sit2standpy 1.1.1.post4

Sit-to-stand detection using a single lumbar-mounted accelerometer.

Sit2StandPy

Sit2StandPy is an open source Python package that uses novel algorithms to first detect Sit-to-Stand transitions from lumbar-mounted accelerometer data, and then provide quantitative metrics assessing the performance of the transitions. A modular framework is employed that would allow for easy modification of parts of the algorithm to suit other specific requirements, while still keeping core elements of the algorithm intact. As gyroscopes impose a significant detriment to battery life due to power consumption, Sit2StandPy's use of acceleration only allows for a single sensor to collect days worth of analyzable data.

Documentation

Full documentation is available, containing API references, installation instructions, and usage examples.

Requirements

• Python >= 3.7
• Numpy
• pandas
• Scipy
• pywavelets
• udatetime

To run the tests, additionally the following are needed

• pytest
• h5py

Installation

Run in the command line/terminal:

pip install sit2standpy

pip will automatically collect and install the required packages by default. If you do not want this behavior, run

pip install sit2standpy --no-deps

Testing

Automated tests can be run with pytest through the terminal:

pytest --pyargs sit2standpy.tests -v

To run the v2 interface tests:

pytest --pyargs sit2standpy.v2.tests -v

V2 Interface

Starting with version 1.1.0 a new "v2" interface is available alongside the old interface. Following a sequential pipeline layout, a basic usage example is:

import sit2standpy as s2s

# transform the data into the appropriate format for H5 or dictionary
data = data_transform_function(acceleration_data)

sequence = s2s.v2.Sequential()
sequence.add(WindowDays(hours=[8, 20]))  # window the data into days using only the hours from 8:00 to 20:00
sequence.add(AccelerationFilter())  # Do the initial filtering and processing required
sequence.add(Detector(stillness_constraint=True))  # Detect the transitions using the stillness constraint

sequence.predict(data)  # predict and save the results into data

s2s.v2.tabulate_results(data, path_to_csv_output, method='stillness')  # tabulate the results to a csv for easy reading

Old Usage

Basic use is accomplished through the Sit2Stand object:

import sit2standpy as s2s
import numpy as np  # importing sample data
from sys import version_info
if version_info < (3, 7):
    from pkg_resources import resource_filename
else:
    from importlib import resources

# locate the sample data and load it (depending on python version)
if version_info < (3, 7):
    file_path = resource_filename('sit2standpy', 'data/sample.csv')
    data = np.loadtxt(file_path, delimiter=',')
else:
    with resources.path('sit2standpy', 'sample.csv') as file_path:
        data = np.loadtxt(file_path, delimiter=',')

# separate the stored sample data
time = data[:, 0]
accel = data[:, 1:]

# initialize the framework for detection
ths = {'stand displacement': 0.125, 'transition velocity': 0.3, 'accel moving avg': 0.15,
                   'accel moving std': 0.1, 'jerk moving avg': 2.5, 'jerk moving std': 3}
sts = s2s.Sit2Stand(method='stillness', gravity=9.84, thresholds=ths, long_still=0.3, still_window=0.3,
                    duration_factor=4, displacement_factor=0.6, lmin_kwargs={'height': -9.5}, power_band=[0, 0.5],
                    power_peak_kwargs={'distance': 128}, power_stdev_height=True)

# run the sit-to-stand detection
SiSt = sts.apply(accel, time, time_units='us')

# print the list of Transition objects, stored as a dictionary with the time they occurred
print(SiSt)

sit_to_stands is then a dictionary of Transition objects containing information about each of the transitions detected

Contributing

Contributions are welcome. Please see the contributions document for more information