Package: gaitpy
GaitPyRead and process raw vertical accelerometry data from a sensor on the lower back during gait; calculate clinical gait characteristics.OverviewGaitPy provides python functions to read accelerometry data from a single lumbar-mounted sensor and estimate clinical
characteristics of gait.Device location: lower back/lumbarSensing modality: AccelerometerSensor data: Vertical accelerationMinimum sampling rate: 50HzDOCUMENTATIONCOMMUNITY GUIDELINESInstallationGaitPy is compatible with python v3.6 on MacOSX, Windows, and Linux and is available throughAnaconda.Installation via Anaconda (recommended):Once you have Anaconda installed, open a terminal window and create a new environment using the following command.condacreate--namemy_envpython=3.6Then, activate your environment using the following command for Mac and Linux.sourceactivatemy_envFor Windows use the following.activatemy_envLastly, to install GaitPy, use the following command.condainstallgaitpyAlternatively, you can install via pip:pipinstallgaitpyYou can also install GaitPy from source:gitclonehttps://github.com/matt002/gaitpycd~/gaitpy
pythonsetup.pyinstallBasic usageGaitPy consists of the following 3 functions:classify_bouts: If your data consists of gait and non-gait data, run the classify_bouts function to first
classify bouts of gait. If your data is solely during gait, this function is not necessary to use.extract_features: Extract initial contact (IC) and final contact (FC) events from your data and estimate
various temporal and spatial gait features.plot_contacts: Plot the resulting bout detections and IC/FC events alongside your raw accelerometer signal.GaitPy accepts a csv file or pandas dataframe that includes a column containing unix timestamps and a column containing
vertical acceleration from a lumbar-mounted sensor. GaitPy makes three assumptions by default:Timestamps and vertical acceleration columns are labeled 'timestamps' and 'y' respectively, however
this can be changed using the 'v_acc_col_name' and 'ts_col_name' arguments respectively.Timestamps are in Unix milliseconds and data is in meters per second squared, however this can be be changed
using the 'ts_units' and 'v_acc_units' arguments respectively.Baseline vertical acceleration data is -9.8m/s^2 or -1g. If your baseline data is +9.8m/s^2 or +1g, set the 'flip'
argument to True.Additionally, the sample rate of your device (at least 50Hz) and height of the subject must be provided.More details about the inputs and ouputs of each of these functions can be found in thedocumentationand Czech et al. 2019.fromgaitpy.gaitimportGaitpyraw_data='raw-data-path or pandas dataframe'sample_rate=128# hertzsubject_height=170# centimeters#### Create an instance of Gaitpy ####gaitpy=Gaitpy(raw_data,# Raw data consisting of vertical acceleration from lumbar location and unix timestampssample_rate,# Sample rate of raw data (in Hertz)v_acc_col_name='y',# Vertical acceleration column namets_col_name='timestamps',# Timestamp column namev_acc_units='m/s^2',# Units of vertical accelerationts_units='ms',# Units of timestampsflip=False)# If baseline data is at +1g or +9.8m/s^2, set flip=True#### Classify bouts of gait - Optional (use if your data consists of gait and non-gait periods)####gait_bouts=gaitpy.classify_bouts(result_file='/my/folder/classified_gait.h5')# File to save results to (None by default)#### Extract gait characteristics ####gait_features=gaitpy.extract_features(subject_height,# Subject heightsubject_height_units='centimeter',# Units of subject heightresult_file='/my/folder/gait_features.csv',# File to save results to (None by default)classified_gait=gait_bouts)# Pandas Dataframe or .h5 file results of classify_bouts function (None by default)#### Plot results of gait feature extraction ####gaitpy.plot_contacts(gait_features,# Pandas Dataframe or .csv file results of extract_features functionresult_file='/my/folder/plot_contacts.html)',# File to save results to (None by default)show_plot=True)# Specify whether to display plot upon completion (True by default)Running the demoThe demo file provided lets you to test whether GaitPy outputs the expected results on your system.You may run the demo directly from a terminal window:cdgaitpy/demo
pythondemo.pyYou may also run the demo via a python interpreter. In a terminal window start python by typing:pythonIn the interpreter window you can then import and run the demo with the following two commands:fromgaitpy.demoimportdemo
demo.run_demo()The demo script will prompt you to type in a results directory. Following the run, results will be saved in the provided
results directory (less than 250kB of data will be saved). Running the demo should take less than a minute, though this
may vary depending on your machine.Contributing to the projectPlease help contribute to the project! See theCONTRIBUTING.mdfile for details.AcknowledgementsThe Digital Medicine & Translational Imaging group at Pfizer, Inc supported the development of this package.AuthorMatthew CzechLicenseGaitPy is under the MIT license