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LOTUS Software to Process Wearable EmbracePlus Data.

Jack S Fogarty1

  • 1Science of Learning in Education Centre, National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore.

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|December 17, 2024
PubMed
Summary
This summary is machine-generated.

The LOTUS toolkit simplifies processing Empatica EmbracePlus wearable sensor data. This open-source tool allows users to analyze physiological and behavioral data, overcoming programming hurdles for longitudinal studies.

Keywords:
EmbracePlusEmpaticaphysiological signal processingsoftwarewearables

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Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Data Science

Background:

  • The Empatica EmbracePlus is a medical-grade wearable sensor for continuous physiological monitoring.
  • Its raw data is stored in memory-efficient Avro files, requiring programming expertise for analysis.
  • Existing tools lack accessible methods for compiling and analyzing EmbracePlus data over user-defined periods.

Purpose of the Study:

  • To introduce the LOTUS toolkit, an open-source graphical user interface (GUI).
  • To enable users to reconstitute and process Empatica EmbracePlus datasets over selected time intervals.
  • To facilitate signal processing and analysis of longitudinal wearable data.

Main Methods:

  • Development of the LOTUS toolkit, an open-source GUI.
  • Implementation of functionalities to compile raw Avro timeseries data from EmbracePlus.
  • Conversion of compiled data into unified timeseries formats (Excel, Matlab) for easier analysis.

Main Results:

  • The LOTUS toolkit provides an accessible solution for processing Empatica EmbracePlus data.
  • Users can now reconstitute and analyze longitudinal physiological and behavioral data without advanced programming skills.
  • The toolkit supports conversion to standard data formats, enhancing data usability.

Conclusions:

  • The LOTUS toolkit addresses a critical gap in analyzing Empatica EmbracePlus wearable sensor data.
  • It empowers researchers to conduct more comprehensive longitudinal studies on autonomic functions, behavior, and health.
  • Future development aims to include support for Empatica E4 data and advanced feature extraction.