Measurement of Vital Signs Using Lifelight® Remote Photoplethysmography: results of the VISION-D and VISION-V observational studies (Preprint)
Abstract: BACKGROUND Detection of early changes in vital signs (VS) enables timely intervention; however, measurement of VS requires hands-on technical expertise and is often time-consuming. Contactless measurement of VS is beneficial to prevent infection, such as during the COVID-19 pandemic. Lifelight® is a novel software being developed to measure VS by remote photoplethysmography, based on video capture of the face via the integral camera on mobile phones and tablets. We report the observatio… Show more
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Cited by 4 publications
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Measurement of Vital Signs by Lifelight Software in Comparison to Standard of Care Multisite Development (VISION-MD): Protocol for an Observational Study
Background Measuring vital signs (VS) is an important aspect of clinical care but is time-consuming and requires multiple pieces of equipment and trained staff. Interest in the contactless measurement of VS has grown since the COVID-19 pandemic, including in nonclinical situations. Lifelight is an app being developed as a medical device for the contactless measurement of VS using remote photoplethysmography (rPPG) via the camera on smart devices. The VISION-D (Measurement of Vital Signs by Lifelight Software in Comparison to the Standard of Care—Development) and VISION-V (Validation) studies demonstrated the accuracy of Lifelight compared with standard-of-care measurement of blood pressure, pulse rate, and respiratory rate, supporting the certification of Lifelight as a class I Conformité Européenne (CE) medical device. Objective To support further development of the Lifelight app, the observational VISION Multisite Development (VISION-MD) study is collecting high-quality data from a broad range of patients, including those with VS measurements outside the normal healthy range and patients who are critically ill. Methods The study is recruiting adults (aged ≥16 years) who are inpatients (some critically ill), outpatients, and healthy volunteers, aiming to cover a broad range of normal and clinically relevant VS values; there are no exclusion criteria. High-resolution 60-second videos of the face are recorded by the Lifelight app while simultaneously measuring VS using standard-of-care methods (automated sphygmomanometer for blood pressure; finger clip sensor for pulse rate and oxygen saturation; manual counting of respiratory rate). Feedback from patients and nurses who use Lifelight is collected via a questionnaire. Data to estimate the cost-effectiveness of Lifelight compared with standard-of-care VS measurement are also being collected. A new method for rPPG signal processing is currently being developed, based on the identification of small areas of high-quality signals in each individual. Anticipated recruitment is 1950 participants, with the expectation that data from approximately 1700 will be used for software development. Data from 250 participants will be retained to test the performance of Lifelight against predefined performance targets. Results Recruitment began in May 2021 but was hindered by the restrictions instigated during the COVID-19 pandemic. The development of data processing methodology is in progress. The data for analysis will become available from September 2022, and the algorithms will be refined continuously to improve clinical accuracy. The performance of Lifelight compared with that of the standard-of-care measurement of VS will then be tested. Recruitment will resume if further data are required. The analyses are expected to be completed in early 2023. Conclusions This study will support the refinement of data collection and processing toward the development of a robust app that is suitable for routine clinical use. Trial Registration ClinicalTrials.gov NCT04763746; https://clinicaltrials.gov/ct2/show/NCT04763746 International Registered Report Identifier (IRRID) DERR1-10.2196/41533
Measurement of Vital Signs by Lifelight Software in Comparison to Standard of Care Multisite Development (VISION-MD): Protocol for an Observational Study
Background Measuring vital signs (VS) is an important aspect of clinical care but is time-consuming and requires multiple pieces of equipment and trained staff. Interest in the contactless measurement of VS has grown since the COVID-19 pandemic, including in nonclinical situations. Lifelight is an app being developed as a medical device for the contactless measurement of VS using remote photoplethysmography (rPPG) via the camera on smart devices. The VISION-D (Measurement of Vital Signs by Lifelight Software in Comparison to the Standard of Care—Development) and VISION-V (Validation) studies demonstrated the accuracy of Lifelight compared with standard-of-care measurement of blood pressure, pulse rate, and respiratory rate, supporting the certification of Lifelight as a class I Conformité Européenne (CE) medical device. Objective To support further development of the Lifelight app, the observational VISION Multisite Development (VISION-MD) study is collecting high-quality data from a broad range of patients, including those with VS measurements outside the normal healthy range and patients who are critically ill. Methods The study is recruiting adults (aged ≥16 years) who are inpatients (some critically ill), outpatients, and healthy volunteers, aiming to cover a broad range of normal and clinically relevant VS values; there are no exclusion criteria. High-resolution 60-second videos of the face are recorded by the Lifelight app while simultaneously measuring VS using standard-of-care methods (automated sphygmomanometer for blood pressure; finger clip sensor for pulse rate and oxygen saturation; manual counting of respiratory rate). Feedback from patients and nurses who use Lifelight is collected via a questionnaire. Data to estimate the cost-effectiveness of Lifelight compared with standard-of-care VS measurement are also being collected. A new method for rPPG signal processing is currently being developed, based on the identification of small areas of high-quality signals in each individual. Anticipated recruitment is 1950 participants, with the expectation that data from approximately 1700 will be used for software development. Data from 250 participants will be retained to test the performance of Lifelight against predefined performance targets. Results Recruitment began in May 2021 but was hindered by the restrictions instigated during the COVID-19 pandemic. The development of data processing methodology is in progress. The data for analysis will become available from September 2022, and the algorithms will be refined continuously to improve clinical accuracy. The performance of Lifelight compared with that of the standard-of-care measurement of VS will then be tested. Recruitment will resume if further data are required. The analyses are expected to be completed in early 2023. Conclusions This study will support the refinement of data collection and processing toward the development of a robust app that is suitable for routine clinical use. Trial Registration ClinicalTrials.gov NCT04763746; https://clinicaltrials.gov/ct2/show/NCT04763746 International Registered Report Identifier (IRRID) DERR1-10.2196/41533
Dynamic Region of Interest Selection in Remote Photoplethysmography: Proof-of-Concept Study
Background Remote photoplethysmography (rPPG) can record vital signs (VSs) by detecting subtle changes in the light reflected from the skin. Lifelight (Xim Ltd) is a novel software being developed as a medical device for the contactless measurement of VSs using rPPG via integral cameras on smart devices. Research to date has focused on extracting the pulsatile VS from the raw signal, which can be influenced by factors such as ambient light, skin thickness, facial movements, and skin tone. Objective This preliminary proof-of-concept study outlines a dynamic approach to rPPG signal processing wherein green channel signals from the most relevant areas of the face (the midface, comprising the cheeks, nose, and top of the lip) are optimized for each subject using tiling and aggregation (T&A) algorithms. Methods High-resolution 60-second videos were recorded during the VISION-MD study. The midface was divided into 62 tiles of 20×20 pixels, and the signals from multiple tiles were evaluated using bespoke algorithms through weighting according to signal-to-noise ratio in the frequency domain (SNR-F) score or segmentation. Midface signals before and after T&A were categorized by a trained observer blinded to the data processing as 0 (high quality, suitable for algorithm training), 1 (suitable for algorithm testing), or 2 (inadequate quality). On secondary analysis, observer categories were compared for signals predicted to improve categories following T&A based on the SNR-F score. Observer ratings and SNR-F scores were also compared before and after T&A for Fitzpatrick skin tones 5 and 6, wherein rPPG is hampered by light absorption by melanin. Results The analysis used 4310 videos recorded from 1315 participants. Category 2 and 1 signals had lower mean SNR-F scores than category 0 signals. T&A improved the mean SNR-F score using all algorithms. Depending on the algorithm, 18% (763/4212) to 31% (1306/4212) of signals improved by at least one category, with up to 10% (438/4212) improving into category 0, and 67% (2834/4212) to 79% (3337/4212) remaining in the same category. Importantly, 9% (396/4212) to 21% (875/4212) improved from category 2 (not usable) into category 1. All algorithms showed improvements. No more than 3% (137/4212) of signals were assigned to a lower-quality category following T&A. On secondary analysis, 62% of signals (32/52) were recategorized, as predicted from the SNR-F score. T&A improved SNR-F scores in darker skin tones; 41% of signals (151/369) improved from category 2 to 1 and 12% (44/369) from category 1 to 0. Conclusions The T&A approach to dynamic region of interest selection improved signal quality, including in dark skin tones. The method was verified by comparison with a trained observer’s rating. T&A could overcome factors that compromise whole-face rPPG. This method’s performance in estimating VS is currently being assessed. Trial Registration ClinicalTrials.gov NCT04763746; https://clinicaltrials.gov/ct2/show/NCT04763746
Dynamic Region of Interest Selection in Remote Photoplethysmography: Proof-of-Concept Study (Preprint)
BACKGROUND Remote photoplethysmography (rPPG) can record vital signs (VS) by detecting subtle changes in the light reflected from the skin. Lifelight®(Xim Ltd) is a novel software being developed as a medical device for the contactless measurement of VS using rPPG via the integral cameras on smart devices. Research to date has focused on extracting the pulsatile VS signal from the raw signal, which can be influenced by factors such as ambient light, skin thickness, facial movements and skin tone. OBJECTIVE This preliminary proof-of-concept study outlines a dynamic approach to rPPG signal processing in which green channel signals from the most relevant areas of the face (the mid-face, comprising the cheeks, nose and top of the lip) are optimized for each subject using tiling and aggregation (T&A) algorithms. METHODS High-resolution 60 second videos were recorded during the VISION-MD study (Clinicaltrials.gov identifier NCT04763746). The mid-face was divided into 62 tiles of 20 × 20 pixels and the best 30 tiles, based on the signal to noise ratio in the frequency domain (SNR-F), aggregated using five different algorithms. Signals from the mid-face before and after T&A were categorized by a trained observer blinded to the data processing as 0 (high quality, suitable for algorithm training), 1 (suitable for algorithm testing) or 2 (inadequate quality). In a secondary analysis, observer categories were compared for signals predicted to improve category following T&A based on SNR-F score. Observer ratings and SNR-F scores were also compared before and after T&A for Fitzpatrick skin tones 5 and 6, in which rPPG is hampered by light absorption by melanin. RESULTS The analysis used 4310 videos recorded from 1315 participants. Signals in categories 2 and 1 had lower mean SNR-F scores than those in category 0. T&A improved the mean SNR-F score using all algorithms. Nine to 21% improved by at least one category, with up to 10% improving into category 0, and 15–39% remained in the same category,. Importantly, 9–21% improved from category 2 (not usable) into category 1. Improvements were seen with all the algorithms tested. No more than 2% of signals were assigned into a lower-quality category following T&A. In the secondary analysis, 62% of 52 signals were re-categorized by the observer as predicted from SNR-F score. T&A improved SNR-F scores in darker skin tones; 41% of 369 signals improved from category 2 to 1 and 12% from category 1 to 0. CONCLUSIONS The T&A approach to dynamic ROI selection improved signal quality, including in dark skin tones. The method was verified by comparison with a trained observer rating. T&A can reasonably be expected to overcome factors that compromise whole-face rPPG. The performance of this method in estimating VS is currently being assessed.
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