Please use this identifier to cite or link to this item: http://hdl.handle.net/10400.13/2201
Title: PhysioLab - a multivariate physiological computing toolbox for ECG, EMG and EDA signals: a case of study of cardiorespiratory fitness assessment in the elderly population
Author: Muñoz, John Edison
Gouveia, Elvio Rubio
Cameirão, Mónica S.
Bermúdez I Badia, Sergi
Keywords: Cardiorespiratory fitness assessment
Elderly population
.
Faculdade de Ciências Sociais
Issue Date: 2017
Publisher: Springer Verlag
Abstract: The exponential increase of wearable health-tracking technologies offers new possibilities but also poses new challenges in signal processing to enable fitness monitoring through multimodal physiological recordings. Although there are several software tools used for postprocessing in physiological computing applications, limitations in the analysis, incorporating signals from multiple sources, integrating contextual information and providing information visualization tools prevent a widespread use of this technology. To address these issues, we introduce PhysioLab, a multimodal processing Matlab tool for the data analysis of Electromyography (EMG), Electrocardiography (ECG) and Electrodermal Activity (EDA). The software is intendedtofacilitatetheprocessingandcomprehensionofmultimodalphysiologicaldatawiththe aim of assessing fitness in several domains. A unique feature of PhysioLab is that is informed by normative data grouped by age and sex, allowing contextualization of data based on users’ demographics. Besides signal processing, PhysioLab includes a novel approach to multivariable
Peer review: yes
URI: http://hdl.handle.net/10400.13/2201
DOI: 10.1007/s11042-017-5069-z
Appears in Collections:Artigos em revistas internacionais



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