Preprocess and extract interpretable features from walk activity.

A convenience wrapper for extracting interpretable features from the walk activity measured using smartphone raw accelerometer and gyroscope sensors.

get_walk_features(
  accelerometer_data = NULL,
  gyroscope_data = NULL,
  gravity_data = NULL,
  time_filter = NULL,
  detrend = F,
  frequency_filter = NULL,
  IMF = 2,
  window_length = NULL,
  window_overlap = NULL,
  derived_kinematics = F,
  funs = NULL,
  models = NULL
)

Arguments

accelerometer_data	An n x 4 data frame with columns t, x, y, z containing accelerometer measurements. Here n is the total number of measurements, t is the timestamp of each measurement, and x, y and z are linear acceleration measurements.
gyroscope_data	An n x 4 data frame with columns t, x, y, z containing gyroscope measurements. Here n is the total number of measurements, t is the timestamp of each measurement, and x, y and z are linear velocity measurements.
gravity_data	An n x 4 data frame with columns t, x, y, z containing gravity measurements. Here n is the total number of measurements, t is the timestamp of each measurement, and x, y and z are linear gravity measurements.
time_filter	A length 2 numeric vector specifying the time range of measurements to use during preprocessing and feature extraction after normalizing the first timestamp to 0. A NULL value means do not filter any measurements.
detrend	A logical value indicating whether to detrend the signal. By default the value is FALSE.
frequency_filter	A length 2 numeric vector specifying the frequency range of the signal (in hertz) to use during preprocessing and feature extraction. A NULL value means do not filter frequencies.
IMF	The number of IMFs used during an empirical mode decomposition (EMD) transformation. The default value of 1 means do not apply EMD to the signal.
window_length	A numerical value representing the length (in number of samples) of the sliding window used during the windowing transformation. Both window_length and window_overlap must be set for the windowing transformation to be applied.
window_overlap	Fraction in the interval [0, 1) specifying the amount of window overlap during a windowing transformation. Both window_length and window_overlap must be set for the windowing transformation to be applied.
derived_kinematics	A logical value specifying whether to add derived kinematic measurements (displacement, velocity or acceleration, and jerk) to accelerometer_data and gyroscope_data after the transform defined by the above parameters has been applied to the raw sensor measurements.
funs	A function or list of functions that each accept a single numeric vector as input. Each function should return a dataframe of features (normally a single-row datafame, with column names as feature names). The input vectors will be the axial measurements from accelerometer_data and/or gyroscope_data after the transform defined by the above parameters has been applied.If no argument is supplied to either funs or models, a default set of feature extraction functions (as described in default_kinematic_features) will be supplied for this parameter.
models	A function or list of functions that each accept sensor_data as input after the transform defined by the above parameters has been applied and returns features. Useful for functions which compute individual features using multiple input variables.

Value

A list. The outputs from funs will be stored under $extracted_features and the outputs from models will be stored under $model_features. If there is an error during the transform process, an error dataframe will be stored under $error. If gravity_data is passed and window_length and window_overlap are set, phone rotation information will be stored under $outlier_windows.

Details

The walk activity entails participants walking in a straight line for approximately 20 steps with the mobile device in their pocket or in a bag.

See also

walk_data

Examples

walk_features <- get_walk_features(
  accelerometer_data,
  gyroscope_data)
#> Joining, by = c("axis", "IMF", "window")
#> Joining, by = c("axis", "IMF", "window")

walk_features <- get_walk_features(
  accelerometer_data,
  gyroscope_data,
  time_filter = c(2,4),
  detrend = TRUE,
  frequency_filter = c(0.5,25),
  window_length = 256,
  window_overlap = 0.2,
  derived_kinematics = TRUE)
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Joining, by = c("axis", "IMF", "window")
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Joining, by = c("axis", "IMF", "window")

walk_features <- get_walk_features(
  accelerometer_data,
  gyroscope_data,
  funs = list(time_domain_summary))
#> Joining, by = c("axis", "IMF", "window")
#> Joining, by = c("axis", "IMF", "window")