ndx-wearables 0.2.1

Store data from human wearables

ndx-wearables Extension for NWB

Store data from wearable devices in NWB

This extension is designed to help store data collected from a wide variety of wearable devices in a cross-device capable way, and with an eye towards clinical applications. For more details about the extension, see the paper "NDX-Wearables: An NWB Extension for Clinical Neuroscience" (to be presented at NER 2025).

Installation

Create a new Python environment. Python 3.11 works well, python 3.8-3.12 have been tested.

conda create -n <env_name> python=3.11

You should be able to install from pypi using pip install ndx-wearables

If you would like to install and contribute to developing the package, follow the instructions below in Installing in editable mode

Usage

To see how the data stored using this extension looks, visit our example dataset on the EMBER Archive.

You can also generate a local copy of a synthetic datasets by running the relevant examples/ scripts, for instance

python examples/activity_classification.py

Notes on Extension Usage

This extension provides three key classes (WearableTimeSeries, WearableEnumSeries, WearableEvents) to cover:

• WearableTimeSeries for general time series data sampled from a wearable device, such as blood oxygenation level
• WearableEnumSeries for categorical variables over time with pre-defined meanings (for example sleep stages over time)
• WearableEvents for sparse events over long time periods (e.g. exercise events)

In addition to these classes, metadata may be required, including:

• wearable_device is required for classes that link to a device
• algorithm is required for many classes to indicate how the data was derived

The PhysiologicalMeasure class is povided to group together similar physiological measurements (e.g. heart rate variability, motion) from different devices/sources. This could include redundant measurements of the same physiological variable, or multiple measurements which will be fused by later processing to estimate an unknown quantity (e.g. multiple accelerometer measurements).

If these arguments are omitted, instantiating the class will raise an error. You can find working examples in the scripts under examples.

Arguments for ndx-wearables Classes

Class Name	Required Arguments	Optional Arguments
WearableTimeSeries	name, description, data, timestamps, wearable_device, algorithm	comments, resolution, conversion
WearableEnumSeries	name, description, data, timestamps, wearable_device, meanings, algorithm	resolution, conversion, comments
WearableEvents	name, description, columns, wearable_device, algorithm	resolution, conversion, comments

These reflect typical usage in constructors. For full context or updates, refer to the class definitions in src/pynwb/ndx_wearables and usage examples in examples.

Developing the extension

We use a gitflow model for collaborative development. Each feature should be created on a branch that branches off of the develop branch. Draft PRs back into develop should be open for each WIP feature, and marked as ready for review once the feature is complete. Periodic releases will be made from develop into main.

Installing in editable mode

Navigate to the project root cd path/to/ndx-wearables, then install the required dependencies. For developers, use:

pip install -r requirements-dev.txt

Custom extensions are added to the extension spec YAML file by running:

python src/spec/create_extension_spec.py

After running this script, you can verify that the extensions are correctly added to spec/ndx-wearables.extensions.yaml.

Running test code may be done with PyTest using the test files located in src/pynwb/tests.

To use custom extensions outside a PyTest setting, they must be registered by navigating to the directory root and installing the package:

cd path/to/ndx-wearables
pip install -e .

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This extension was created using ndx-template.