TY - GEN
T1 - BVP signal feature analysis for intelligent user interface
AU - Luo, Simon
AU - Duh, Henry Been Lirn
AU - Zhou, Jianlong
AU - Chen, Fang
N1 - Publisher Copyright:
Copyright © 2017 by the Association for Computing Machinery, Inc. (ACM).
PY - 2017/5/6
Y1 - 2017/5/6
N2 - 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.
AB - 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.
KW - Blood Volume Pulse
KW - Cognitive load
KW - Heart rate
KW - Heart Rate Variability
KW - Intelligent user interface
KW - Signal processing
UR - http://www.scopus.com/inward/record.url?scp=85019596342&partnerID=8YFLogxK
U2 - 10.1145/3027063.3053121
DO - 10.1145/3027063.3053121
M3 - Conference article published in proceeding or book
AN - SCOPUS:85019596342
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 1861
EP - 1868
BT - CHI 2017 Extended Abstracts - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017
Y2 - 6 May 2017 through 11 May 2017
ER -