actipy

A Python package to process accelerometer data.

Axivity3 (.cwa), Actigraph (.gt3x), and GENEActiv (.bin) files are supported, as well as custom CSV files.

Axivity3 is the activity tracker watch used in the large-scale UK-Biobank accelerometer study.

Getting started

Prerequisite

• Python 3.8 or greater

  $ python --version  # or python3 --version

• Java 8 (1.8.0) or greater

  $ java -version

Install

$ pip install actipy

Usage

Process an Axivity3 (.cwa) file:

import actipy

data, info = actipy.read_device("sample.cwa.gz",
                                 lowpass_hz=20,
                                 calibrate_gravity=True,
                                 detect_nonwear=True,
                                 resample_hz=50)

Output:

data [pandas.DataFrame]
                                 x         y         z  temperature
 time
 2014-05-07 13:29:50.430 -0.513936  0.070043  1.671264    20.000000
 2014-05-07 13:29:50.440 -0.233910 -0.586894  0.081946    20.000000
 2014-05-07 13:29:50.450 -0.080303 -0.951132 -0.810433    20.000000
 2014-05-07 13:29:50.460 -0.067221 -0.976200 -0.864934    20.000000
 2014-05-07 13:29:50.470 -0.109617 -0.857322 -0.508587    20.000000
 ...                           ...       ...       ...          ...

info [dict]
 Filename                 : data/sample.cwa.gz
 Filesize(MB)             : 69.4
 Device                   : Axivity
 DeviceID                 : 13110
 ReadErrors               : 0
 SampleRate               : 100.0
 ReadOK                   : 1
 StartTime                : 2014-05-07 13:29:50
 EndTime                  : 2014-05-13 09:50:33
 NumTicks                 : 51391800
 WearTime(days)           : 5.847725231481482
 NumInterrupts            : 1
 ResampleRate             : 100.0
 NumTicksAfterResample    : 25262174
 LowpassOK                : 1
 LowpassCutoff(Hz)        : 20.0
 CalibErrorBefore(mg)     : 82.95806873592024
 CalibErrorAfter(mg)      : 4.434966371604519
 CalibOK                  : 1
 NonwearTime(days)        : 0.0
 NumNonwearEpisodes       : 0
 ...

Refer to the Data Dictionary for a comprehensive list of outputs.

If you have a CSV file that you want to process, you can also use the data processing routines from actipy.processing:

import actipy.processing as P

data, info_lowpass = P.lowpass(data, 100, 20)
data, info_calib = P.calibrate_gravity(data)
data, info_nonwear = P.detect_nonwear(data)
data, info_resample = P.resample(data, sample_rate)

See the documentation for more.

Contributing

If you would like to contribute to this repository, please check out CONTRIBUTING.md. We welcome contributions in the form of bug reports, feature requests, and pull requests.

License

See LICENSE.md.