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abstract

BVP Feature Signal Analysis for Intelligent User Interface

Authors:

Henry Been-Lirn Duh,

Fang ChenAuthors Info & Claims

CHI EA '17: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems

Pages 1861 - 1868

https://doi.org/10.1145/3027063.3053121

Published: 06 May 2017 Publication History

Abstract

The Blood Volume Pulse (BVP) sensor has been becoming increasingly common in devices such as smart phones and smart watches. These devices often use BVP to monitor the heart rate of an individual. There has been a large amount of research linking the mental and emotional changes with the physiological changes. The BVP sensor measures one of these physiological changes known as Heart Rate Variability (HRV). HRV is known to be closely related to Respiratory Sinus Arrhythmia (RSA) which can be used as a measurement to quantify the activity of the parasympathetic activity. However, the BVP sensor is highly susceptible to noise and therefore BVP signals often contain a large number of artefacts which make it difficult to extract meaningful features from the BVP signals. This paper proposes a new algorithm to filter artefacts from BVP signals. The algorithm is comprised of two stages. The first stage is to detect the corrupt signal using a Short Term Fourier Transform (STFT). The second stage uses Lomb-Scargle Periodogram (LSP) to approximate the Power Spectral Density (PSD) of the BVP signal. The algorithm has shown to be effective in removing artefacts which disrupt the signal for a short period of time. This algorithm provides the capability for BVP signals to be analysed for frequency based features in HRV which traditionally could be done from the cleaner signals from electrocardiogram (ECG) in medical applications.

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Cited By

Fernandez Rojas RHirachan NBrown NWaddington GMurtagh LSeymour BGoecke R(2023)Multimodal physiological sensing for the assessment of acute painFrontiers in Pain Research10.3389/fpain.2023.11502644Online publication date: 19-Jun-2023
https://doi.org/10.3389/fpain.2023.1150264
Chandel RSharma SKaur SSingh SKumar R(2022)Smart watches: A review of evolution in bio-medical sectorMaterials Today: Proceedings10.1016/j.matpr.2021.07.46050(1053-1066)Online publication date: 2022
https://doi.org/10.1016/j.matpr.2021.07.460
Ramírez-Moreno MCarrillo-Tijerina PCandela-Leal MAlanis-Espinosa MTudón-Martínez JRoman-Flores ARamírez-Mendoza RLozoya-Santos J(2021)Evaluation of a Fast Test Based on Biometric Signals to Assess Mental Fatigue at the Workplace—A Pilot StudyInternational Journal of Environmental Research and Public Health10.3390/ijerph18221189118:22(11891)Online publication date: 12-Nov-2021
https://doi.org/10.3390/ijerph182211891
Show More Cited By

Index Terms

BVP Feature Signal Analysis for Intelligent User Interface
1. Human-centered computing

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Published In

cover image ACM Conferences

CHI EA '17: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems

May 2017

3954 pages

ISBN:9781450346566

DOI:10.1145/3027063

General Chairs:
Gloria Mark
University of California Irvine
,
Susan Fussell
Cornell University
,
Program Chairs:
Cliff Lampe
University of Michigan
,
m.c. schraefel
University of Southampton
,
Juan Pablo Hourcade
University of Iowa
,
Caroline Appert
Université Paris-Sud
,
Daniel Wigdor
University of Toronto

Copyright © 2017 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 May 2017

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AOARD

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CHI '17

Sponsor:

SIGCHI

CHI '17: CHI Conference on Human Factors in Computing Systems

May 6 - 11, 2017

Colorado, Denver, USA

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CHI EA '17 Paper Acceptance Rate 1,000 of 5,000 submissions, 20%;

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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CHI 2025

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ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

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Citations

Cited By

Fernandez Rojas RHirachan NBrown NWaddington GMurtagh LSeymour BGoecke R(2023)Multimodal physiological sensing for the assessment of acute painFrontiers in Pain Research10.3389/fpain.2023.11502644Online publication date: 19-Jun-2023
https://doi.org/10.3389/fpain.2023.1150264
Chandel RSharma SKaur SSingh SKumar R(2022)Smart watches: A review of evolution in bio-medical sectorMaterials Today: Proceedings10.1016/j.matpr.2021.07.46050(1053-1066)Online publication date: 2022
https://doi.org/10.1016/j.matpr.2021.07.460
Ramírez-Moreno MCarrillo-Tijerina PCandela-Leal MAlanis-Espinosa MTudón-Martínez JRoman-Flores ARamírez-Mendoza RLozoya-Santos J(2021)Evaluation of a Fast Test Based on Biometric Signals to Assess Mental Fatigue at the Workplace—A Pilot StudyInternational Journal of Environmental Research and Public Health10.3390/ijerph18221189118:22(11891)Online publication date: 12-Nov-2021
https://doi.org/10.3390/ijerph182211891
Zhou JChen F(2021)Towards Humanity-in-the-Loop in AI LifecycleHumanity Driven AI10.1007/978-3-030-72188-6_1(3-13)Online publication date: 2-Dec-2021
https://doi.org/10.1007/978-3-030-72188-6_1
Zhou JHu HLi ZYu KChen F(2019)Physiological Indicators for User Trust in Machine Learning with Influence Enhanced Fact-CheckingMachine Learning and Knowledge Extraction10.1007/978-3-030-29726-8_7(94-113)Online publication date: 23-Aug-2019
https://doi.org/10.1007/978-3-030-29726-8_7
Nogueira DTavares RAbreu PRestivo M(2018)Demonstration: Online Detection of Abnormalities in Blood Pressure Waveform: Bisfiriens and Alternans PulseSmart Industry & Smart Education10.1007/978-3-319-95678-7_60(536-545)Online publication date: 25-Jul-2018
https://doi.org/10.1007/978-3-319-95678-7_60

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