def project_walk_direction_attitude(ax, ay, az, uw, ux, uy, uz):
"""
Project accelerometer data on local walk direction by attitude rotation.
Parameters
----------
ax : list or numpy array
x-axis accelerometer data
ay : list or numpy array
y-axis accelerometer data
az : list or numpy array
z-axis accelerometer data
uw : list or numpy array
w of attitude quaternion
ux : list or numpy array
x of attitude quaternion
uy : list or numpy array
y of attitude quaternion
uz : list or numpy array
z of attitude quaternion
Returns
-------
px : numpy array
accelerometer data along x axis projected on unit vector
py : numpy array
accelerometer data along y axis projected on unit vector
pz : numpy array
accelerometer data along z axis projected on unit vector
Examples
--------
>>> from mhealthx.xio import read_accel_json
>>> from mhealthx.signals import compute_sample_rate
>>> input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/deviceMotion_walking_outbound.json.items-a2ab9333-6d63-4676-977a-08591a5d837f5221783798792869048.tmp'
>>> device_motion = True
>>> start = 0
>>> t, axyz, gxyz, wxyz, rxyz, sample_rate, duration = read_accel_json(input_file, start, device_motion)
>>> ax, ay, az = axyz
>>> uw, ux, uy, uz = wxyz
>>> from mhealthx.extractors.pyGait import project_walk_direction_attitude
>>> px, py, pz = project_walk_direction_attitude(ax, ay, az, uw, ux, uy, uz)
"""
from mhealthx.extractors.pyGait import walk_direction_attitude, \
project_axes
directions = walk_direction_attitude(ax, ay, az, uw, ux, uy, uz)
vectors = project_axes(zip(ax, ay, az), directions)
px = [x[0] for x in vectors]
py = [x[1] for x in vectors]
pz = [x[2] for x in vectors]
return px, py, pz
def project_walk_direction_preheel(ax, ay, az, t, sample_rate,
stride_fraction, threshold, order, cutoff):
"""
Project accelerometer data on local walk (not cardinal) direction.
NOTE::
This calls walk_direction_preheel(), which computes a single walk
direction. It does NOT estimate walking direction for every time
point, like walk_direction_attitude().
Parameters
----------
ax : numpy array
accelerometer data along x axis
ay : numpy array
accelerometer data along y axis
az : numpy array
accelerometer data along z axis
t : list or numpy array
accelerometer time points
sample_rate : float
sample rate of accelerometer reading (Hz)
stride_fraction : float
fraction of stride assumed to be deceleration phase of primary leg
threshold : float
ratio to the maximum summed acceleration to extract peaks
order : integer
order of the Butterworth filter
cutoff : integer
cutoff frequency of the Butterworth filter (Hz)
Returns
-------
px : numpy array
accelerometer data along x axis projected on unit vector
py : numpy array
accelerometer data along y axis projected on unit vector
pz : numpy array
accelerometer data along z axis projected on unit vector
Examples
--------
>>> from mhealthx.xio import read_accel_json
>>> from mhealthx.extractors.pyGait import project_walk_direction_preheel
>>> input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/deviceMotion_walking_outbound.json.items-a2ab9333-6d63-4676-977a-08591a5d837f5221783798792869048.tmp'
>>> device_motion = True
>>> start = 150
>>> t, axyz, gxyz, uxyz, rxyz, sample_rate, duration = read_accel_json(input_file, start, device_motion)
>>> ax, ay, az = axyz
>>> stride_fraction = 1.0/8.0
>>> threshold = 0.5
>>> order = 4
>>> cutoff = max([1, sample_rate/10])
>>> px, py, pz = project_walk_direction_preheel(ax, ay, az, t, sample_rate, stride_fraction, threshold, order, cutoff)
"""
from mhealthx.extractors.pyGait import walk_direction_preheel, \
project_axes
directions = walk_direction_preheel(ax, ay, az, t, sample_rate,
stride_fraction, threshold, order,
cutoff, False)
vectors = project_axes(zip(ax, ay, az), directions)
px = [x[0] for x in vectors]
py = [x[1] for x in vectors]
pz = [x[2] for x in vectors]
return px, py, pz
