Are you ready to FLIRT with your wearable data?
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Updated
Mar 28, 2024 - Jupyter Notebook
Are you ready to FLIRT with your wearable data?
Multimodal human activity recognition using wrist-worn wearable sensors.
Pre-processing methods for mHealth and wearables data.
E4 TimeStamper helps researchers to automatically add timestamps to physiological data derived from Empatica's E4 wristbands.
Estimation of resting heart rate.
Custom client for the E4 wristband from Empatica
Python wrapper for Empatica TCP client, allowing bidirectional communication with an Empatica E4.
wearablecompute is an open source Python package containing over 50 data and domain-driven features that can be computed from wearables and mHealth sensor data.
The LOTUS Reader is a GUI primarily built to read and compile raw Empatica EmbracePlus data over user-defined periods of time. Fragmented 'chunks' of raw timeseries data output by Empatica (i.e., EDA, BVP, systolic peaks, temperature, accelerometer, and event tags) can be selectively reconstituted as continuous timeseries for further processing.
Visualize your raw data from .avro files for the EmbracePlus device from Empatica
Charts for physiological data gathered from Empatica E4 wristband
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