Efficient Real-Time Camera Based Estimation of Heart Rate and Its Variability

Abstract

Remote photo-plethysmography (rPPG) uses a remotely placed camera to estimating a person's heart rate (HR). Similar to how heart rate can provide useful information about a person's vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine fluctuations in the intervals between heart beats. However, this measure requires temporally locating heart beats with a high degree of precision. We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart rate more accurately, but also extracts the pulse waveform to time heart beats and measure heart rate variability. This method requires no rPPG specific training and is able to operate in real-time. We validate our method on a self-recorded dataset under an idealized lab setting, and show state-of-the-art results on two public dataset with realistic conditions (VicarPPG and PURE).

Biometric Signals Estimation Using Single Photon Camera and Deep Learning

“…As can be observed from performing rPPG using the SPAD camera and the tachogram estimation techniques described in Section 3.3.3, one could retrieve some information on a relatively hard task as remotely estimating the sympathovagal balance. In particular, the best results are achieved at the 550 nm wavelength with a average RMSE of 0.8, which is a state of the art result as reported in [32]. Respiration Rate Lastly, in Table 5, the respiration rate errors are reported.…”

Automated vs. manual pain coding and heart rate estimations based on videos of older adults with and without dementia

“…The remote photoplethysmography (rPPG) module 24 is able to analyze heart rate and heart rate variability from videos by the quantifying the amount of light reflected by the face which relates to cardiac cycles and changes in blood volume based on video captured by a camera. 43,65 Gudi et al 66 tested the rPPG system used in the FaceReader TM and found that repetitive facial movements (i.e., in talking conditions) can affect the performance of the system by compromising accurate face modelling and estimations. However, when validated against ECG measurements in video datasets of participants under various conditions (e.g., resting, walking, post-workout), the rPPG system showed promising results.…”

“… 43 , 65 Gudi et al. 66 tested the rPPG system used in the FaceReader™ and found that repetitive facial movements (i.e., in talking conditions) can affect the performance of the system by compromising accurate face modelling and estimations. However, when validated against ECG measurements in video datasets of participants under various conditions (e.g., resting, walking, post-workout), the rPPG system showed promising results.…”

“…However, when validated against ECG measurements in video datasets of participants under various conditions (e.g., resting, walking, post-workout), the rPPG system showed promising results. 66 FaceReader’s™ heart rate analysis follows 3 phases: calibration (i.e., an 8.5 s period where the signal of the skin is initially sampled and processed), calculation (i.e., pulses over the previous 10 s are calculated and converted to beats per minute; bpm), and post-processing (i.e., processed to improve accuracy) 64 (for a detailed description of the remote PPG algorithm, see Gudi et al. 66 ).…”

“…The artifacts due to motion or changes in light conditions that can lead to erroneous estimates of the heart rate have been analyzed. 66 , 70 As such, a number of algorithms have been proposed to mitigate these artifacts. For example, motion tracking approaches have been proposed to overcome this problem 71 , 72 ; however, moving the face introduces other artifacts due to corresponding changes in light conditions.…”