Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.13/2201
Título: PhysioLab - a multivariate physiological computing toolbox for ECG, EMG and EDA signals: a case of study of cardiorespiratory fitness assessment in the elderly population
Autor: Muñoz, John Edison
Gouveia, Elvio Rubio
Cameirão, Mónica S.
Bermúdez I Badia, Sergi
Palavras-chave: Cardiorespiratory fitness assessment
Elderly population
Faculdade de Ciências Sociais
Data: 2017
Editora: Springer Verlag
Resumo: 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
Aparece nas colecções:Artigos em revistas internacionais

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