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PhysioLab - a multivariate physiological computing toolbox for ECG, EMG and EDA signals: a case of study of cardiorespiratory fitness assessment in the elderly population

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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 post-processing 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 intended to facilitate the processing and comprehension of multimodal physiological data with the 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 data visualization with the aim of simplifying interpretation by non-experts users. The system computes a set of ECG features based on heart rate variability analysis, EMG parameters to quantify force and fatigue levels, and galvanic skin level/responses from EDA signals. Furthermore, PhysioLab provides compatibility with data from multiple low-cost wearable sensors. We conducted an experiment with 17 community-dwelling older adults (64.5 ± 6.4) to assess the feasibility of the tool in characterizing cardiorespiratory profiles during physical activity. Correlation analyses and regression models showed significant interactions between physiology and fitness evaluations. Our results suggest novel ways that physiological parameters could be effectively used to complement traditional fitness assessment.

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  1. http://www.bitalino.com/

  2. http://biosignalsplux.com

  3. https://www.cooking-hacks.com/ehealth-sensors-complete-kit-biometric-medical-arduino-raspberry-pi

  4. https://www.myo.com/

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Acknowledgements

This work is supported by the Portuguese Foundation for Science and Technology through the Augmented Human Assistance project (CMUPERI/HCI/0046/2013), Projeto Estratégico LA 9 - UID/EEA/50009/2013 and ARDITI (Agência Regional para o Desenvolvimento da Investigação, Tecnologia e Inovação) institution.

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Authors and Affiliations

  1. Madeira interactive Technologies Institute (M-iti), Caminho da Penteada, 9020-105, Funchal, Portugal

    John Edison Muñoz, Elvio Rubio Gouveia, Mónica S. Cameirão & Sergi Bermúdez i Badia

  2. Faculdade de Ciências Exatas e da Engenharia, Universidade da Madeira, Caminho da Penteada, 9020-105, Funchal, Portugal

    John Edison Muñoz, Mónica S. Cameirão & Sergi Bermúdez i Badia

  3. Faculdade de Ciências Sociais, Universidade da Madeira, Caminho da Penteada, 9020-105, Funchal, Portugal

    Elvio Rubio Gouveia

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  1. John Edison Muñoz
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Muñoz, J.E., Gouveia, E.R., Cameirão, M.S. et al. PhysioLab - a multivariate physiological computing toolbox for ECG, EMG and EDA signals: a case of study of cardiorespiratory fitness assessment in the elderly population. Multimed Tools Appl 77, 11521–11546 (2018). https://doi.org/10.1007/s11042-017-5069-z

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