def run_pyGait(data, t, sample_rate, duration, threshold, order, cutoff,
distance, row, file_path, table_stem, save_rows=False):
"""
Run pyGait (replication of iGAIT) accelerometer feature extraction code.
Steps ::
1. Run pyGait accelerometer feature extraction.
2. Construct a feature row from the original and pyGait rows.
3. Write the feature row to a table or append to a feature table.
Parameters
----------
data : numpy array
accelerometer data along any (preferably forward walking) axis
t : list or numpy array
accelerometer time points
sample_rate : float
sample rate of accelerometer reading (Hz)
duration : float
duration of accelerometer reading (s)
threshold : float
ratio to the maximum value of the anterior-posterior acceleration
order : integer
order of the Butterworth filter
cutoff : integer
cutoff frequency of the Butterworth filter (Hz)
distance : float
estimate of distance traversed
row : pandas Series
row to prepend, unaltered, to feature row
file_path : string
path to accelerometer file (from row)
table_stem : string
prepend to output table file
save_rows : Boolean
save individual rows rather than write to a single feature table?
Returns
-------
feature_row : pandas Series
row combining the original row with a row of pyGait feature values
feature_table : string
output table file (full path)
Examples
--------
>>> import pandas as pd
>>> from mhealthx.xio import read_accel_json
>>> from mhealthx.extract import run_pyGait
>>> from mhealthx.extractors.pyGait import project_on_walking_direction
>>> input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/accel_walking_outbound.json.items-6dc4a144-55c3-4e6d-982c-19c7a701ca243282023468470322798.tmp'
>>> start = 150
>>> device_motion = False
>>> 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
>>> threshold0 = 0.5
>>> threshold = 0.2
>>> order = 4
>>> cutoff = max([1, sample_rate/10])
>>> distance = None
>>> row = pd.Series({'a':[1], 'b':[2], 'c':[3]})
>>> file_path = '/fake/path'
>>> table_stem = './walking'
>>> save_rows = True
>>> px, py, pz = project_on_walking_direction(ax, ay, az, t, sample_rate, stride_fraction, threshold0, order, cutoff)
>>> feature_row, feature_table = run_pyGait(py, t, sample_rate, duration, threshold, order, cutoff, distance, row, file_path, table_stem, save_rows)
"""
import pandas as pd
from mhealthx.extractors.pyGait import heel_strikes, gait
from mhealthx.extract import make_row_table
# Extract features from data:
strikes, strike_indices = heel_strikes(data, sample_rate, threshold,
order, cutoff, False, t)
number_of_steps, cadence, velocity, avg_step_length, avg_stride_length,\
step_durations, avg_step_duration, sd_step_durations, strides, \
stride_durations, avg_number_of_strides, avg_stride_duration, \
sd_stride_durations, step_regularity, stride_regularity, \
symmetry = gait(strikes, data, duration, distance)
# Create row of data:
row_data = pd.DataFrame({'number_of_steps': number_of_steps,
'cadence': cadence,
'velocity': velocity,
'avg_step_length': avg_step_length,
'avg_stride_length': avg_stride_length,
'avg_step_duration': avg_step_duration,
'sd_step_durations': sd_step_durations,
'avg_number_of_strides': avg_number_of_strides,
'avg_stride_duration': avg_stride_duration,
'sd_stride_durations': sd_stride_durations,
'step_regularity': step_regularity,
'stride_regularity': stride_regularity,
'symmetry': symmetry},
index=[0])
# Write feature row to a table or append to a feature table:
feature_row, feature_table = make_row_table(file_path, table_stem,
save_rows, row, row_data,
feature_row=None)
return feature_row, feature_table
def run_pyGait(data, t, sample_rate, duration, threshold, order, cutoff,
distance, row, file_path, table_stem, save_rows=False):
"""
Run pyGait (replication of iGAIT) accelerometer feature extraction code.
Steps ::
1. Run pyGait accelerometer feature extraction.
2. Construct a feature row from the original and pyGait rows.
3. Write the feature row to a table or append to a feature table.
Parameters
----------
data : numpy array
accelerometer data along any (preferably forward walking) axis
t : list or numpy array
accelerometer time points
sample_rate : float
sample rate of accelerometer reading (Hz)
duration : float
duration of accelerometer reading (s)
threshold : float
ratio to the maximum value of the anterior-posterior acceleration
order : integer
order of the Butterworth filter
cutoff : integer
cutoff frequency of the Butterworth filter (Hz)
distance : float
estimate of distance traversed
row : pandas Series
row to prepend, unaltered, to feature row
file_path : string
path to accelerometer file (from row)
table_stem : string
prepend to output table file
save_rows : Boolean
save individual rows rather than write to a single feature table?
Returns
-------
feature_row : pandas Series
row combining the original row with a row of pyGait feature values
feature_table : string
output table file (full path)
Examples
--------
>>> import pandas as pd
>>> from mhealthx.xio import read_accel_json
>>> from mhealthx.extract import run_pyGait
>>> from mhealthx.extractors.pyGait import project_on_walking_direction
>>> input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/accel_walking_outbound.json.items-6dc4a144-55c3-4e6d-982c-19c7a701ca243282023468470322798.tmp'
>>> start = 150
>>> device_motion = False
>>> 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
>>> threshold0 = 0.5
>>> threshold = 0.2
>>> order = 4
>>> cutoff = max([1, sample_rate/10])
>>> distance = None
>>> row = pd.Series({'a':[1], 'b':[2], 'c':[3]})
>>> file_path = '/fake/path'
>>> table_stem = './walking'
>>> save_rows = True
>>> px, py, pz = project_on_walking_direction(ax, ay, az, t, sample_rate, stride_fraction, threshold0, order, cutoff)
>>> feature_row, feature_table = run_pyGait(py, t, sample_rate, duration, threshold, order, cutoff, distance, row, file_path, table_stem, save_rows)
"""
import pandas as pd
from mhealthx.extractors.pyGait import heel_strikes, gait
from mhealthx.extract import make_row_table
# Extract features from data:
strikes, strike_indices = heel_strikes(data, sample_rate, threshold,
order, cutoff, False, t)
number_of_steps, cadence, velocity, avg_step_length, avg_stride_length,\
step_durations, avg_step_duration, sd_step_durations, strides, \
stride_durations, avg_number_of_strides, avg_stride_duration, \
sd_stride_durations, step_regularity, stride_regularity, \
symmetry = gait(strikes, data, duration, distance)
# Create row of data:
row_data = pd.DataFrame({'number_of_steps': number_of_steps,
'cadence': cadence,
'velocity': velocity,
'avg_step_length': avg_step_length,
'avg_stride_length': avg_stride_length,
'avg_step_duration': avg_step_duration,
'sd_step_durations': sd_step_durations,
'avg_number_of_strides': avg_number_of_strides,
'avg_stride_duration': avg_stride_duration,
'sd_stride_durations': sd_stride_durations,
'step_regularity': step_regularity,
'stride_regularity': stride_regularity,
'symmetry': symmetry},
index=[0])
# Write feature row to a table or append to a feature table:
feature_row, feature_table = make_row_table(file_path, table_stem,
save_rows, row, row_data,
feature_row=None)
return feature_row, feature_table
def walk_direction_preheel(ax, ay, az, t, sample_rate,
stride_fraction=1.0/8.0, threshold=0.5,
order=4, cutoff=5, plot_test=False):
"""
Estimate local walk (not cardinal) direction with pre-heel strike phase.
Inspired by Nirupam Roy's B.E. thesis: "WalkCompass:
Finding Walking Direction Leveraging Smartphone's Inertial Sensors,"
this program derives the local walk direction vector from the end
of the primary leg's stride, when it is decelerating in its swing.
While the WalkCompass relies on clear heel strike signals across the
accelerometer axes, this program just uses the most prominent strikes,
and estimates period from the real part of the FFT of the data.
NOTE::
This algorithm computes a single walk direction, and could compute
multiple walk directions prior to detected heel strikes, but does
NOT estimate walking direction for every time point, like
walk_direction_attitude().
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
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 value of the summed acceleration across axes
plot_test : Boolean
plot most prominent heel strikes?
Returns
-------
direction : numpy array of three floats
unit vector of local walk (not cardinal) direction
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 = 150
>>> t, axyz, gxyz, uxyz, rxyz, sample_rate, duration = read_accel_json(input_file, start, device_motion)
>>> ax, ay, az = axyz
>>> from mhealthx.extractors.pyGait import walk_direction_preheel
>>> threshold = 0.5
>>> stride_fraction = 1.0/8.0
>>> order = 4
>>> cutoff = max([1, sample_rate/10])
>>> plot_test = True
>>> direction = walk_direction_preheel(ax, ay, az, t, sample_rate, stride_fraction, threshold, order, cutoff, plot_test)
"""
import numpy as np
from mhealthx.extractors.pyGait import heel_strikes
from mhealthx.signals import compute_interpeak
# Sum of absolute values across accelerometer axes:
data = np.abs(ax) + np.abs(ay) + np.abs(az)
# Find maximum peaks of smoothed data:
plot_test2 = False
dummy, ipeaks_smooth = heel_strikes(data, sample_rate, threshold,
order, cutoff, plot_test2, t)
# Compute number of samples between peaks using the real part of the FFT:
interpeak = compute_interpeak(data, sample_rate)
decel = np.int(np.round(stride_fraction * interpeak))
# Find maximum peaks close to maximum peaks of smoothed data:
ipeaks = []
for ipeak_smooth in ipeaks_smooth:
ipeak = np.argmax(data[ipeak_smooth - decel:ipeak_smooth + decel])
ipeak += ipeak_smooth - decel
ipeaks.append(ipeak)
# Plot peaks and deceleration phase of stride:
if plot_test:
from pylab import plt
if isinstance(t, list):
tplot = np.asarray(t) - t[0]
else:
tplot = np.linspace(0, np.size(ax), np.size(ax))
idecel = [x - decel for x in ipeaks]
plt.plot(tplot, data, 'k-', tplot[ipeaks], data[ipeaks], 'rs')
for id in idecel:
plt.axvline(x=tplot[id])
plt.title('Maximum stride peaks')
plt.show()
# Compute the average vector for each deceleration phase:
vectors = []
for ipeak in ipeaks:
decel_vectors = np.asarray([[ax[i], ay[i], az[i]]
for i in range(ipeak - decel, ipeak)])
vectors.append(np.mean(decel_vectors, axis=0))
# Compute the average deceleration vector and take the opposite direction:
direction = -1 * np.mean(vectors, axis=0)
# Return the unit vector in this direction:
direction /= np.sqrt(direction.dot(direction))
# Plot vectors:
if plot_test:
from mhealthx.utilities import plot_vectors
dx = [x[0] for x in vectors]
dy = [x[1] for x in vectors]
dz = [x[2] for x in vectors]
hx, hy, hz = direction
title = 'Average deceleration vectors + estimated walk direction'
plot_vectors(dx, dy, dz, [hx], [hy], [hz], title)
return direction