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Article

LOTUS Software to Process Wearable EmbracePlus Data

Science of Learning in Education Centre, National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore
Sensors 2024, 24(23), 7462; https://doi.org/10.3390/s24237462
Submission received: 11 October 2024 / Revised: 15 November 2024 / Accepted: 20 November 2024 / Published: 22 November 2024
(This article belongs to the Special Issue Wearable Sensors for Behavioral and Physiological Monitoring)

Abstract

The Empatica EmbracePlus is a recent innovation in medical-grade wristband wearable sensors that enable unobtrusive continuous measurement of pulse rate, electrodermal activity, skin temperature, and various accelerometry-based actigraphy measures using a minimalistic smartwatch design. The advantage of this lightweight wearable is the potential for holistic longitudinal recording and monitoring of physiological processes that index a suite of autonomic functions, as well as to provide ecologically valid insights into human behaviour, health, physical activity, and psychophysiological processes. Given the longitudinal nature of wearable recordings, EmbracePlus data collection is managed by storing raw timeseries in short ‘chunks’ in avro file format organised by universal standard time. This is memory-efficient but requires programming expertise to compile the raw data into continuous file formats that can be processed using standard techniques. Currently, there are no accessible tools available to compile and analyse raw EmbracePlus data over user-defined time periods. To address that, we introduce the LOTUS toolkit, an open-source graphical user interface that allows users to reconstitute and process EmbracePlus datasets over select time intervals. LOTUS is available on GitHub, and currently allows users to compile raw EmbracePlus data into unified timeseries stored in more familiar Excel or Matlab file formats to facilitate signal processing and analysis. Future work will expand the toolkit to process Empatica E4 and other wearable signal data, while also integrating more sophisticated functions for feature extraction and analysis.
Keywords: Empatica; EmbracePlus; wearables; physiological signal processing; software Empatica; EmbracePlus; wearables; physiological signal processing; software

Share and Cite

MDPI and ACS Style

Fogarty, J.S. LOTUS Software to Process Wearable EmbracePlus Data. Sensors 2024, 24, 7462. https://doi.org/10.3390/s24237462

AMA Style

Fogarty JS. LOTUS Software to Process Wearable EmbracePlus Data. Sensors. 2024; 24(23):7462. https://doi.org/10.3390/s24237462

Chicago/Turabian Style

Fogarty, Jack S. 2024. "LOTUS Software to Process Wearable EmbracePlus Data" Sensors 24, no. 23: 7462. https://doi.org/10.3390/s24237462

APA Style

Fogarty, J. S. (2024). LOTUS Software to Process Wearable EmbracePlus Data. Sensors, 24(23), 7462. https://doi.org/10.3390/s24237462

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