sit2standpy.TransitionQuantifier¶
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class
sit2standpy.TransitionQuantifier¶ Quantification of a sit-to-stand transition.
Methods
quantify(times, fs[, raw_acc, mag_acc_f, …])Compute quantitative values from the provided signals sparc(x, fs[, padlevel, fc, amp_th])SPectral ARC length metric for quantifying smoothness -
quantify(times, fs, raw_acc=None, mag_acc_f=None, mag_acc_r=None, v_vel=None, v_pos=None)¶ Compute quantitative values from the provided signals
Parameters: - times : tuple
Tuple of the start and end timestamps for the transition
- mag_acc_f : {None, numpy.ndarray}, optional
Filtered acceleration magnitude during the transition.
- mag_acc_r : {None, numpy.ndarray}, optional
Reconstructed acceleration magnitude during the transition.
- v_vel : {None, numpy.ndarray}, optional
Vertical velocity during the transition.
- v_pos : {None, numpy.ndarray}, optional
Vertical position during the transition.
Returns: - transition : Transition
Transition object containing metrics quantifying the transition.
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static
sparc(x, fs, padlevel=4, fc=10.0, amp_th=0.05)¶ SPectral ARC length metric for quantifying smoothness
Parameters: - x : numpy.ndarray
Array containing the data to be analyzed for smoothness
- fs : float
Sampling frequency
- padlevel : int, optional
Indicates the amount of zero-padding to be done to the movement data for estimating the spectral arc length. Default is 4.
- fc : float, optional
The max cutoff frequency for calculating the spectral arc length metric. Default is 10.0
- amp_th : float, optional
The amplitude threshold to be used for determining the cut off frequency up to which the spectral arc length is to be estimated. Default is 0.05
Returns: - sal : float
The spectral arc length estimate of the given data’s smoothness
- (f, Mf) : (numpy.ndarray, numpy.ndarray)
The frequency and the magnitude spectrum of the input data. This spectral is from 0 to the Nyquist frequency
- (f_sel, Mf_sel) : (numpy.ndarray, numpy.ndarray)
The portion of the spectrum that is selected for calculating the spectral arc length
References
S. Balasubramanian, A. Melendez-Calderon, A. Roby-Brami, E. Burdet. “On the analysis of movement smoothness.” Journal of NeuroEngineering and Rehabilitation. 2015.
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