2016
DOI: 10.1109/access.2016.2580594
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Spectral Matrix Decomposition-Based Motion Artifacts Removal in Multi-Channel PPG Sensor Signals

Abstract: The intelligent wearable heart rate measurement requirement has attracted more and more attention, and the related applications of Internet of Things are emerging. However, under intensive physical exercises, motion artifacts are strong interference sources for wrist-type photoplethysmography (PPG) sensor signals, thus significantly affecting the accurate estimation of heart rate and other physiological parameters. Currently, how to effectively remove the motion artifacts from PPG sensor signals is becoming an… Show more

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Cited by 43 publications
(18 citation statements)
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“…This approach could be combined with more sophisticated routines (e.g. a weighting scheme [38]) to combine input from PIR, radar and image processing to identify a ‘true‘ motion event with even greater accuracy [39]. …”
Section: Discussionmentioning
confidence: 99%
“…This approach could be combined with more sophisticated routines (e.g. a weighting scheme [38]) to combine input from PIR, radar and image processing to identify a ‘true‘ motion event with even greater accuracy [39]. …”
Section: Discussionmentioning
confidence: 99%
“…It further improves the previous results by jointly exploiting the spectral structures of WPPG signal and the accelerometer signals. Motivated by the aforementioned model, Xiong et al [24] presented an approach, denoted by MC-SMD. In MC-SMD, a spectral matrix is constructed by using multi-channel WPPG sensor signals and simultaneous acceleration signals.…”
Section: Introductionmentioning
confidence: 99%
“…WSN is a complex network system and integrates data acquisition, processing and transmission, which consists of sensor module, wireless communication module, data processing module, power supply module and so on [69]. Compared with the traditional communication network, WSN has the advantages of small volume, high flexibility, strong self-organization and so on [1013].…”
Section: Introductionmentioning
confidence: 99%