epoch = "60 sec"
  )
  #^^Note that this collapses everything to one-minute epochs

  utils::head(soj_results)

}


#### Below, note the variations throughout the examples,
#### showing different ways you can customize the output



## Raw acceleration examples:

if (isTRUE(requireNamespace("read.gt3x", quietly = TRUE))) {

  f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")
  d <- stats::setNames(
    read.gt3x::read.gt3x(f, asDataFrame = TRUE, imputeZeroes = TRUE),
    c("Timestamp", "Accelerometer_X", "Accelerometer_Y", "Accelerometer_Z")
  )[1:30000, ]

  utils::head(
    accelEE(
      d, "Hibbing 2018", algorithm = 1,
      site = c("Left Wrist", "Right Wrist"),
      warn_high_low = FALSE, shrink_output = FALSE
    )
  )

  utils::head(
    accelEE(
      d, c("Hildebrand Linear", "Hildebrand Non-Linear"), age = "adult",
      monitor = "ActiGraph", location = "Wrist", warn_high_low = FALSE,
      ee_vars = c("METs", "kcal"), output_epoch = "60 sec"
    )
  )

  accelEE(
    d, c(
      "Montoye 2017", "Staudenmayer Linear",
      "Staudenmayer Random Forest"
    ), side = "left", ee_vars = "VO2", combine = FALSE
  )

}


## Activity count examples:

if (isTRUE(requireNamespace("TwoRegression", quietly = TRUE))) {

  data(count_data, package = "TwoRegression")

  utils::head(
    accelEE(
      count_data, c("Crouter 2006", "Crouter 2010"),
      movement_var = "Axis1", time_var = "time"
    )
  )

}


if (isTRUE(requireNamespace("Sojourn", quietly = TRUE))) {

  # Sojourn methods can't be implemented in a single call,
  # but you can chain them together, particularly with
  # `magrittr` piping although that is not shown below

  data(SIP_ag, package = "Sojourn")
  data(SIP_ap, package = "Sojourn")
  d <- Sojourn::enhance_actigraph(SIP_ag, SIP_ap)

  soj_results <- accelEE(d, "SIP", time_var = "Time", warn_high_low = FALSE)
  #^^Note that the SIP method causes a `Timestamp` variable to be
  #  silently populated, whereas the input data frame must have a column
  #  named `Time`. In general, the Sojourn methods (especially SIP) are
  #  currently coded somewhat inflexibly, often requiring specific
  #  variable names for the input. Your best bet for getting them to run
  #  is to copy, paste, and execute the package examples, then format your
  #  data to match the example data exactly.

  soj_results <- accelEE(
    soj_results, "Sojourn 1x", axis1 = "counts", time_var = "Time"
  )

  soj_results <- accelEE(
    soj_results, "Sojourn 3x", axis1 = "counts", axis2 = "axis2",
    axis3 = "axis3", vector.magnitude = "vm", output_epoch = "60 sec"
  )
  #^^Note that this collapses everything to one-minute epochs

  utils::head(soj_results)

}

Run a pre-specified processing scheme

Description

Pre-specified routines are designed to facilitate replication of methods from prior studies

Usage

ee_file(filename, scheme = "Hibbing 2023", ...)
ee_file(filename, scheme = "Hibbing 2023", ...)

Arguments

filename

character. Path to the file

scheme

character. Name of the routine to be executed. Currently the only option is "Hibbing 2023".

...

Arguments passed to sub-routine functions

Value

A data frame whose contents are prepared according to the indicated scheme

Examples

  f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")
  ee_file(f)
f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")
  ee_file(f)

Run a pre-specified summary scheme

Description

Pre-specified routines are designed to facilitate replication of methods from prior studies

Usage

ee_summary(d, scheme = "Hibbing 2023", ...)
ee_summary(d, scheme = "Hibbing 2023", ...)

Arguments

d

input data (presumably a data frame)

scheme

character. Name of the routine to be executed. Currently the only option is "Hibbing 2023".

...

Arguments passed to sub-routine functions

Value

A data frame whose contents are prepared according to the indicated scheme

Examples


f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")

## Read acceleration data
  accel <- ee_file(f)

## Generate some bogus count data
  cts <- data.frame(
    Timestamp = accel$Timestamp,
    Axis1 = 0, Axis2 = 0, Axis3 = 0,
    valid_status = "Non-Wear"
  )

## Returns NA with a warning about no complete days in the file
  suppressWarnings(ee_summary(
    merge(accel, cts)
  ))

f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")

## Read acceleration data
  accel <- ee_file(f)

## Generate some bogus count data
  cts <- data.frame(
    Timestamp = accel$Timestamp,
    Axis1 = 0, Axis2 = 0, Axis3 = 0,
    valid_status = "Non-Wear"
  )

## Returns NA with a warning about no complete days in the file
  suppressWarnings(ee_summary(
    merge(accel, cts)
  ))

Calculate generic features for model application

Description

Calculate generic features for model application

Usage

generic_features(
  d,
  time_var = "Timestamp",
  x_var = "Accelerometer_X",
  y_var = "Accelerometer_Y",
  z_var = "Accelerometer_Z",
  win_width_sec = 1,
  verbose = FALSE,
  ...
)
generic_features(
  d,
  time_var = "Timestamp",
  x_var = "Accelerometer_X",
  y_var = "Accelerometer_Y",
  z_var = "Accelerometer_Z",
  win_width_sec = 1,
  verbose = FALSE,
  ...
)

Arguments

d

data frame of data to use for generating predictions

time_var

character. Name of the column containing POSIX-formatted timestamps

x_var

character. Name of the X-axis variable

y_var

character. Name of the Y-axis variable

z_var

character. Name of the Z-axis variable

win_width_sec

desired window width for features

verbose

logical. Print updates to console?

...

currently unused

Value

A data frame of features in the specified epoch length

Examples

if (isTRUE(requireNamespace("read.gt3x"))) {

  f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")
  d <- stats::setNames(
    read.gt3x::read.gt3x(f, asDataFrame = TRUE, imputeZeroes = TRUE),
    c("Timestamp", "Accelerometer_X", "Accelerometer_Y", "Accelerometer_Z")
  )[1:30000, ]

  utils::head(generic_features(d))

}
if (isTRUE(requireNamespace("read.gt3x"))) {

  f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")
  d <- stats::setNames(
    read.gt3x::read.gt3x(f, asDataFrame = TRUE, imputeZeroes = TRUE),
    c("Timestamp", "Accelerometer_X", "Accelerometer_Y", "Accelerometer_Z")
  )[1:30000, ]

  utils::head(generic_features(d))

}

Run the Hibbing 2023 summary scheme

Description

Run the Hibbing 2023 summary scheme

Usage

ee_summary_hibbing23(d, verbose = FALSE)
ee_summary_hibbing23(d, verbose = FALSE)

Arguments

d

input data (presumably a data frame)

verbose

logical. Print updates to console?

Value

A data frame whose contents are prepared according to the indicated scheme

Examples


f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")

## Read acceleration data
  accel <- ee_file(f)

## Generate some bogus count data
  cts <- data.frame(
    Timestamp = accel$Timestamp,
    Axis1 = 0, Axis2 = 0, Axis3 = 0,
    valid_status = "Non-Wear"
  )

## Returns NA with a warning about no complete days in the file
  suppressWarnings(ee_summary(
    merge(accel, cts)
  ))

f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")

## Read acceleration data
  accel <- ee_file(f)

## Generate some bogus count data
  cts <- data.frame(
    Timestamp = accel$Timestamp,
    Axis1 = 0, Axis2 = 0, Axis3 = 0,
    valid_status = "Non-Wear"
  )

## Returns NA with a warning about no complete days in the file
  suppressWarnings(ee_summary(
    merge(accel, cts)
  ))

Calculate features for Montoye's neural networks

Description

Calculate features for Montoye's neural networks

Usage

montoye_features(
  d,
  time_var = "Timestamp",
  x_var = "Accelerometer_X",
  y_var = "Accelerometer_Y",
  z_var = "Accelerometer_Z",
  side = c("left", "right"),
  win_width_sec = 30,
  verbose = FALSE,
  ...
)
montoye_features(
  d,
  time_var = "Timestamp",
  x_var = "Accelerometer_X",
  y_var = "Accelerometer_Y",
  z_var = "Accelerometer_Z",
  side = c("left", "right"),
  win_width_sec = 30,
  verbose = FALSE,
  ...
)

Arguments

d

data frame of data to use for generating predictions

time_var

character. Name of the column containing POSIX-formatted timestamps

x_var

character. Name of the X-axis variable

y_var

character. Name of the Y-axis variable

z_var

character. Name of the Z-axis variable

side

character vector or scalar indicating which side-specific wrist model(s) to implement for "Montoye 2017". Can be "left", "right", or c("left", "right")

win_width_sec

desired window width for features

verbose

logical. Print updates to console?

...

currently unused

Value

A dataframe of features for entry into the neural networks

References

Montoye et al. (2017) doi:10.1080/1091367X.2017.1337638

Examples

if (isTRUE(requireNamespace("read.gt3x"))) {

  f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")
  d <- read.gt3x::read.gt3x(f, asDataFrame = TRUE, imputeZeroes = TRUE)

  montoye_features(d[1:12000, ], "time", "X", "Y","Z", "right")

}
if (isTRUE(requireNamespace("read.gt3x"))) {

  f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")
  d <- read.gt3x::read.gt3x(f, asDataFrame = TRUE, imputeZeroes = TRUE)

  montoye_features(d[1:12000, ], "time", "X", "Y","Z", "right")

}

Calculate features for Staudenmayer models

Description

Calculate features for Staudenmayer models

Usage

staudenmayer_features(
  d,
  time_var = "Timestamp",
  x_var = "Accelerometer_X",
  y_var = "Accelerometer_Y",
  z_var = "Accelerometer_Z",
  win_width_sec = 15,
  verbose = FALSE,
  ...
)
staudenmayer_features(
  d,
  time_var = "Timestamp",
  x_var = "Accelerometer_X",
  y_var = "Accelerometer_Y",
  z_var = "Accelerometer_Z",
  win_width_sec = 15,
  verbose = FALSE,
  ...
)

Arguments

d

data frame of data to use for generating predictions

time_var

character. Name of the column containing POSIX-formatted timestamps

x_var

character. Name of the X-axis variable

y_var

character. Name of the Y-axis variable

z_var

character. Name of the Z-axis variable

win_width_sec

desired window width for features

verbose

logical. Print updates to console?

...

currently unused

Value

Data frame containing features in the specified format

References

Staudenmayer et al. (2015), doi:10.1152/japplphysiol.00026.2015

Examples

if (isTRUE(requireNamespace("read.gt3x"))) {

  f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")
  d <- read.gt3x::read.gt3x(f, asDataFrame = TRUE, imputeZeroes = TRUE)

  staudenmayer_features(d[1:12000, ], "time", "X", "Y", "Z")

}
if (isTRUE(requireNamespace("read.gt3x"))) {

  f <- system.file("extdata/TAS1H30182785_2019-09-17.gt3x", package = "read.gt3x")
  d <- read.gt3x::read.gt3x(f, asDataFrame = TRUE, imputeZeroes = TRUE)

  staudenmayer_features(d[1:12000, ], "time", "X", "Y", "Z")

}

Package 'accelEE'

Help Index

Predict energy expenditure for accelerometry data

Description

Usage

Arguments

Details

Value

Note

References

See Also

Examples

Run a pre-specified processing scheme

Description

Usage

Arguments

Value

See Also

Examples

Run a pre-specified summary scheme

Description

Usage

Arguments

Value

See Also

Examples

Calculate generic features for model application

Description

Usage

Arguments

Value

Examples

Run the Hibbing 2023 summary scheme

Description

Usage

Arguments

Value

Examples

Calculate features for Montoye's neural networks

Description

Usage

Arguments

Value

References

Examples

Calculate features for Staudenmayer models

Description

Usage

Arguments

Value

References

Examples