skdh.context.PredictGaitLumbarLgbm#

class skdh.context.PredictGaitLumbarLgbm(downsample_aa_filter=True)#

Process lumbar acceleration data to predict bouts of gait using a Light Gradient Boosted model.

Predictions are computed on non-overlappping 3-second windows.

Parameters:

downsample_aa_filterbool, optional: Apply an anti-aliasing filter when downsampling accelerometer data. Default is True.

Methods

`convert_timestamps`(t)	Convert a timestamp/array of timestamps to a datetime object
`predict`(*, time, accel[, fs])	Predict gait bouts.
`save_results`(results, file_name)	Save the results of the processing pipeline to a csv file

predict(*, time, accel, fs=None)#

Predict gait bouts.

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.

Returns:

gait_boutsnumpy.ndarray: (N, 2) array of indices of the starts (column 1) and stops (column 2) of gait bouts.

Other Parameters:

tz_name{None, str}, optional: IANA time-zone name for the recording location if passing in time as UTC timestamps. Can be ignored if passing in naive timestamps.

Raises: