wristpy

A Python package for wrist-worn accelerometer data processing.

Welcome to wristpy, a Python library designed for processing and analyzing wrist-worn accelerometer data. This library provides a set of tools for loading sensor information, calibrating raw accelerometer data, calculating physical activity metrics (ENMO derived) and sleep metrics (angle-Z derived), finding non-wear periods, and detecting sleep periods (onset and wakeup times). Additionally, we provide access to other sensor data that may be recorded by the watch, including; temperature, luminosity, capacitive sensing, battery voltage, and all metadata.

Supported formats & devices

The package currently supports the following formats:

Format	Manufacturer	Device	Implementation status
GT3X	Actigraph	wGT3X-BT	✅
BIN	GENEActiv	GENEActiv	✅

Special Note The idle_sleep_mode for Actigraph watches will lead to uneven sampling rates during periods of no motion (read about this here). Consequently, this causes issues when implementing wristpy's non-wear and sleep detection. As of this moment, we fill in the missing acceleration data with the assumption that the watch is perfeclty idle in the face-up position (Acceleration vector = [0, 0, -1]). The data is filled in at the same sampling rate as the raw acceleration data. In the special circumstance when acceleration samples are not evenly spaced, the data is resampled to the highest effective sampling rate to ensure linearly sampled data.

Processing pipeline implementation

The main processing pipeline of the wristpy module can be described as follows:

• Data loading: sensor data is loaded using actfast, and a WatchData object is created to store all sensor data
• Data calibration: A post-manufacturer calibration step can be applied, to ensure that the acceleration sensor is measuring 1g force during periods of no motion. There are three possible options: None, gradient, ggir.
• Data imputation In the special case when dealing with the Actigraph idle_sleep_mode == enabled, the gaps in acceleration are filled in after calibration, to avoid biasing the calibration phase.
• Metrics Calculation: Calculates various metrics on the calibrated data, namely ENMO (euclidean norm , minus one) and angle-Z (angle of acceleration relative to the x-y axis).
• Non-wear detection: We find periods of non-wear based on the acceleration data. Specifically, the standard deviation of the acceleration values in a given time window, along each axis, is used as a threshold to decide wear or not wear.
• Sleep Detection: Using the HDCZ¹ and HSPT² algorithms to analyze changes in arm angle we are able to find periods of sleep. We find the sleep onset-wakeup times for all sleep windows detected.
• Physical activity levels: Using the enmo data (aggreagated into epoch 1 time bins, 5 second default) we compute activity levels into the following categories: inactivity, light activity, moderate activity, vigorous activity. The default threshold values have been chosen based on the values presented in the Hildenbrand 2014 study³.

Installation

Install this package from PyPI via :

pip install wristpy

Quick start

Using Wristpy through the command-line:

Run single files:

wristpy /input/file/path.gt3x -o /save/path/file_name.csv -c gradient

Run entire directories:

wristpy /path/to/files/input_dir -o /path/to/files/output_dir -c gradient -O .csv

Using Wristpy through a python script or notebook:

Running single files:

from wristpy.core import orchestrator

# Define input file path and output location
# Support for saving as .csv and .parquet
input_path = '/path/to/your/file.gt3x'
output_path = '/path/to/save/file_name.csv'

# Run the orchestrator
results = orchestrator.run(
    input=input_path,
    output=output_path,
    calibrator='gradient',  # Choose between 'ggir', 'gradient', or 'none'
)

#Data available in results object
enmo = results.enmo
anglez = results.anglez
physical_activity_levels = results.physical_activity_levels
nonwear_array = results.nonwear_epoch
sleep_windows = results.sleep_windows_epoch

Running entire directories:

from wristpy.core import orchestrator

# Define input file path and output location

input_path = '/path/to/files/input_dir'
output_path = '/path/to/files/output_dir'

# Run the orchestrator
# Specify the output file type, support for saving as .csv and .parquet
results_dict = orchestrator.run(
    input=input_path,
    output=output_path,
    calibrator='gradient',  # Choose between 'ggir', 'gradient', or 'none'
    output_filetype = '.csv'
)


#Data available in dictionry of results.
subject1 = results_dict['subject1']

enmo = subject1.enmo
anglez = subject1.anglez
physical_activity_levels = subject1.physical_activity_levels
nonwear_array = subject1.nonwear_epoch
sleep_windows = subject1.sleep_windows_epoch

References

1. van Hees, V.T., Sabia, S., Jones, S.E. et al. Estimating sleep parameters using an accelerometer without sleep diary. Sci Rep 8, 12975 (2018). https://doi.org/10.1038/s41598-018-31266-z
2. van Hees, V. T., et al. A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer. PLoS One 10, e0142533 (2015). https://doi.org/10.1371/journal.pone.0142533
3. Hildebrand, M., et al. Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. Medicine and Science in Sports and Exercise, 46(9), 1816-1824 (2014). https://doi.org/10.1249/mss.0000000000000289