get_walk_features: Preprocess and extract interpretable features from walk...

In Sage-Bionetworks/mHealthTools: mhealthtools - Package for processing mobile sensor data and to extract meaningful features

Description

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

Usage

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.

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.

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.

Author(s)

Thanneer Malai Perumal, Meghasyam Tummalacherla, Phil Snyder

See Also

walk_data

Examples

walk_features <- get_walk_features(
  accelerometer_data,
  gyroscope_data)

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)

walk_features <- get_walk_features(
  accelerometer_data,
  gyroscope_data,
  funs = list(time_domain_summary))

Sage-Bionetworks/mHealthTools documentation built on Sept. 21, 2020, 12:35 p.m.