Submodules¶

sensbiotk.algorithms.basic module¶

Basic algo for sensors data

sensbiotk.algorithms.basic.compute_norm(sig_raw)[source]¶

Norm on a signal

Returns:	sig_norm : numpy array of float norm of the signal

sensbiotk.algorithms.basic.cut_signal(sig_time, sig_raw, time0, timef)[source]¶

Cut the signal on a windows between time0 and timef.

Returns:	sig_time:numpy array of float : time signal between time0 and timef sig_cut : numpy array of float signal cutted in a window time0 and timef.

sensbiotk.algorithms.basic.find_peaks(sig_raw, min_peak_height, min_peak_distance, max_peak_distance)[source]¶

Find locations and peak values in a 1D numpy array,

taking into account a minimal peak height, min distance

and max distance.

Returns:	sig_raw[locs] : numpy array of float detected peaks values locs : numpy array of float detected peaks locations

sensbiotk.algorithms.basic.find_static_periods(sig_raw, static_threshold, min_sample_duration)[source]¶

Find locations of static periods in a 1D numpy array, taking into account a minimal static threshold (ex: 3 deg/s), and a minimum nb of samples for the static duration.

Returns:	start_idx : numpy array of float start periods locations end_idx : numpy array of float end periods locations

sensbiotk.algorithms.basic.find_static_periods_3D(sig_raw, static_threshold, min_sample_duration)[source]¶

Find locations of static periods in a 3D numpy array, taking into account a minimal static threshold (ex: 3 deg/s), and a minimum nb of samples for the static duration.

Returns:	start_idx : numpy array of float start periods locations end_idx : numpy array of float end periods locations

sensbiotk.algorithms.basic.lowpass_filter(sig_raw, cuttoff_freq, samp_freq)[source]¶

Low pass filter (butterworth order 6) on a signal

Returns:	sig_filt : numpy array of float of dim N signal filtered :

sensbiotk.algorithms.basic.lowpass_filter2(sig_raw, cuttoff_freq, samp_freq)[source]¶

Simple low pass filter on a signal

Returns:	sig_filt : numpy array of float of dim N signal filtered :

sensbiotk.algorithms.basic.moving_average(sig_raw, fen)[source]¶

Moving Average on a signal

Returns:	sig_filt : numpy array of float signal filtered :

sensbiotk.algorithms.basic.moving_average2(sig_raw, fen)[source]¶

Moving Average using convolution on a signal

Returns:	sig_filt : numpy array of float signal filtered :

sensbiotk.algorithms.basic.search_maxpeak(sig_raw)[source]¶

Search max peak

Returns:	time_peak : numpy array of float time where a max peak has been detected index_peak : numpy array of float index where a max peak has been detected

sensbiotk.algorithms.basic.threshold_signal(sig_time, sig_raw, threshold)[source]¶

Threshold signal

Returns:	time_thresh : numpy array of float time signal thresholed index_peak : numpy array of float signal thresholed

sensbiotk.algorithms.basic.threshold_under_signal(sig_time, sig_raw, threshold)[source]¶

Threshold under signal

Returns:	time_thresh : numpy array of float time signal thresholed index_peak : numpy array of float signal thresholed

sensbiotk.algorithms.basic.window_mean(sig_time, sig_raw, time0, timef)[source]¶

Mean of the signal on a windows between time0 and timef.

Returns:	sig_mean : numpy array of float mean of the signal assumed to be constant between time0 and timef.

sensbiotk.algorithms.goniometer module¶

Computes rotation angle between two quaternions

@author: bsijober

sensbiotk.algorithms.goniometer.compute(q_0, q_1, q_offset=[1, 0, 0, 0])[source]¶

Computes rotation angle between two quaternions

Returns:	angle : float the angle of rotation between q_0 and q_1 (rad) rot_axis : numpy array of float the axis of rotation

sensbiotk.algorithms.integration_gyro module¶

Gyrometer integration signal

sensbiotk.algorithms.integration_gyro.compute(data, freqs=200)[source]¶

Compute a minimized drift integration of the gyrometer signal

sensbiotk.algorithms.madgwick_ahrs module¶

This algorithms is based on the work of Sebastian Madgwick described in :

Madgwick, Estimation of IMU and MARG orientation using a gradient descent algorithm. 2011 Ieee International Conference on Rehabilitation Robotics (Icorr), 2011.

It has been implemented in Python from the original Matlab author code.

It enables the computation of a quaternion from magneto-inertial measurements.

sensbiotk.algorithms.madgwick_ahrs.norm(x)[source]¶

Computes the norm of x

sensbiotk.algorithms.madgwick_ahrs.update(q, z, fs=200)[source]¶

Updates the computed q quaternion

sensbiotk.algorithms.mahony_ahrs module¶

This algorithms is based on the work of Robert Mahony described in :

Mahony, R., T. Hamel, and J.-M. Pflimlin, Nonlinear Complementary Filters on the Special Orthogonal Group. Automatic Control, IEEE Transactions on, 2008. 53(5): p. 1203-1218.

It enables the computation of a quaternion from magneto-inertial measurements.

sensbiotk.algorithms.mahony_ahrs.norm(x)[source]¶

class sensbiotk.algorithms.mahony_ahrs.obj[source]¶

eInt = 0¶

sensbiotk.algorithms.mahony_ahrs.update(q, z, fs=200)[source]¶

sensbiotk.algorithms.markley module¶

Fast Quaternion Attitude Estimation from Accelerometer and Magnetometers

sensbiotk.algorithms.markley.compute(z, a=array([ 0.5, 0.5]))[source]¶

Fast Quaternion Attitude Estimation from Accelerometer and Magnetometers 3D measures.

See Publication : Fast Quaternion Attitude Estimation from Two Vector Measurements, L. Markley, NASA Tech Doc, 2001 https://archive.org/details/nasa_techdoc_20010068636

Returns:	q : numpy array of float (dim 4) attitude quaternion

sensbiotk.algorithms.martin_ahrs module¶

Martin Salaun observer implementation

This algorithms is based on the following work :

Martin, P. and E. Salaün, Design and implementation of a low-cost observer-based attitude and heading reference system. Control Engineering Practice, 2010. 18(7): p. 712-722.

It enables the computation of a quaternion from magneto-inertial measurements.

class sensbiotk.algorithms.martin_ahrs.martin_ahrs[source]¶

Bases: object

Martin Salaun observer class

Methods

init_observer(z)[source]¶

Martin Salaun init observer

update(z, sample_period)[source]¶

Martin Salaun iteration observer

Module contents¶