A novel computational signal processing framework towards multimodal vital signs extraction using neck-worn wearable devices
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Author(s)
Abdulsadig, Rawan
Rodriguez Villegas, Esther
Type
Journal Article
Abstract
Pulse rate (PR) and respiratory rate (RR) are two of the most important vital signs. Monitoring them would benefit from
easy-to-use technologies. Hence, wearable devices would, in principle, be ideal candidates for such systems. The neck,
although highly susceptible to artifacts, presents an attractive location for a diverse pool of physiological biomarkers monitoring
purposes such as airflow sensing in a non-obstructive manner. This paper presents a methodology for PR and RR estimation
using photoplethysmography (PPG) and accelerometry (Acc) sensors placed on the neck. Neck PPG and Acc signals were
recorded from 22 healthy participants for RR estimation, where the resting subjects performed guided breathing following a
visual metronome. Neck PPG signals were obtained from 16 healthy participants who breathed through an altitude generator
machine in order to acquire a wider range of PR readings while at rest. The proposed methodology was able to provide
rate estimates via a combination of recursive FFT-based dominance scoring coupled with an exponentially weighted moving
average (EWMA)-driven aggregation scheme. The recursion aimed at bypassing sudden intra-window amplitude deviations
caused by momentary artifacts, while the EWMA-based aggregation was utilized for handling inter-window artifact-induced
deviations. To further improve estimation stability and confidence, estimates were calculated in the form of rate bands taking
into account the relevant clinically acceptable error margins, and results when considering rate values and rate bands are
presented and discussed. The framework was able to achieve an overall pulse rate value accuracy of 93.67±7.64% within the
clinically acceptable ±5 BPM with reference to the gold-standard reference devices while providing an overall respiratory rate
value accuracy within the clinically appropriate ±3 BrPM of 94.94±3.56% with reference to the guiding visual metronome, and
88.4±7.63% with respect to the gold-standard reference device. The proposed methodology achieves acceptable PR and RR
estimation capabilities, even when signals are acquired from an unusual location such as the neck. This work introduces novel
ideas that can lead to the development of medical device outputs for PR and RR monitoring, especially capitalizing on the
advantages of the neck as a multi-modal physiological monitoring location.
easy-to-use technologies. Hence, wearable devices would, in principle, be ideal candidates for such systems. The neck,
although highly susceptible to artifacts, presents an attractive location for a diverse pool of physiological biomarkers monitoring
purposes such as airflow sensing in a non-obstructive manner. This paper presents a methodology for PR and RR estimation
using photoplethysmography (PPG) and accelerometry (Acc) sensors placed on the neck. Neck PPG and Acc signals were
recorded from 22 healthy participants for RR estimation, where the resting subjects performed guided breathing following a
visual metronome. Neck PPG signals were obtained from 16 healthy participants who breathed through an altitude generator
machine in order to acquire a wider range of PR readings while at rest. The proposed methodology was able to provide
rate estimates via a combination of recursive FFT-based dominance scoring coupled with an exponentially weighted moving
average (EWMA)-driven aggregation scheme. The recursion aimed at bypassing sudden intra-window amplitude deviations
caused by momentary artifacts, while the EWMA-based aggregation was utilized for handling inter-window artifact-induced
deviations. To further improve estimation stability and confidence, estimates were calculated in the form of rate bands taking
into account the relevant clinically acceptable error margins, and results when considering rate values and rate bands are
presented and discussed. The framework was able to achieve an overall pulse rate value accuracy of 93.67±7.64% within the
clinically acceptable ±5 BPM with reference to the gold-standard reference devices while providing an overall respiratory rate
value accuracy within the clinically appropriate ±3 BrPM of 94.94±3.56% with reference to the guiding visual metronome, and
88.4±7.63% with respect to the gold-standard reference device. The proposed methodology achieves acceptable PR and RR
estimation capabilities, even when signals are acquired from an unusual location such as the neck. This work introduces novel
ideas that can lead to the development of medical device outputs for PR and RR monitoring, especially capitalizing on the
advantages of the neck as a multi-modal physiological monitoring location.
Date Issued
2024-09-27
Date Acceptance
2024-09-04
Citation
Scientific Reports, 2024, 14
ISSN
2045-2322
Publisher
Nature Portfolio
Journal / Book Title
Scientific Reports
Volume
14
Copyright Statement
© The Author(s) 2024 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.nature.com/articles/s41598-024-72184-7
Publication Status
Published
Article Number
22368
Date Publish Online
2024-09-27