skdh.gait.substeps.PreprocessGaitBout#

class skdh.gait.substeps.PreprocessGaitBout(correct_orientation=True, filter_cutoff=20.0, filter_order=4, ap_axis_filter_kw=None)#

Preprocess acceleration data for gait using the newer/V2 method.

Parameters:
correct_orientationbool, optional

Correct the accelerometer orientation if it is slightly mis-aligned with the anatomical axes. Default is True.

filter_cutofffloat, optional

Low-pass filter cutoff in Hz. Default is 20.0

filter_orderint, optional

Low-pass filter order. Default is 4.

ap_axis_filter_kw{None, dict}, optional

Key-word arguments for the filter applied to the acceleration data before cross-correlation when estimating the AP axis. If None (default), the following are used:

  • N: 4

  • Wn: [0.25, 7.5] - NOTE, this should be in Hz, not radians. fs will be passed into the filter setup at filter creation time.

  • btype: band

  • output: sos - NOTE that this will always be set/overriden

See scipy.signal.butter() for full options.

Methods

convert_timestamps(t)

Convert a timestamp/array of timestamps to a datetime object

get_ap_axis(fs, accel, v_axis)

Estimate the AP axis index

get_ap_axis_sign(fs, accel, ap_axis)

Estimate the sign of the AP axis

get_step_time(fs, accel, ap_axis)

Estimate the average step time for the walking bout.

predict(time, accel, *[, fs, v_axis, ap_axis])

save_results(results, file_name)

Save the results of the processing pipeline to a csv file
get_ap_axis(fs, accel, v_axis)#

Estimate the AP axis index

Parameters:
fs
accel
v_axis
Returns:
ap_axis{0, 1, 2}

Index of the AP axis in the acceleration data

static get_ap_axis_sign(fs, accel, ap_axis)#

Estimate the sign of the AP axis

Parameters:
fsfloat

Sampling frequency in Hz.

accelnumpy.ndarray
ap_axisint

Anterior-Posterior axis

Returns:
ap_axis_sign{-1, 1}

Sign of the AP axis.

static get_step_time(fs, accel, ap_axis)#

Estimate the average step time for the walking bout.

Parameters:
fs
accel
ap_axis
Returns:
mean_step_timefloat

Mean step time for the walking bout

predict(time, accel, *, fs=None, v_axis=None, ap_axis=None)#
Parameters:
timenumpy.ndarray

(N, ) array of unix timestamps, in seconds

accelnumpy.ndarray

(N, 3) array of accelerations measured by a centrally mounted lumbar inertial measurement device, in units of ‘g’.

fsfloat, optional

Sampling frequency in Hz of the accelerometer data. If not provided, will be computed form the timestamps.

v_axis{None, 0, 1, 2}, optional

Index of the vertical axis in the acceleration data. Default is None. If None, will be estimated from the acceleration data.

ap_axis{None, 0, 1, 2}, optional

Index of the Anterior-Posterior axis in the acceleration data. Default is None. If None, will be estimated from the acceleration data.

Returns:
resultsdict

Dictionary with the following items that can be used for future processing steps:

  • v_axis: provided or estimated vertical axis index.

  • v_axis_est: estimated vertical axis index.

  • v_axis_sign: sign of the vertical axis.

  • ap_axis: provided or estimated AP axis index.

  • ap_axis_est: estimated AP axis index.

  • ap_axis_sign: estimated sign of the AP axis.

  • mean_step_freq: estimated mean step frequency during this gait bout.

  • accel_filt: filtered and detrended acceleration for this gait bout.