Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam

By Ming-Zher Poh, Daniel J. McDuff, Rosalind W. Picard. IEEE Transactions on Biomedical Engineering, 2011

1,464 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/82f348f8f0fbbbf777fcb5089393682cc1d3dcad

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In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

A Novel Algorithm for Remote Photoplethysmography: Spatial Subspace Rotation (2016, methodology): “, PCAbased [3] and ICA-based [4]), which separates the temporal”
Wearable Photoplethysmographic Sensors—Past and Present (2014, background): “The results verify its effectiveness for removing noise [45,46].”
Non-contact video-based pulse rate measurement on a mobile service robot (2014, background): “Since that time, the work done by [11], [12], [13] have had major influence and lead to mobile phone applications mostly based upon the same methods, e.”
Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions (2013, methodology): “Additionally, measurement techniques of both ECG and EDA signals from video data [9], which allow to remove the intrusiveness aspect of the typical”
Remote Heart Rate Measurement from Face Videos under Realistic Situations (2014, methodology): “Recently, some investigations [2, 10, 12, 13] have shown that HR can be measured from face videos under well controlled laboratory conditions.”
Algorithmic Principles of Remote PPG (2017, methodology): “4) ICA (2011) [3], the most famous BSS-based method that has been widely used.”
Unobtrusive Sensing and Wearable Devices for Health Informatics (2014, background): “[26] showed that heart rate and respiration rate can be derived from PPG that was remotely captured from a subject’s face using a simple digital camera.”
Self-Adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions (2016, methodology): “Speciﬁcally we consider the approaches described in [18, 19, 4, 17, 10].”
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (2020, methodology): “The PPG and BCG signals provide complementary information to one another and also contain information about respiration due to respiratory sinus arrhythmia [12].”
RhythmNet: End-to-End Heart Rate Estimation From Face via Spatial-Temporal Representation (2019, methodology): “The baseline methods we use for comparisons are Poh2010 [1], Poh2011 [2], Balakrish-nan2013 [3], Li2014 [5], Haan2013 [4] and Tulyakov2016 [6], and we directly use the results of these methods provided in [5] and [6].”
Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement (2019, methodology): “Four commonly used HRV features [11, 18] are calculated for evaluation, including respiratory frequency (RF) (in Hz), low frequency (LF), high frequency (HF) and LF/HF (in normalized units, n.”
Physiological Parameter Monitoring from Optical Recordings with a Mobile Phone (2011, neutral): “studies using built-in computer cameras at 15 Hz were resampled to 250 Hz to perform HRV analysis [15].”
Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks (2019, methodology): “The first four [4, 10, 14, 22] are earlier methods not involving neural network.”
DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks (2018, methodology): “Independent component analysis (ICA) [24, 25, 19] and principal component analysis (PCA) [37] are two common approaches for combining multiple color or location channels.”