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Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes

Ke Liu, Jiankai Tang, Zhang Jiang, Yuntao Wang, Xiaojing Liu, Dong Li, Yuanchun Shi

TL;DR

The SUMS dataset is introduced, designed to validate video vitals estimation algorithms and compare facial rPPG with finger cPPG, and suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO 2 estimation.

Abstract

Video photoplethysmography (vPPG) is an emerging method for non-invasive and convenient measurement of physiological signals, utilizing two primary approaches: remote video PPG (rPPG) and contact video PPG (cPPG). Monitoring vitals in high-altitude environments, where heart rates tend to increase and blood oxygen levels often decrease, presents significant challenges. To address these issues, we introduce the SUMS dataset comprising 80 synchronized non-contact facial and contact finger videos from 10 subjects during exercise and oxygen recovery scenarios, capturing PPG, respiration rate (RR), and SpO2. This dataset is designed to validate video vitals estimation algorithms and compare facial rPPG with finger cPPG. Additionally, fusing videos from different positions (i.e., face and finger) reduces the mean absolute error (MAE) of SpO2 predictions by 7.6\% and 10.6\% compared to only face and only finger, respectively. In cross-subject evaluation, we achieve an MAE of less than 0.5 BPM for HR estimation and 2.5\% for SpO2 estimation, demonstrating the precision of our multi-camera fusion techniques. Our findings suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO2 estimation by 17.8\%.

Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes

TL;DR

The SUMS dataset is introduced, designed to validate video vitals estimation algorithms and compare facial rPPG with finger cPPG, and suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO 2 estimation.

Abstract

Video photoplethysmography (vPPG) is an emerging method for non-invasive and convenient measurement of physiological signals, utilizing two primary approaches: remote video PPG (rPPG) and contact video PPG (cPPG). Monitoring vitals in high-altitude environments, where heart rates tend to increase and blood oxygen levels often decrease, presents significant challenges. To address these issues, we introduce the SUMS dataset comprising 80 synchronized non-contact facial and contact finger videos from 10 subjects during exercise and oxygen recovery scenarios, capturing PPG, respiration rate (RR), and SpO2. This dataset is designed to validate video vitals estimation algorithms and compare facial rPPG with finger cPPG. Additionally, fusing videos from different positions (i.e., face and finger) reduces the mean absolute error (MAE) of SpO2 predictions by 7.6\% and 10.6\% compared to only face and only finger, respectively. In cross-subject evaluation, we achieve an MAE of less than 0.5 BPM for HR estimation and 2.5\% for SpO2 estimation, demonstrating the precision of our multi-camera fusion techniques. Our findings suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO2 estimation by 17.8\%.
Paper Structure (22 sections, 1 equation, 5 figures, 3 tables)

This paper contains 22 sections, 1 equation, 5 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. Related Works
  3. Physiological Sensing in Plateau
  4. Physiological Sensing Dataset
  5. Physiological Sensing Model Training
  6. Dataset
  7. Data Collection
  8. Data Processing
  9. Physiological Signal Distribution
  10. Method
  11. Input embedding
  12. Neural Network Model
  13. Joint-Training
  14. Result
  15. Remote Heart Rate Sensing
  16. ...and 7 more sections

Figures (5)

  • Figure 1: A visual illustration of our data collection protocol. Participants have different activities (exercise, rest, oxygen inhalation) between stations (yellow, magenta, blue, and orange color).
  • Figure 2: The experimental setup of data collection while participants are having oxygen inhalation.
  • Figure 3: Physiological indicators of the same subject under four states. (a) BVP (b) SpO2 (c) HR (d) RR. The duration of the signal is noted in the bottom of the figure.
  • Figure 4: HR and SPO2 distribution statistics of all samples.
  • Figure 5: MultiPhysNet Model with Input frames of face and finger. PPG and SpO2 estimation tasks are trained simultaneously with combined loss.