Wristpy Documentation

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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., de Wael, R. V., Rupprecht, F., d’Antonio-Bertagnolli, J. V., Franco, A., & Kiar, G. (2025). “wristpy: Fast, User-Friendly Python Processing of Wrist-worn Accelerometer Data”, Journal of Open Source Software, 10(114), 8637. https://doi.org/10.21105/joss.08637

Reference