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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-24219
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dc.contributor.authorSuter, Susanne-
dc.contributor.authorSpinner, Georg-
dc.contributor.authorHoelz, Bianca-
dc.contributor.authorRey, Sofia-
dc.contributor.authorThanabalasingam, Sujeanthraa-
dc.contributor.authorEckstein, Jens-
dc.contributor.authorHirsch, Sven-
dc.date.accessioned2022-02-10T15:07:33Z-
dc.date.available2022-02-10T15:07:33Z-
dc.date.issued2022-01-19-
dc.identifier.otherarXiv:2201.07698de_CH
dc.identifier.urihttps://arxiv.org/abs/2201.07698de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/24219-
dc.description.abstractEffective 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.de_CH
dc.format.extent17de_CH
dc.language.isoende_CH
dc.publisherarXivde_CH
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/de_CH
dc.subjectWearable vital signde_CH
dc.subjectCOVID-19 patientde_CH
dc.subjectVisualizationde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.subject.ddc616: Innere Medizin und Krankheitende_CH
dc.titleVisualization and analysis of wearable health data from COVID-19 patientsde_CH
dc.typeWorking Paper – Gutachten – Studiede_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.21256/zhaw-24219-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.webfeedBiomedical Simulationde_CH
zhaw.webfeedMedical Image Analysis & Data Modelingde_CH
zhaw.webfeedDigital Health Labde_CH
zhaw.webfeedHealth Research Hub (LSFM)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Suter, S., Spinner, G., Hoelz, B., Rey, S., Thanabalasingam, S., Eckstein, J., & Hirsch, S. (2022). Visualization and analysis of wearable health data from COVID-19 patients. arXiv. https://doi.org/10.21256/zhaw-24219
Suter, S. et al. (2022) Visualization and analysis of wearable health data from COVID-19 patients. arXiv. Available at: https://doi.org/10.21256/zhaw-24219.
S. Suter et al., “Visualization and analysis of wearable health data from COVID-19 patients,” arXiv, Jan. 2022. doi: 10.21256/zhaw-24219.
SUTER, Susanne, Georg SPINNER, Bianca HOELZ, Sofia REY, Sujeanthraa THANABALASINGAM, Jens ECKSTEIN und Sven HIRSCH, 2022. Visualization and analysis of wearable health data from COVID-19 patients [online]. arXiv. Verfügbar unter: https://arxiv.org/abs/2201.07698
Suter, Susanne, Georg Spinner, Bianca Hoelz, Sofia Rey, Sujeanthraa Thanabalasingam, Jens Eckstein, and Sven Hirsch. 2022. “Visualization and Analysis of Wearable Health Data from COVID-19 Patients.” arXiv. https://doi.org/10.21256/zhaw-24219.
Suter, Susanne, et al. Visualization and Analysis of Wearable Health Data from COVID-19 Patients. arXiv, 19 Jan. 2022, https://doi.org/10.21256/zhaw-24219.


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