stepcount

A Python package to estimate step counts from accelerometer data.

The algorithm is tuned for wrist-worn AX3 data collected at 100 Hz, using data from the open-source OxWalk Dataset, making it compatible with the UK Biobank Accelerometer Dataset.

Check out the 5-minute video tutorial to get started: https://www.youtube.com/watch?v=FPb7H-jyRVQ.

Getting started

Prerequisite

• Python 3.8 or greater

  $ python --version  # or python3 --version

• Java 8 (1.8.0) or greater

  $ java -version

Install (Windows)

For Windows users, we recommend running stepcount using the Anaconda Prompt from Miniconda via the following steps:

1. Download Miniconda (light-weight version of Anaconda). Choose Miniconda3 Windows 64-bit.
2. Install. Use the default recommended settings.
3. From the Start menu, search and open the Anaconda Prompt.

$ pip install stepcount

For further information running Anaconda on Windows using virtual environments, see this guide.

Install (Linux)

$ pip install stepcount

Usage

# Process an AX3 file
$ stepcount sample.cwa

# Or an ActiGraph file
$ stepcount sample.gt3x

# Or a GENEActiv file
$ stepcount sample.bin

# Or a CSV file (see data format below)
$ stepcount sample.csv

Output:

Summary
-------
{
    "Filename": "sample.cwa",
    "Filesize(MB)": 65.1,
    "Device": "Axivity",
    "DeviceID": 2278,
    "ReadErrors": 0,
    "SampleRate": 100.0,
    "ReadOK": 1,
    "StartTime": "2013-10-21 10:00:07",
    "EndTime": "2013-10-28 10:00:01",
    "TotalWalking(min)": 655.75,
    "TotalSteps": 43132,
    ...
}

Estimated Daily Steps
---------------------
              steps
time
2013-10-21     5368
2013-10-22     7634
2013-10-23    10009
...

Output: outputs/sample/

Output files

By default, output files will be stored in a folder named after the input file, outputs/{filename}/, created in the current working directory. You can change the output path with the -o flag:

$ stepcount sample.cwa -o /path/to/some/folder/

The following output files are created:

• Info.json Summary info, as shown above.
• Steps.csv Raw time-series of step counts
• HourlySteps.csv Hourly step counts
• DailySteps.csv Daily step counts
• HourlyStepsAdjusted.csv Like HourlySteps but accounting for missing data (see section below).
• DailyStepsAdjusted.csv Like DailySteps but accounting for missing data (see section below).

Crude vs. Adjusted Estimates

Adjusted estimates are provided that account for missing data. Missing values in the time-series are imputed with the mean of the same timepoint of other available days. For adjusted totals and daily statistics, 24h multiples are needed and will be imputed if necessary. Estimates will be NaN where data is still missing after imputation.

Processing CSV files

If a CSV file is provided, it must have the following header: time, x, y, z.

Example:

time,x,y,z
2013-10-21 10:00:08.000,-0.078923,0.396706,0.917759
2013-10-21 10:00:08.010,-0.094370,0.381479,0.933580
2013-10-21 10:00:08.020,-0.094370,0.366252,0.901938
2013-10-21 10:00:08.030,-0.078923,0.411933,0.901938
...

Processing multiple files

Windows

To process multiple files you can create a text file in Notepad which includes one line for each file you wish to process, as shown below for file1.cwa, file2.cwa, and file2.cwa.

Example text file commands.txt:

stepcount file1.cwa &
stepcount file2.cwa &
stepcount file3.cwa 
:END

Once this file is created, run cmd < commands.txt from the terminal.

Linux

Create a file command.sh with:

stepcount file1.cwa
stepcount file2.cwa
stepcount file3.cwa

Then, run bash command.sh from the terminal.

Collating outputs

A utility script is provided to collate outputs from multiple runs:

$ stepcount-collate-outputs outputs/

This will collate all *-Info.json files found in outputs/ and generate a CSV file.

Validation

Validation for this algorithm is presented in a preprint on medRxiv at: https://www.medrxiv.org/content/10.1101/2023.02.20.23285750v1.

Citing our work

When using this tool, please consider citing the works listed in CITATION.md.

Licence

See LICENSE.md.

Acknowledgements

We would like to thank all our code contributors, manuscript co-authors, and research participants for their help in making this work possible.