Lifelight
– proven by research & clinicians

Lifelight - The Science

Lifelight
– proven by research & clinicians

Lifelight - The Science

The Need for Change

The burden of chronic diseases is massive – in the UK 15 million people have a long-term condition (LTC) and those diseases are responsible for 41 million (71%) of global deaths annually – a huge financial cost to health and care providers, businesses, taxpayers and societies and a very significant cause of distress to humanity.

Regular vital sign monitoring is proven to improve chronic disease patient outcomes.  Regular vital sign monitoring is not widely practiced as patients in developed world countries find existing monitoring methods time-consuming and uncomfortable, and patients in emerging countries do not have access to the needed medical equipment.

  • Life Changing: Lifelight First and ultimately, Lifelight Home promises unbeatable compliance to vital signs measurements for people with LTCs and diseases. Regular vital sign measurement is critical for clinical monitoring of LTCs such as hypertension and atrial fibrillation, as it provides healthcare professionals with the data they need to optimise a patient’s treatment plan, and patients with the data to better understand and manage their condition or disease. It also allows for the early detection of LTCs and disease exacerbations such as asthma attacks and acute pulmonary exacerbations in cystic fibrosis, which are triggered by infection.

  • Unique Technology: Lifelight’s incredible claim of unbeatable compliance is achieved as the sensor used to measure the vital signs is the camera within any standard smart device (mobile phone, tablet, laptop). This means vital signs can be measured completely contactlessly, passively and automatically while the user is using their smart device.  All that is required is for the user’s face to be in line of sight of the camera for 40 seconds.

  • The Solution: No peripherals are required, making it a completely ubiquitous solution to the age-old problem of patient non-compliance and lack of access to clinical hardware. The vital signs are tracked over time, with alerts sent to the user and their accountable clinician (e.g. GP) or other responsible adult (e.g. parent) when pre-defined vital sign thresholds are crossed.

Lifelight is unique, clinical proven technology, based on rPPG – remote photoplethysmography.  It is not a wearable and therefore is truly passive and 100% contactless.  Lifelight Home has potential to create a new paradigm in chronic disease self-care and to revolutionise healthcare access in emerging countries.

The Need for Change

The burden of chronic diseases is massive – in the UK 15 million people have a long-term condition (LTC) and those diseases are responsible for 41 million (71%) of global deaths annually – a huge financial cost to health and care providers, businesses, taxpayers and societies and a very significant cause of distress to humanity.

Regular vital sign monitoring is proven to improve chronic disease patient outcomes.  Regular vital sign monitoring is not widely practiced as patients in developed world countries find existing monitoring methods time-consuming and uncomfortable, and patients in emerging countries do not have access to the needed medical equipment.

  • Life Changing: Lifelight First and ultimately, Lifelight Home promises unbeatable compliance to vital signs measurements for people with LTCs and diseases. Regular vital sign measurement is critical for clinical monitoring of LTCs such as hypertension and atrial fibrillation, as it provides healthcare professionals with the data they need to optimise a patient’s treatment plan, and patients with the data to better understand and manage their condition or disease. It also allows for the early detection of LTCs and disease exacerbations such as asthma attacks and acute pulmonary exacerbations in cystic fibrosis, which are triggered by infection.

  • Unique Technology: Lifelight’s incredible claim of unbeatable compliance is achieved as the sensor used to measure the vital signs is the camera within any standard smart device (mobile phone, tablet, laptop). This means vital signs can be measured completely contactlessly, passively and automatically while the user is using their smart device.  All that is required is for the user’s face to be in line of sight of the camera for 40 seconds.

  • The Solution: No peripherals are required, making it a completely ubiquitous solution to the age-old problem of patient non-compliance and lack of access to clinical hardware. The vital signs are tracked over time, with alerts sent to the user and their accountable clinician (e.g. GP) or other responsible adult (e.g. parent) when pre-defined vital sign thresholds are crossed.

Lifelight is unique, clinical proven technology, based on rPPG – remote photoplethysmography.  It is not a wearable and therefore is truly passive and 100% contactless.  Lifelight Home has potential to create a new paradigm in chronic disease self-care and to revolutionise healthcare access in emerging countries.

The Need for Change

The burden of chronic diseases is massive – in the UK 15 million people have a long-term condition (LTC) and those diseases are responsible for 41 million (71%) of global deaths annually – a huge financial cost to health and care providers, businesses, taxpayers and societies and a very significant cause of distress to humanity.

Regular vital sign monitoring is proven to improve chronic disease patient outcomes.  Regular vital sign monitoring is not widely practiced as patients in developed world countries find existing monitoring methods time-consuming and uncomfortable, and patients in emerging countries do not have access to the needed medical equipment.

  • Life Changing: Lifelight First and ultimately, Lifelight Home promises unbeatable compliance to vital signs measurements for people with LTCs and diseases. Regular vital sign measurement is critical for clinical monitoring of LTCs such as hypertension and atrial fibrillation, as it provides healthcare professionals with the data they need to optimise a patient’s treatment plan, and patients with the data to better understand and manage their condition or disease. It also allows for the early detection of LTCs and disease exacerbations such as asthma attacks and acute pulmonary exacerbations in cystic fibrosis, which are triggered by infection.

  • Unique Technology: Lifelight’s incredible claim of unbeatable compliance is achieved as the sensor used to measure the vital signs is the camera within any standard smart device (mobile phone, tablet, laptop). This means vital signs can be measured completely contactlessly, passively and automatically while the user is using their smart device.  All that is required is for the user’s face to be in line of sight of the camera for 40 seconds.

  • The Solution: No peripherals are required, making it a completely ubiquitous solution to the age-old problem of patient non-compliance and lack of access to clinical hardware. The vital signs are tracked over time, with alerts sent to the user and their accountable clinician (e.g. GP) or other responsible adult (e.g. parent) when pre-defined vital sign thresholds are crossed.

Lifelight is unique, clinical proven technology, based on rPPG – remote photoplethysmography.  It is not a wearable and therefore is truly passive and 100% contactless.  Lifelight Home has potential to create a new paradigm in chronic disease self-care and to revolutionise healthcare access in emerging countries.

How does Lifelight Work?

When your heart beats, your skin “micro blushes” red.  Undetectable by the human eye, Lifelight’s algorithms captures these tiny changes in colour, cleans up the signals and converts them into vital sign measurements.

Our solution is based on photoplethysmography (PPG), used today in the finger pulse oximeter.  Widely used by clinicians to measure pulse, the wired clip is placed on your fingertip.  Lifelight has uniquely taken the science one step further by using rPPG – remote photoplethysmography – enabling Lifelight to be 100% contactless.

In clinical terms, PPG is a non-invasive technology that uses a light source and a photodetector at the surface of skin to measure the volumetric variations of blood circulation.  Remote PPG (rPPG) is an evolution of PPG technology that no longer requires physical contact, relying instead on ambient light being reflected from the skin and captured remotely by a CMOS (complementary metal-oxide semiconductor) camera. The colour signals captured can then be filtered using AI techniques such as Independent Component Analysis (ICA).

When a patient looks at Lifelight on a tablet or smartphone, the device’s camera uses the ambient light to pick up changes in the skin by light reflecting off blood vessels in the hypodermis.  The camera’s readings are then converted into red, green and blue (RGB) values at a rate of 30 times a second and sent to the cloud.

These RGB values are then passed through independent component analysis, followed by signal processing and finally analysed by Lifelight’s algorithms to return the vital signs readings.

In just 40 seconds Lifelight delivers a single set of measurements – which currently requires multiple devices.

Due to the market-first nature of the technology, Lifelight is effectively patented (two), contractually protected and proven by a uniquely large clinical dataset.  This proprietary databank has proven the accuracy of Lifelight over traditional methods.

Lifelight’s technology is a practical application of rPPG using ubiquitous mobile phones and tablets for static non-contact health monitoring of patients/consumers, eliminating the need for additional devices.

What is rPPG?

Proven by Research

Lifelight understands that UK medicine is evidence-based; hence the size of our clinical trial at Portsmouth Hospitals NHS Trust and continual protection of our IP.  Lifelight has already been granted 2 patents.

“This study was about whether a phone could collect enough information to accurately tell us about a patient’s vital signs.
As a result of patients simply taking a photo, we’ve been able to do that and discover problems that people previously didn’t know about.”

Professor Anoop Chauhan, Director of Research – Portsmouth Hospitals NHS Trust, talking about the Vision-D study

Lifelight has been developed through funding by the Government’s Innovate UK programme and NHS England’s SBRI Healthcare initiative, led by the Academic Health Science Networks (AHSNs).  Additionally, Lifelight is being trialed at a number of early adopter healthcare organisations, including, North East London NHS Foundation Trust (NELFT), NHS Essex Partnership University NHS Foundation Trust (EPUT) and East Devon Health Limited.

In terms of further recognition, Lifelight has been selected by NHS England as one of four ‘nationally important’ technologies to be piloted by NICE through its new ‘Digital Health Evaluation Framework.’   This will result in a NICE recommendation for the adoption of Lifelight.

As well as the development of our own and our partner’s clinical publications, namely Portsmouth’s Vision-D study, a number of wider industry papers exist clearly demonstrating the obvious need for Lifelight in regular vital signs monitoring.

Existing Clinical Papers – in order of publication – most recent first

Blood Pressure Estimation from Photoplethysmogram
Using a Spectro-Temporal Deep Neural Network

Gašper Slapničar, Nejc Mlakar & Mitja Luštrek

Published online: 4 August 2019
© 2019 by the authors. Licensee MDPI, Basel, Switzerland

Abstract
Blood pressure (BP) is a direct indicator of hypertension, a dangerous and potentially deadly condition. Regular monitoring of BP is thus important, but many people have aversion towards cuff-based devices, and their limitation is that they can only be used at rest. Using just a photoplethysmogram (PPG) to estimate BP is a potential solution investigated in our study. We analyzed the MIMIC III database for high-quality PPG and arterial BP waveforms, resulting in over 700 h of signals after preprocessing, belonging to 510 subjects. We then used the PPG alongside its first and second derivative as inputs into a novel spectro-temporal deep neural network with residual connections. We have shown in a leave-one-subject-out experiment that the network is able to model the dependency between PPG and BP, achieving mean absolute errors of 9.43 for systolic and 6.88 for diastolic BP. Additionally we have shown that personalization of models is important and substantially improves the results, while deriving a good general predictive model is difficult. We have made crucial parts of our study, especially the list of used subjects and our neural network code, publicly available, in an effort to provide a solid baseline and simplify potential comparison between future studies on an explicit MIMIC III subset.

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Accuracy of blood pressure monitoring devices: a critical need for improvement that could resolve discrepancy in hypertension guidelines

James E. Sharman & Thomas H. Marwick

Published online: 31 October 2018
© Springer Nature Limited 2018

Abstract
Hypertension is the most significant modifiable risk factor for cardiovascular disease and contributes to the highest global burden of disease. Blood pressure (BP) measurement is among the most important of all medical tests, and it is critical for BP monitoring devices to be accurate. Comprehensive new evidence from meta-analyses clearly shows that many BP monitoring devices (including oscillometric machines and “gold standard” mercury auscultation) do not accurately represent the BP within the arteries at the upper arm (brachial) or central aorta.

Altogether, there is a critical need to improve the accuracy standards of BP monitoring devices.

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Optical blood pressure estimation with photoplethysmography and FFT-based neural networks

Xiaoman Xing & Mingshan Sun

Published online: 12 July 2016
© Articles from Biomedical Optics Express are provided here courtesy of Optical Society of America

Abstract
We introduce and validate a beat-to-beat optical blood pressure (BP) estimation paradigm using only photoplethysmogram (PPG) signal from finger tips. The scheme determines subject-specific contribution to PPG signal and removes most of its influence by proper normalization. Key features such as amplitudes and phases of cardiac components were extracted by a fast Fourier transform and were used to train an artificial neural network, which was then used to estimate BP from PPG. Validation was done on 69 patients from the MIMIC II database plus 23 volunteers. All estimations showed a good correlation with the reference values. This method is fast and robust, and can potentially be used to perform pulse wave analysis in addition to BP estimation.

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Real-time Quantifying Heart Beat Rate from Facial Video Recording on a Smart Phone using Kalman Filters

Wen Jun Jiang, Shi Chao Gao, Peter Wittek & Li Zhao

Published at: 15 – 18 October 2014
IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)

Abstract
Photoplethysmography (PPG) can be carried out through facial video recording by a smart phone camera in ambient light. The main challenge is to eliminate motion artifacts and ambient noise. We describe a real-time algorithm to quantify the heart beat rate from facial video recording captured by the camera of a smart phone. We extract the green channel from the video. Then we normalize it and use a Kalman filter with a particular structure to eliminate ambient noise. This filter also enhances the heart pulse component in the signal distorted by Gaussian noise and white noise. After that we employ a band-pass FIR filter to remove the remaining motion artifacts. This is followed by peak detection or Lomb periodogram to estimate heart rate. The algorithm has low computational overhead, low delay and high robustness, making it suitable for real-time interaction on a smart phone. Finally we describe an Android application based on this study.

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Remote plethysmographic imaging using ambient light

Wim Verkruysse, Lars O Svaasand & J Stuart Nelson

Published online: 12 December 2008
© 2008 Optical Society of America

Abstract
Plethysmographic signals were measured remotely (>1m) using ambient light and a simple consumer level digital camera in movie mode. Heart and respiration rates could be quantified up to several harmonics. Although the green channel featuring the strongest plethysmographic signal, corresponding to an absorption peak by (oxy-) hemoglobin, the red and blue channels also contained plethysmographic information. The results show that ambient light photo-plethysmography may be useful for medical purposes such as characterization of vascular skin lesions (e.g., port wine stains) and remote sensing of vital signs (e.g., heart and respiration rates) for triage or sports purposes.

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How the tech works

Lifelight’s data source is derived from the signal that is picked up from the patient’s face.  A signal is detected by Lifelight First from analysing tiny colour changes of the face with each pulse beat. This is turned into a stream of red, green and blue (RGB) average numbers.  The face is, effectively, pixelated then the anonymised colour signal is sent to the Amazon Web Service (AWS) for processing by Lifelight First’s algorithms.

AWS is one of the world’s largest cloud infrastructures.  Delivering enterprise-grade security with a 99.5% guaranteed uptime, the AWS cloud platform also meets a broad set of international and industry specific compliance standards, including, ISO/IEC 27001:2013, 27017:2015 and 27018:2014.
Lifelight First is hosted on AWS, located within the EEC (European Economic Community).

The data processed is a combination of the waveform signal as derived from the mobile device’s camera plus anonymous biometric data (age, height and sex).  This is sent to the AWS for processing by the Lifelight First algorithms.  Five data items are sent back to the Lifelight First application (blood pressure, pulse, respiration, SpO2 and AF – coming soon).  These appear on the screen along with the signal quality measure which indicates reliability as a safety feature.

The patient’s age, height and sex are captured to help the Lifelight First algorithm to accurately calculate systolic and diastolic blood pressure.

It is important to emphasise that although the Lifelight First application captures height age and sex, it is not associated with the patient as there is no other personal identifiable data captured – e.g. name, email address, NHS number, fingerprint, etc.  The device will wipe this information immediately after Lifelight First has sent the RGB stream to the AWS for processing.

In this respect, the data processing is considered to be very low risk as there is no threat to the rights and freedoms of the data subject (typically, the patient) when a clinician uses Lifelight First.  In many respects it is very similar to taking an individual’s temperature with a thermometer.

In the context of a standalone system no data is saved on the device.  This data is stored on the server for quality control and to provide data for the continued testing of quality and efficacy of the algorithm (machine learning).

Lifelight First is not the data controller – the data controller is the employer of the user conducting the test (e.g. clinic, hospital, care home etc.) and they will process data using the appropriate legal pathway.

Lifelight First is registered with the ICO (Information Commissioner’s Office in the United Kingdom).

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