OxWearables/actinet
An activity classification model based on self-supervised learning for wrist-worn accelerometer data.
This tool helps researchers and health professionals analyze data from wrist-worn accelerometers to understand activity patterns. You input raw accelerometer data files (e.g., from AX3, ActiGraph, or GENEActiv devices), and it provides detailed time-series data and summary metrics, revealing how much time a person spends sleeping, being sedentary, or engaging in physical activity. This is ideal for public health researchers, sports scientists, or clinicians studying human movement.
Available on PyPI.
Use this if you need to transform raw accelerometer data from wearable devices into meaningful insights about sleep, sedentary behavior, and physical activity.
Not ideal if you need real-time activity monitoring or custom machine learning model development for very specific, niche activities not covered by general classifications.
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12
Forks
3
Language
Python
License
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Category
Last pushed
Mar 09, 2026
Commits (30d)
0
Dependencies
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