im-ethz/flirt
Are you ready to FLIRT with your wearable data?
This tool helps researchers and data scientists working with wearable device data transform raw physiological measurements into meaningful features. It takes data from smartwatches or smart rings, like Empatica E4 archives, and outputs structured features for heart rate variability, electrodermal activity, and accelerometer data. The end-user is typically a researcher or data scientist analyzing health, behavior, or performance based on consumer wearable sensors.
No commits in the last 6 months. Available on PyPI.
Use this if you need to extract and prepare robust, clean features from consumer wearable device data for machine learning or AI models, especially for physiological signals.
Not ideal if you are working with medical-grade physiological recording devices like clinical ECGs or EEGs, which have different data characteristics and processing requirements.
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81
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23
Language
Jupyter Notebook
License
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Last pushed
Mar 28, 2024
Commits (30d)
0
Dependencies
9
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