A convenience wrapper function for extracting interpretable features from triaxial gyroscope data collected through smartphones.
gyroscope_features( sensor_data, time_filter = NULL, detrend = F, frequency_filter = NULL, IMF = 1, window_length = NULL, window_overlap = NULL, derived_kinematics = F, funs = NULL, models = NULL )
sensor_data | An |
---|---|
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 |
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 |
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_overlap | Fraction in the interval [0, 1) specifying the amount of
window overlap during a windowing transformation.
Both |
derived_kinematics | A logical value specifying whether to add derived
kinematic 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 |
models | A function or list of functions that each accept
|
A list of gyroscope features. The output from funs
will
be stored under $extracted_features
and the output from models
will be stored under $model_features
. If there is an error during
extraction, the returned result will be stored under $error
.
gyro_features <- gyroscope_features(gyroscope_data)#>gyro_features <- gyroscope_features(gyroscope_data, IMF = 3)#>gyro_features <- gyroscope_features( gyroscope_data, time_filter = c(2, 5), detrend = TRUE, frequency_filter = c(4, 16), window_length = 256, window_overlap = 0.5, derived_kinematics = TRUE, funs = time_domain_summary)#>