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. 2025 Jun 20;16(7):2860-2874.
doi: 10.1364/BOE.543645. eCollection 2025 Jul 1.

Non-contact imaging of the neck for monitoring cardiopulmonary clinic patients

Mohamad H Davoodabadi Farahani  1 , Miodrag Bolic  1 , Tianyu Zhang  1 , Terry Zadorsky  2 , Roland Sabbagh  2
Affiliations

Non-contact imaging of the neck for monitoring cardiopulmonary clinic patients

Mohamad H Davoodabadi Farahani et al. Biomed Opt Express. .

Abstract

Non-contact neck imaging using a standard smartphone camera and ambient lighting with accurate estimates of heart rate (HR) and respiratory rate (RR) provides superior access to care in resource constrained health care systems. While most existing techniques for non-contact imaging focus on detecting HR from facial regions, limited work has explored HR and RR detection from the neck. Prior studies involve healthy volunteers while relying on specialized cameras or controlled lighting conditions. Data acquired for the study were collected in a cardiology clinic under regular fluorescent lighting, with the participants being assessed for cardiac pathology. The proposed approach uses 15 to 30-second videos to analyze the skin displacement motion of neck pixels. The results demonstrate superior performance with R-values of 0.98 and 0.85 for HR and RR respectively compared to existing imaging photoplethysmography (iPPG) algorithms, highlighting the robustness of this method. The outcomes of this work could facilitate the detection of carotid artery and jugular venous pulsation, providing a more comprehensive assessment of cardiovascular and respiratory health, particularly for patient monitoring.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Fig. 1.
Fig. 1.
Distribution of the cardiac disorders between participants (Left), and Heartbeat Rhythms (Right).
Fig. 2.
Fig. 2.
Two-view video acquisition.
Fig. 3.
Fig. 3.
The experimental setup for data acquisition.
Fig. 4.
Fig. 4.
Workflow of the proposed approach.
Fig. 5.
Fig. 5.
Illustration of majority voting using blocks for determining the Heart rate (Upper) and Respiratory rate (Lower).
Fig. 6.
Fig. 6.
Comparison between the sMOT and the corresponding iPPG in time and frequency domain for 8 subjects.
Fig. 7.
Fig. 7.
Direct view result for sMOT-based HR. 45-degree patient’s position (1st row) and sitting (2nd row). Tangential view result for sMOT-based HR. 45-degree patient’s position (3rd row) and sitting (4th row).
Fig. 8.
Fig. 8.
Direct view result for sMOT-based RR. 45-degree patient’s position (1st row) and sitting (2nd row). Tangential view result for sMOT-based RR. 45-degree patient’s position (3rd row) and sitting (4th row).
Fig. 9.
Fig. 9.
Comparison between the Reference value and the estimated values in different camera angles and patient positions for HR(Upper) and RR(Lower).
Fig. 10.
Fig. 10.
ECG (Left) and Finger PPG (Right) of Three patients with PVCs (Upper Row) and Afib (Middle Row) and NSR (Bottom Row).
See this image and copyright information in PMC

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