Wristpy Documentation

Welcome to wristpy, a Python library for processing and analyzing wrist-worn accelerometer data.

Wristpy provides tools for:

• Loading and calibrating raw accelerometer data

• Computing physical activity levels using several established metrics (ENMO, MAD, Activity- Counts, MIMS)

• Detecting non-wear periods and sleep onset/wake times

• Accessing additional sensor streams (temperature, luminosity, capacitive sense, battery, metadata)

For installation instructions and a quick introduction, see Getting Started.

How To Cite

If you use wristpy in your research, please cite the following paper:

Santorelli A, Perez F, Vos de Wael R, Rupprecht F, d’Antonio-Bertagnolli JV, Franco A, Kiar G. wristpy: Fast, User-Friendly Python Processing of Wrist-worn Accelerometer Data. 2025. Under review. DOI: https://doi.org/10.5281/zenodo.13883190

Reference

• GitHub Repository: childmindresearch/wristpy

• Issues & Bug Reports: GitHub Issues

• Contributing: See Development Guide