wristpy Documentation¶
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 various physical activity metrics, 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.
Quick Start¶
Installation
Note
macOS users: Install libomp first via brew install libomp
Install wristpy from PyPI:
pip install wristpy
Basic Usage
Single file processing:
from wristpy.core import orchestrator
results = orchestrator.run(
input='/path/to/your/file.gt3x',
output='/path/to/save/file_name.csv',
calibrator='gradient'
)
For detailed examples and usage patterns, see the Wristpy Tutorial.
Supported Devices & Formats¶
wristpy currently supports:
Format |
Manufacturer |
Device |
Status |
|---|---|---|---|
GT3X |
Actigraph |
wGT3X-BT |
✅ Supported |
BIN |
GENEActiv |
GENEActiv |
✅ Supported |
Processing Pipeline¶
The wristpy processing pipeline includes:
Data Loading - Sensor data loaded via actfast
Data Calibration - Post-manufacturer calibration (None, gradient, ggir options)
Metrics Calculation - ENMO, MAD, Actigraph counts, MIMS, angle-Z
Non-wear Detection - Multiple algorithms including CTA and DETACH
Sleep Detection - HDCZ and HSPT algorithms for sleep onset/wakeup detection
Physical Activity Levels - Classification into inactive, light, moderate, vigorous categories
For complete details, see the Wristpy Tutorial.
Getting Help¶
GitHub Repository: childmindresearch/wristpy
Issues & Bug Reports: GitHub Issues
Contributing: See our Development Guide guide
Documentation Contents¶
User Guide
API Reference
Development