Image Analysis on Fingertip Video To Obtain PPG

Chatterjee, Ayan; Prinz, Andreas

doi:10.13005/bpj/1554

Cited by 14 publications

(9 citation statements)

References 13 publications

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“…Prior work in computer vision to extract heart rate from RGB (red-green-blue) video signals has leveraged manually extracted features in PPG signals from the finger for arrhythmia detection, 16 ballistocardiographic movements from fingertips, 17 red-channel PPG from fingertip videos, 18 and the relationship between RGB channels. 19…”

Section: Methodsmentioning

confidence: 99%

Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms

Bae

Borac

Emre

et al. 2021

Preprint

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Measuring vital signs plays a key role in both patient care and wellness, but can be challenging outside of medical settings due to the lack of specialized equipment. In this study, we prospectively evaluated smartphone camera-based techniques for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. HR was measured by placing the finger over the rear-facing camera, while RR was measured via a video of the participants sitting still in front of the front-facing camera. In the HR study of 95 participants (with a protocol that included both measurements at rest and post exercise), the mean absolute percent error (MAPE) ± standard deviation of the measurement was 1.6% ± 4.3%, which was significantly lower than the pre-specified goal of 5%. No significant differences in the MAPE were present across colorimeter-measured skin-tone subgroups: 1.8% ± 4.5% for very light to intermediate, 1.3% ± 3.3% for tan and brown, and 1.8% ± 4.9% for dark. In the RR study of 50 participants, the mean absolute error (MAE) was 0.78 ± 0.61 breaths/min, which was significantly lower than the pre-specified goal of 3 breath/min. The MAE was low in both healthy participants (0.70 ± 0.67 breaths/min), and participants with chronic respiratory conditions (0.80 ± 0.60 breaths/min). Our results validate that smartphone camera-based techniques can accurately measure HR and RR across a range of pre-defined subgroups.

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Section: Methodsmentioning

confidence: 99%

Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms

Bae

Borac

Emre

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The leading four stories collected from the "Not Equal" summer school workshop [17], are described in the following sub-sections portraying person generated data [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] and its security challenges.…”

Section: Patient Stories and Identified Challengesmentioning

confidence: 99%

Leveraging technology for healthcare and retaining access to personal health data to enhance personal health and well-being

Chatterjee

Shahaab

Gerdes

et al. 2021

Recent Trends in Computational Intelligence Enabled Research

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“…Prior work in computer vision to extract heart rate from RGB (red-green-blue) video signals has leveraged manually extracted features in PPG signals from the nger for arrhythmia detection, 45 ballistocardiographic movements from ngertips, 46 red-channel PPG from ngertip videos, 47 and the relationship between RGB channels. 48 Our method estimates HR by optically measuring the PPG waveform from participants' ngertips and then extracting the dominant frequency.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

Prospective Validation of Smartphone-based Heart Rate and Respiratory Rate Measurement Algorithms

Bae

Borac

Emre

et al. 2021

Preprint

View full text Add to dashboard Cite

Measuring vital signs plays a key role in both patient care and wellness, but can be challenging outside of medical settings due to the lack of specialized equipment. In this study, we prospectively evaluated smartphone camera-based techniques for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. HR was measured by placing the finger over the rear-facing camera, while RR was measured via a video of the participants sitting still in front of the front-facing camera. In the HR study of 95 participants (with a protocol that included both measurements at rest and post exercise), the mean absolute percent error (MAPE) ± standard deviation of the measurement was 1.6% ± 4.3%, which was significantly lower than the pre-specified goal of 5%. No significant differences in the MAPE were present across colorimeter-measured skin-tone subgroups: 1.8% ± 4.5% for very light to intermediate, 1.3% ± 3.3% for tan and brown, and 1.8% ± 4.9% for dark. In the RR study of 50 participants, the mean absolute error (MAE) was 0.78 ± 0.61 breaths/min, which was significantly lower than the pre-specified goal of 3 breath/min. The MAE was low in both healthy participants (0.70 ± 0.67 breaths/min), and participants with chronic respiratory conditions (0.80 ± 0.60 breaths/min). These results validate the accuracy of our smartphone camera-based techniques to measure HR and RR across a range of pre-defined subgroups.

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Image Analysis on Fingertip Video To Obtain PPG

Cited by 14 publications

References 13 publications

Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms

Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms

Leveraging technology for healthcare and retaining access to personal health data to enhance personal health and well-being

Prospective Validation of Smartphone-based Heart Rate and Respiratory Rate Measurement Algorithms

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