sit2standpy.v2.AccelerationFilter

class sit2standpy.v2.filters.AccelerationFilter(continuous_wavelet='gaus1', power_band=None, power_peak_kw=None, power_std_height=True, power_std_trim=0, reconstruction_method='moving average', lowpass_order=4, lowpass_cutoff=5, window=0.25, discrete_wavelet='dmey', extension_mode='constant', reconstruction_level=1, **kwargs)

Filter acceleration and located potential sit-to-stand time points.

Parameters:

continuous_wavelet : str, optional

Continuous wavelet to use for signal deconstruction. Default is ‘gaus1’. CWT coefficients will be summed in the frequency range defined by power_band

power_band : {array_like, int, float}, optional

Frequency band in which to sum the CWT coefficients. Either an array_like of length 2, with the lower and upper limits, or a number, which will be taken as the upper limit, and the lower limit will be set to 0. Default is [0, 0.5].

power_peak_kw : {None, dict}, optional

Extra key-word arguments to pass to scipy.signal.find_peaks when finding peaks in the summed CWT coefficient power band data. Default is None, which will use the default parameters except setting minimum height to 90, unless power_std_height is True.

power_std_height : bool, optional

Use the standard deviation of the power for peak finding. Default is True. If True, the standard deviation height will overwrite the height setting in power_peak_kw.

power_std_trim : float, int, optional

Number of seconds to trim off the start and end of the power signal before computing the standard deviation for power_std_height. Default is 0s, which will not trim anything. Suggested value of trimming is 0.5s.

reconstruction_method : {‘moving average’, ‘dwt’}, optional

Method for computing the reconstructed acceleration. Default is ‘moving average’, which takes the moving average over the specified window. Other option is ‘dwt’, which uses the discrete wavelet transform to deconstruct and reconstruct the signal while filtering noise out.

lowpass_order : int, optional

Initial low-pass filtering order. Default is 4.

lowpass_cutoff : float, optional

Initial low-pass filtering cuttoff, in Hz. Default is 5Hz.

window : float, optional

Window to use for moving average, in seconds. Default is 0.25s. Ignored if reconstruction_method is ‘dwt’.

discrete_wavelet : str, optional

Discrete wavelet to use if reconstruction_method is ‘dwt’. Default is ‘dmey’. See pywt.wavelist(kind=’discrete’) for a complete list of options. Ignored if reconstruction_method is ‘moving average’.

extension_mode : str, optional

Signal extension mode to use in the DWT de- and re-construction of the signal. Default is ‘constant’, see pywt.Modes.modes for a list of options. Ignored if reconstruction_method is ‘moving average’.

reconstruction_level : int, optional

Reconstruction level of the DWT processed signal. Default is 1. Ignored if reconstruction_method is ‘moving average’.

Notes

The default height threshold of 90 in power_peak_kw was determined on data sampled at 128Hz, and would likely need to be adjusted for different sampling frequencies. Especially if using a different sampling frequency, use of power_std_height=True is recommended.

Attributes:

data

Methods

predict(data)

Fit and transform the data with the given process.