Abstract
Due to the technological evolution on wearable devices, biosignals, such as inter-cardiac beat interval (RR) time series, are being captured in a non-controlled environment. These RR signals, derived from photoplethysmography (PPG), enable health status assessment in a more continuous, non-invasive, non-obstructive way, and fully integrated into the individual’s daily activity. However, PPG is vulnerable to motion artefacts, which can affect the accuracy of the estimated neurophysiological markers. This paper introduces a method for motion artefact characterization in terms of location and relative variation parameters obtained in different common daily activities. The approach takes into consideration interindividual variability. Data was analyzed throughout related-samples Friedman’s test, followed by pairwise comparison with Wilcoxon signed-rank tests with a Bonferroni correction. Results showed that movement, involving only arms, presents more variability in terms of the two analyzed parameters.
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Acknowledgements
This work was supported by the European Regional Development Fund through the programme COMPETE by FCT (Portugal) in the scope of the project PEst-UID/CEC/00027/2015 and QVida+: Estimação Contínua de Qualidade de Vida para Auxílio Eficaz à Decisão Clínica, NORTE010247FEDER003446, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement. It was partially supported by LIACC (FCT/UID/CEC/0027/2020).
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Oliveira, A. et al. (2020). Assessing Daily Activities Using a PPG Sensor Embedded in a Wristband-Type Activity Tracker. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1161. Springer, Cham. https://doi.org/10.1007/978-3-030-45697-9_11
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DOI: https://doi.org/10.1007/978-3-030-45697-9_11
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