Assessing Daily Activities Using a PPG Sensor Embedded in a Wristband-Type Activity Tracker

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Alexandra Oliveira (Author) Other; Joyce Aguiar (Author) Other; Eliana Silva (Author) FEUP; Brígida Mónica Faria (Author) Other; Helena Gonçalves (Author) Other; Luís Teófilo (Author) Other; Joaquim Gonçalves (Author) Other; Victor Carvalho (Author) Other; Henrique Lopes Cardoso (Author) FEUP; Luís Paulo Reis (Author) FEUP

Conference proceedings International

Title: WorldCIST (3)

Pages: 108-119

8th World Conference on Information Systems and Technologies, WorldCIST 2020

7 April 2020 through 10 April 2020

Indexing

Scopus - 2 Citations

INSPEC

Other information

Authenticus ID: P-00S-403

DOI: 10.1007/978-3-030-45697-9_11

Abstract (EN): 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. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12