Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-24219
Publication type: Working paper – expertise – study
Title: Visualization and analysis of wearable health data from COVID-19 patients
Authors: Suter, Susanne
Spinner, Georg
Hoelz, Bianca
Rey, Sofia
Thanabalasingam, Sujeanthraa
Eckstein, Jens
Hirsch, Sven
et. al: No
DOI: 10.21256/zhaw-24219
Extent: 17
Issue Date: 19-Jan-2022
Publisher / Ed. Institution: arXiv
Other identifiers: arXiv:2201.07698
Language: English
Subjects: Wearable vital sign; COVID-19 patient; Visualization
Subject (DDC): 005: Computer programming, programs and data
616: Internal medicine and diseases
Abstract: Effective visualizations were evaluated to reveal relevant health patterns from multi-sensor real-time wearable devices that recorded vital signs from patients admitted to hospital with COVID-19. Furthermore, specific challenges associated with wearable health data visualizations, such as fluctuating data quality resulting from compliance problems, time needed to charge the device and technical problems are described. As a primary use case, we examined the detection and communication of relevant health patterns visible in the vital signs acquired by the technology. Customized heat maps and bar charts were used to specifically highlight medically relevant patterns in vital signs. A survey of two medical doctors, one clinical project manager and seven health data science researchers was conducted to evaluate the visualization methods. From a dataset of 84 hospitalized COVID-19 patients, we extracted one typical COVID-19 patient history and based on the visualizations showcased the health history of two noteworthy patients. The visualizations were shown to be effective, simple and intuitive in deducing the health status of patients. For clinical staff who are time-constrained and responsible for numerous patients, such visualization methods can be an effective tool to enable continuous acquisition and monitoring of patients' health statuses even remotely.
URI: https://arxiv.org/abs/2201.07698
https://digitalcollection.zhaw.ch/handle/11475/24219
License (according to publishing contract): CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Appears in collections:Publikationen Life Sciences und Facility Management

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