Calibration of Contactless Pulse Oximetry.

BACKGROUND: Contactless, camera-based photoplethysmography (PPG) interrogates shallower skin layers than conventional contact probes, either transmissive or reflective. This raises questions on the calibratability of camera-based pulse oximetry. METHODS: We made video recordings of the foreheads of 41 healthy adults at 660 and 840 nm, and remote PPG signals were extracted. Subjects were in normoxic, hypoxic, and low temperature conditions. Ratio-of-ratios were compared to reference SpO2 from 4 contact probes. RESULTS: A calibration curve based on artifact-free data was determined for a population of 26 individuals. For an SpO2 range of approximately 83% to 100% and discarding short-term errors, a root mean square error of 1.15% was found with an upper 99% one-sided confidence limit of 1.65%. Under normoxic conditions, a decrease in ambient temperature from 23 to 7°C resulted in a calibration error of 0.1% (±1.3%, 99% confidence interval) based on measurements for 3 subjects. PPG signal strengths varied strongly among individuals from about 0.9 × 10 to 4.6 × 10 for the infrared wavelength. CONCLUSIONS: For healthy adults, the results present strong evidence that camera-based contactless pulse oximetry is fundamentally feasible because long-term (eg, 10 minutes) error stemming from variation among individuals expressed as A*rms is significantly lower (<1.65%) than that required by the International Organization for Standardization standard (<4%) with the notion that short-term errors should be added. A first illustration of such errors has been provided with A**rms = 2.54% for 40 individuals, including 6 with dark skin. Low signal strength and subject motion present critical challenges that will have to be addressed to make camera-based pulse oximetry practically feasible.

Camera-based system for contactless monitoring of respiration.

Reliable, remote measurement of respiration rate is still an unmet need in clinical and home settings. Although the predictive power of respiratory rate for a patient's health status is well-known, this vital sign is often measured inaccurately or not at all. In this paper we propose a camera-based monitoring system to reliably measure respiration rate without any body contact. A computationally efficient algorithm to extract raw breathing signals from the video stream has been developed and implemented. Additionally, a camera offers an easy access to motion information in the analyzed scenes, which significantly improves subsequent breath-to-breath classification. The performance of the sensor system was evaluated using data acquired with healthy volunteers, as well as with a mechanical phantom, under laboratory conditions covering a large range of challenging measurement situations.