Name: | Description: | Size: | Format: | |
---|---|---|---|---|
979.56 KB | Adobe PDF |
Advisor(s)
Abstract(s)
The recent rise and popularization of wearable and ubiquitous fitness sensors has increased our ability to generate large amounts of multivariate data for cardiorespiratory fitness (CRF) assessment. Consequently, there is a need to find new methods to visualize and interpret CRF data without overwhelming users. Current visualizations of CRF data are mainly tabular or in the form of stacked univariate plots. Moreover, normative data differs significantly between gender, age and activity, making data interpretation yet more challenging. Here we present a CRF assessment tool based on radar plots that provides a way to represent multivariate cardiorespiratory data from electrocardiographic (ECG) signals within its normative context. To that end, 5 parameters are extracted from raw ECG data using R-peak information: mean HR, SDNN, RMSSD, HRVI and the maximal oxygen uptake, VO2max. Our tool processes ECG data and produces a visualization of the data in a way that it is easy to compare between the performance of the user and normative data. This type of representation can assist both health professionals and non-expert users in the interpretation of CRF data.
Description
Keywords
Cardiorespiratory fitness assessment through ECG (R-Peak) analysis Visualization of multivariate physiological data Multivariate physiological data . Faculdade de Ciências Exatas e da Engenharia
Citation
Muñoz, J. E., Bermúdez i Badia, S., Rubio, E., & Cameirão, M. S. (2015). Visualization of multivariate physiological data for cardiorespiratory fitness assessment through ECG (R-peak) analysis. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 390-393). IEEE.
Publisher
Institute of Electrical and Electronics Engineers