Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology. Wearables such as smartwatches and fitness trackers commonly use the optical sensing technology ‘photoplethysmography’ to acquire an arterial pulse wave signal, from which a wealth of physiological information can be derived. Several promising applications of wearable photoplethysmography are either being translated into clinical practice or in development, including detecting abnormal heart rhythms, monitoring blood pressure, and identifying sleep disorders. There is great potential for wearable photoplethysmography to improve health and wellbeing, meaning much further work is warranted to realise its full benefits.et alet alet alet alet alet alThe field of wearable photoplethysmography is a highly attractive area for research and development. Photoplethysmography entered clinical use in the 1980s in the form of pulse oximeters, revolutionising the measurement of arterial blood oxygen saturation, and pulse oximeters remain widely used in many clinical settings. In the 2010s, photoplethysmography was incorporated into consumer wearables such as smartwatches and fitness trackers, which are now used by millions each day. The technology is simple enough to be widely understood: an optical sensor for measuring the blood pulse. And yet it is complex enough to capture the imagination of researchers worldwide: there is no consensus yet on the physiological origins of the photoplethysmogram signal section . The pot1.Sensor design: innovative approaches to photoplethysmography sensor design are discussed, including flexible sensors, in-ear sensors (‘hearables’), and multi-wavelength photoplethysmography.2.Signal processing: advances in photoplethysmography signal processing for physiological monitoring are presented, including developments in blood pressure, respiratory and sleep monitoring. These are considered alongside strategies to handle low quality signals and motion artifact.3.Applications: clinical and consumer applications of wearable photoplethysmography are discussed, ranging from established applications such as the detection of atrial fibrillation, to emerging applications such as mental health assessment.4.Research directions: key directions for future research are highlighted, including developing and validating techniques for blood pressure monitoring, and investigating sources of inaccuracy in wearable photoplethysmography measurements.In this Roadmap, experts provide their perspectives on the future development of wearable photoplethysmography. The Roadmap is intended to help guide future research and development in the field. The Roadmap covers a range of key topics within the field of wearable photoplethysmography. The section on each topic provides a concise overview of the topic’s current status, the key challenges ahead, and the advances in science and technology which could address these challenges. The sections stand alone, so can be read individually. Topics lie within four areas, as illustrated in figure •Expanding functionality: much research is focused on expanding the functionality of wearable photoplethysmography devices, such as developing and validating techniques to monitor novel parameters such as blood pressure, oxygen saturation, and even detailed respiratory metrics such as inhalation and exhalation times. The expanding functionality of devices will enable detailed physiological assessments to be performed unobtrusively in daily life, which otherwise could only be performed in clinical settings.•Optimising sensor design: the design of photoplethysmography sensors strongly affects both the quality of the acquired signals, and the physiological information contained within them. Key design decisions include: the anatomical site of the sensor , the mode of sensor attachment, and the wavelength(s) of light to be used. These decisions can all influence PPG pulse wave morphology and resulting measurements. Decisions are often a compromise between making wearable photoplethysmography devices as unobtrusive and as accurate as possible.•Approaches to signal processing: a range of approaches have been proposed to process photoplethysmography signals, and it is not yet clear which provide the best performance, nor whether different approaches are best suited to different applications. Approaches include traditional pulse wave analysis, and more recently pulse wave decomposition and deep learning.•Identifying potential applications: new potential applications for wearable photoplethysmography will emerge as the functionality of the technology expands. In particular, the ability to reliably monitor blood pressure and oxygen saturation would open new opportunities. There is potential for data-driven health interventions and services, as well as applications in resource-constrained settings.•Gaining trust: the successful use of wearable photoplethysmography devices to improve health and well-being will require the trust of multiple stakeholders including clinicians, policy makers, and most importantly device users. Important approaches to gain trust include ensuring that applications are supported by a strong evidence-base, and that personal data are handled appropriately.Several key themes emerge from the Roadmap which could inform overall strategies for research and development:•Signal quality: the photoplethysmogram signal is highly susceptible to noise caused by motion artifact and other sources. Different approaches have been proposed to identify and handle low quality signals, ranging from optimising sensor design, to developing signal processing techniques to delineate low quality periods and recover noise-free signals.•Signal processing resources: the development of signal processing algorithms is greatly aided by open datasets, and open code for existing algorithms. The acquisition of datasets in daily life, from patients with diseases of interest, can be facilitated by devices which provide the raw photoplethysmogram signal.•Validating devices: processes for validating devices should be carefully designed to provide a comprehensive understanding of real-world performance across subjects with different characteristics. Work has begun on creating standards and recommended protocols to guide robust validation studies.•Sources of inaccuracy: studies have identified several potential sources of inaccuracy of wearable photoplethysmography devices. Work is ongoing to identify further sources, and to mitigate against them.•Equity: when developing wearable photoplethysmography devices, it is important to be mindful of the equity of access to devices and their performance. Key considerations are the cost of devices, and the performance of devices in subjects with different characteristics .•Establishing best practices: it may be beneficial to establish best practices in wearable photoplethysmography across several areas, including: sensor design; signal quality assessment; signal processing algorithms; benchmark datasets; measurement protocols; and standardising evaluation protocols.Several key challenges and potential solutions emerge from the Roadmap:Wearable photoplethysmography is a valuable tool for unobtrusive physiological monitoring. Despite having entered the consumer market only a decade ago, it is already being used to guide clinical decision making in selected applications. There is potential for wearable photoplethysmography to be used for many more health and wellbeing applications. High quality, patient-centred research and development is required to realise this potential for the benefit of society.This Roadmap provides a concise overview of several topics in the field of wearable photoplethysmography, focusing on directions for future work. For further information on selected topics, please see the references in table www.cost.eu). The authors have confirmed that any identifiable participants in this study have given their consent for publication.This work was supported by the British Heart Foundation under Grant FS/20/20/34626, and COST Action CA18216 VascAgeNet, supported by COST , Keck School of Medicine, University of Southern California, Los Angeles, CA 90033In the last decade, PPG sensors have transcended their necessity in the health sector as they entered the burgeoning industry of wearable technology being an integral part of fitness tracking gadgets like smartwatches or wristbands. Most commercial PPG sensors in such wearable devices utilize traditional LEDs (inorganic III–V compound semiconductor-based LEDs) as light source and silicon photodiodes as the photodetector, which are limited by their rigidity, bulky nature, and high expense associated with scalable fabrication. To tackle these issues, researchers have been working on developing new types of flexible PPG sensors based on various emerging material technologies which can ensure skin conformability, the versatility of design, lower power consumption, mass-production scalability, and an overall improved user experience. Such flexibility is of great importance in neo-natal and infant care where inconspicuous and flexible sensors can significantly aid in monitoring vital signs of the delicate organs which could otherwise be discomforted by rigid electronic components. Organic materials are particularly relevant in this regard due to their inherent mechanical softness enabling the formation of flexible, lightweight organic optoelectronic devices prepared with solution processing, printing, or coating techniques that allow freedom in design. Figures et al  and organic LEDs (OLED) was reported by Lochner et al . For theal et al  demonstrμm thickness and capable of withstanding high compressive strain was developed by researchers at Tokyo University using green and red Polymer LEDs (PLEDs)  enabled wireless optoelectronic system for PPG monitoring  and electrocardiography (ECG) to photoplethysmography (PPG). In-ear PPG, introduced in the literature as a measurement site in 2007 , offer many advantages over conventional wrist and finger recording sites. A major obstacle to wearable PPG is constriction of peripheral blood flow that occurs with low temperatures, but in-ear PPG has been shown to be resistant to these effects due to the characteristic of the ear canal  against non-obstructive respiratory diseases, and healthy controls, both older and younger  microphones, capable of detecting minor movements between the sensor and skin, have proven effective at removing these ear-specific motion artefacts from ear-EEG  sensors emit and detect multiple wavelengths of light to non-invasively characterize underlying tissue. As summarized in figure et alet alet alet alet alet alThe skin has a layered structure perfused by capillaries in the papillary dermis, arterioles and venules in the dermal layer, and arteries and veins in the subdermal layer. When MWPPG includes well-separated wavelengths differing significantly in penetration depths, the pulsatile waveforms (AC) of MWPPG, referred to as pulsatile MWPPG here, can be used to characterize vessel type-specific pulses originating from different skin depths. It has been observed that pulsatile MWPPG channels differ in morphologies and trajectories in response to contact pressure, reactive hyperemia, breath holding, etc  bear resemblance in their morphologies and frequency spectrums. Novel optical sensing layouts and schemes and intelligent compensating algorithms will be needed to improve the measurement robustness.Light sources of proper wavelength resolution and high sensitivity photodetectors are hardware requisites for reliable measurements, particularly in spectroscopic MWPPG sensing for biomarker detection. Epidermal spectroscopic MWPPG measurement of biomarkers faces challenges in measurement sensitivity and specificity due to low concentration of biomarkers, interfering molecules, and spectral overtone, as well as technical challenges such as identification of optimal wavelength combinations and suitable chromophores and model calibrations for uncertain internal factors and environmental factors such as skin tones, temperature, and humidity.et alet alHigh-performance optical sensors with tailored sensing schemes can assist the acquisition of high-quality MWPPG signals. Flexible electro-optical sensors provide better conformity with the skin surface, thus facilitating motion-resistant and long-term health monitoring  from the Research Grants Council of Hong Kong. Dr Franklin is the Ted Rogers Chair in Cardiovascular Engineering, University Health Network, University of Toronto.5.City, University of Londonet alet alet alPulse rate variability (PRV) describes the changes in pulse rate through time and is measured from pulsatile signals, especially the photoplethysmogram (PPG). It has been used to assess cardiac autonomic activity and multiple mental and somatic diseases  signals continuously, which can be cumbersome and difficult to implement in real-life scenarios, researchers have used the PPG as an alternative source of pulse rate measurements, given that it is a non-invasive, non-intrusive, simple, and low-cost technique Kyriacou . PRV anaet alet alet alet alet alet alet alet alAlthough HRV and PRV originate from similar processes, and pulse rate has been found to be a good surrogate of heart rate, the relationship between HRV and PRV is not straightforward, and there is still no consensus regarding the validity of using PRV as a surrogate of HRV  understanding how the above-mentioned aspects affect PRV analysis and results; and (2) reaching a clearer understanding of how different physiological phenomena affect PRV and its relationship with HRV.PPG sensors are the main technology needed to obtain PRV information. However, and as with most techniques based on PPG, there is a lack of standardisation of the technique and the signal processing strategies used to extract PRV from PPG signals. This lack of standardisation suggests that the comparison and validation of PRV-related results is not possible, hence decreasing the reliability of the technique. It is important to establish guidelines for standardising PRV estimation, similar to the standardisation of HRV measurements , IIS Aragon, University of Zaragoza, 50018 Zaragoza, Spainet alRespiration is the exchange of oxygen and carbon dioxide between the body and the environment. It is essential for human life. Breathing subtly influences the PPG signal, providing opportunity to use the PPG for respiratory monitoring. The effects of breathing on the PPG include: frequency modulation induced by respiratory sinus arrhythmia; and baseline wander, amplitude and pulse width modulation (changes in the width of the systolic portion of the pulse wave)  from PPG signals   networks to process segments of PPG signals, with minimal denoising required  has shown significant promise for accurately interpreting PPG signals in the context of respiratory health . Firstly, the growing number of publicly available PPG datasets provides opportunity to assess performance across multiple datasets to investigate generalisability across different settings and devices. It is particularly important to assess performance in daily life, since most previous work has been conducted in healthcare settings or controlled conditions. Secondly, the statistical methods used should be tailored to the intended application. To date, assessments of RR algorithms have focused on performance at a single time-point. In the future, studies could also assess the ability of algorithms to track changes in parameters over time, using similar approaches to those in section Further work should establish how best to assess the performance of PPG-based respiratory monitoring techniques  using advanced AI models, such as autoencoders and transformers, to reconstruct clean signals from noisy ones; and (ii) using pulse decomposition analysis to reconstruct denoised PPG signals . Alternatively, it may be possible to enhance signal quality by positioning PPG sensors in regions with higher vein density  or local interpretable model-agnostic explanations (LIME), to identify which features contributed most strongly to a model’s output. The model can then be revised to use only the few most important features. In terms of lightweight edge AI architectures, suitable candidates include random forests, shallow convolutional neural networks (CNNs), and LSTMs.With respect to tidal volume estimation, the amplitude of the respiratory modulations that serve as a basis for estimating RR from PPG could also serve as a basis for tracking tidal volume changes. Furthermore, tidal volume may be estimated in absolute terms after a calibration process. Estimating tidal volume in addition to RR would provide a more complete view of breathing, and that would be very interesting in several applications. In fact, all the mentioned applications will potentially benefit from new features based on tidal volume information, since their relation with respiration is actually not limited to RR, but to ventilation.Overall, the use of photoplethysmography for respiratory monitoring is an active field of research. Recent studies have advanced the field through the development of signal quality assessment metrics, use of advanced signal processing techniques, and implementation of machine learning. Nonetheless, significant challenges and opportunities remain for current and future researchers in this exciting field of healthcare. Existing techniques could be improved to enhance the feasibility of using wearables for continuous PPG-derived respiratory monitoring, and further work is required to investigate whether respiratory monitoring can be performed reliably in activities of daily life. Novel techniques are being developed to assess additional respiratory metrics from the PPG such as tidal volume and inhalation/exhalation times, which when combined with RR could provide more holistic views of breathing and ventilation. Additionally, few studies have explored the application of this technology for the diagnosis, monitoring and management of respiratory health conditions; thus, this remains a key direction for future research.PHC acknowledges funding from the British Heart Foundation (FS/20/20/34626) and an EPSRC Impact Acceleration Award. JL acknowloedges funding from CIBER in Bioengineering, Biomaterials & Nanomedicine through Instituto de Salud Carlos III and by Gobierno de Aragon (Reference Group BSICoS T39-20R) cofunded by the Fondo Europeo de Desarrollo Regional (FEDER) 2014–2020 ‘Building Europe from Aragon’.7.1Department of Electronics Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania2Biomedical Engineering Institute, Kaunas University of Technology, 44249 Kaunas, Lithuania3Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, Spain4CIBER-BBN, Instituto de Salud Carlos III, Madrid, Spainet alet alArtery pulse palpation was used by Chinese, Indian, Greek, and Roman physicians. Galen (129–210 AD) described 27 types of pulses and their meaning Parker , but onlet alThe morphology of PPWs is a significant source of diagnostic information since the PPW is determined by the interaction of the heart and blood vessels, but also modulated by respiration and autonomic nervous system activities. Therefore, pulse wave analysis (PWA) can be considered a non-invasive ‘window’ to the main physiological processes of the body. Although PWA is also performed on blood pressure signals .A significant challenge in PWD is the biophysical interpretation of extracted indicators. The link among the decomposed waves  under Projects 01.2.2-LMT-K-718-01-0030 and 01.2.2-LMT-K-718-03-0027, the European COST ACTION ‘Network for Research in Vascular Ageing’ CA18216 supported by COST (European Cooperation in Science and Technology): 8.Waseda UniversityOximetry is the measurement of haemoglobin oxygen saturation in blood or tissue and depends on the Lambert–Beer relationship between light transmission and optical density. Pulse oximeters are non-invasive, compact, and affordable devices that have been used for decades to quantify oxygen saturation. They allow early detection of hypoxaemia, enhance patient safety, decrease the anaesthesia mortality rate, and reduce caregiver workload Tamura . During In 1935, Matthes developed the first oxygen-saturation meter. It used a two-wavelength light source with red and green filters, which were later changed to red and infrared (IR) filters. In the 1940s, Millikan, a British scientist, used a dual light source to create the first aviation ear oxygen meter. In 1964, Hewlett Packard created the first ear oximeter using light of eight wavelengths; these were used primarily in sleep laboratories and pulmonary clinics but were expensive, cumbersome, and large. In 1972, Takuo Aoyagi, a Japanese bioengineer at Nihon Kohden, developed a pulse oximeter based on pulsatile variations in the optical density of tissues in the red and IR wavelengths to quantify arterial oxygen saturation based on the ratio of red to IR light absorption in blood, with no need for calibration. Susumu Nakajima (a surgeon) and his associates first tested the device in patients in 1975. Dr Aoyagi obtained a Japanese patent. Minolta obtained a United States patent based on the same concept. Nihon Kohden introduced an ear oximeter in 1975, and Minolta began marketing a fingertip area oximeter in 1977. The history of the pulse oximeter is shown in figure In the United States, Ohmeda Biox and Nellcor introduced the pulse oximeter. Initially, it was applied for respiratory care, but its use later expanded to operating rooms. Since then, other manufacturers have entered the market and pulse oximeter technology has improved significantly.Pulse oximetry became part of the general anaesthetic procedure in the United States in 1987, and quickly spread to emergency rooms, recovery rooms, neonatal units, and intensive-care units. The first fingertip pulse oximeter appeared on the market in 1995. The Food and Drug Administration (FDA) published a notice in the Federal Register  entitled ‘Draft Guidance for Industry and Food and Drug Administration Staff; Pulse Oximeters Premarket Notification Submissions [510(k)s]’. Shortly thereafter, FDA-approved pulse oximeters became available.Handheld pulse oximeters with small probes, wrist-worn devices with finger probes or embedded light-emitting diode-photodetector (LED-PD) sensors, fingertip devices, and ring oximeters are all available. Instruments used clinically include wrist-worn devices such as WristOx , Pulsox 500i , and Checkme O2 ; wireless telemetry devices with pulse oximetry; and an embedded pulse oximeter . Recently, pulse oximeters have become affordable for both in-hospital and home use. Typical wrist-pulse oximeters include the Oxitone 1000 M  and a wrist monitor , which have been approved by the FDA and used in hospitals.et alet alet alNon-FDA-approved devices include the Apple Watch, Fitbit, and Galaxy Watch, which have personal health applications. Differences between approved and non-approved devices are discussed in the next section. More recently, ring-pulse oximeters have been released, such as the CIRCUL , RingO2 , and Checkme Ring (San-ei Medsys)  signals, including the light wavelength, measurement site, contact force, motion artifacts, skin colour, henna pigmentation, nail varnish, and ambient light intensity and temperature.et alet alet alet alet alet alPulse oximeters may over- or underestimate oxygen saturation. Their accuracy is affected by skin pigmentation, the fit of the device, peripheral blood flow, nail coatings, tattoos and dyes, and device maintenance and cleaning. Currently, skin pigmentation is a major research topic. Pulse oximeters may overestimate oxygen saturation in individuals with relatively dark skin, potentially resulting in hypoxia not being treated  , and dorsal and ventral measurements should be obtained.Regarding standards and regulations for oximeters, the FDA 510k accuracy requirement is <3% for transmittance sensors and <3.5% for reflectance sensors; the ISO requirement for the latter is <4%. Accuracy limitations should be considered when using pulse oximetry to aid diagnosis and treatment decision-making. Standardisation of test protocols is required to elucidate pulse-oximetry errors in patients with dark skin. This will enable performance evaluation data to be shared.et alA patch oximeter with an adhesive sensor is under development. The advantages of patch sensors include flexibility due to the organic material used in the LED and ultralow power requirements. Disposable probes are important in the COVID-19 era  rather than relying on subjective descriptions or race/ethnicity. To improve oximeter accuracy, standardisation is required. Pulse-oximetry errors in patients with dark skin must be quantified in large studies involving subjects with various levels of skin pigmentation. Testing protocols and guidelines are also needed, and standards of accuracy should be established through the collaboration of regulatory authorities, research institutes, medical professionals, and manufacturers.This work was supported in part by grants-in-aid from the Japanese Ministry of Education, Culture, Sports, Science and Technology, Scientific Research (C) (Kakenhi) (#21K12760) and the Japan Agency for Medical Research and Development (JP22dk0310111).9.1Department of Biomedical Engineering, Georgia Institute of Technology & Emory University2INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Portugal3Faculty of Engineering, University of Porto, Portugal4Nell Hodgson Woodruff School of Nursing5Department of Biomedical Informatics, School of Medicine6Department of Computer Sciences, College of Arts and Sciences, Emory Universityet alet al, Mohagheghian et alet alet alThe photoplethysmogram (PPG) is a non-invasive signal which can be used to detect physiological changes in hemodynamic and cardiac functions. However, its recording can be corrupted by subject movement, ambient light, and inappropriate sensor placement resulting in imperfect signals. Indeed, as detailed in section The challenge to incorporate signal quality as an integral part of the pipeline to accomplish a classification task can be illustrated mathematically. In this pipeline, the classification task is only performed on PPG signals whose quality is greater than a threshold. Let Moreover, the morphological characteristic varies when identifying the PPG signal quality under different conditions. First, the shape and amplitude of the PPG signal differ between modes. For example, for transmission-mode photoplethysmography, such as finger or in-ear photoplethysmography, the photodetector detects the light transmitted through the medium, whereas for reflectance-mode photoplethysmography from wrist-worn watches, the reflection time of light from the blood vessels is recorded. Therefore, it impedes the generalizability of PPG signal quality assessment. Second, the interpatient variability also influences the morphology of PPG signals. The time-varying dynamics as well as different temporal and physical conditions of different patients change the shape, amplitude, and intervals of the signals, which also makes proposing a generalizable strategy to identify the PPG quality difficult. Third, because the PPG is often used in tasks other than classification (e.g. estimating heart rate or blood pressure), the strategy designed for classification tasks to determine an optimal signal quality threshold to exclude an imperfect PPG signal is difficult to adopt.et alet alet alAs discussed above, a more nuanced treatment of PPG signal quality as an integral part of a PPG signal processing pipeline is needed to ensure optimal performance of the ultimate task. Here, we propose a potential unified approach to achieve this goal. This approach is based on the premise that the ultimate task will benefit from maximizing the use of clean samples in a PPG signal segment while minimizing the impact of artifactual samples. For example, a detection algorithm of atrial fibrillation (AF) should still be able to confidently predict AF even from a few seconds of clean PPG signal samples that are embedded in an otherwise vastly noisy signal. In terms of algorithm design, one would require weighting differently the contribution of signal samples that have different signal quality to the downstream task. This idea requires one to integrate signal quality assessment as an integral part of the downstream task. This contrasts with treating signal quality assessment as a separate pre-processing task to exclude imperfect PPG signals. Figure Optimal handling of PPG signals of imperfect quality is critical to downstream tasks. Mathematical derivation shows that simply excluding signals whose quality is below a threshold will underperform if the threshold is not chosen at an optimal value. We propose a generic algorithm framework to address this challenge through a more nuanced treatment of PPG signal quality as an integral part of a complete PPG signal processing pipeline.We greatly appreciate domain expertise in cardiology and neurology from our collaborators including Drs Randall Lee, Duc Do, and Karl Meisel.10.1Digital Health Devices Division, Medical Device Evaluation Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju, Republic of Korea2Department of Digital Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of KoreaPhotoplethysmography (PPG) is extensively employed in the latest watch and ring-type wearable electronic devices, because it can offer rich cardiovascular information  TC 124 developed a test protocol and evaluation standard  for assDespite the efforts to remove MAs from PPG signals, there has been no clear solution yet. The efforts are being expanded to include an approach involving bypassing methods focusing on avoiding after defining rather than eliminating. These trends indirectly suggest that it is difficult to eliminate MA at the current level of technology. Traditionally, MA has been recognized as a noise to be removed, but it can be viewed as evidence that the user is active in wearable PPG for daily use. This means that when dealing with MA in wearable PPG, it will be effective to look at it from the point of view of improving the signal availability, and beyond the conventional noise removal that aims to simply increase SNR. An effective response to MA of wearable PPG may begin with such a shift in perspective.This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea .The authors declare there’s no conflict of interests. The views and opinions expressed are those of the authors and do not reflect those of the Ministry of Food and Drug Safety the in Republic of Korea.11.Faculty of Medicine and Life Sciences, Tampere Universityet alAlthough photoplethysmography (PPG) has been widely used in hospital settings since the 1980s, consumer applications of PPG were only introduced around 2010. The first consumer applications were armbands which used mainly infrared light for PPG to measure heart rate (HR). These devices were very prone to motion artefacts and did not gain significant market penetration. The first optical wrist-worn HR monitors with reasonable accuracy were released in 2013  for consumer use, e.g. for monitoring training in and adjustment to high altitudes. Some devices also offer blood pressure (BP) monitoring based on pulse wave analysis of the PPG signal directly for consumers for informative monitoring.The accuracy of optical HR monitoring based on PPG has been significantly improved since 2013. Motion cancellation based on filtering and machine learning algorithms has been significantly enhanced together with advancements in optomechanical design and electronics performance, and numerous studies show that especially during rhythmic activities the best optical HR monitors reach accuracy comparable to ECG-based methods in HR monitoring , with wearable photoplethysmography devices has been a popular research topic for almost a decade. AF is linked with an over five-fold increase in the risk of stroke and there are also many other serious health conditions that are thought to be preceded by an increase in asymptomatic arrhythmic activity of the heart. These potentially preventable conditions include cognitive impairment by microemboli due to asymptomatic atrial fibrillation, sudden arrhythmic cardiac death and worsening of heart failure leading to preventable hospitalizations. Especially in the early stage, AF usually appears intermittently and is therefore detected effectively only by wearable devices enabling long-term monitoring. Today, several commercial smart watches or fitness trackers from manufacturers such as Withings, Apple, and Fitbit feature AF detection as a part of their photoplethysmographic (PPG) heart rate sensors. Currently, there are not many devices that are intended exclusively for clinical use. One such solution is the PulseOn Arrhythmia Monitor System launched in early 2022. Several others are however expected to appear soon.et alet alet al , the diagnosis of AF requires rhythm documentation with an electrocardiogram (ECG) tracing showing AF. For AF, a single-lead ECG of at least 30 seconds recorded with any device and any type of electrodes is sufficient , quantification of the number of ectopic beats , the effect of skin colour and the effect of low superficial blood perfusion. In the case of arrhythmia detection, there is however one additional specific feature in the data affecting the accuracy. Due to the imperfect filling of the ventricles, the strength of the pulse waves varies during arrhythmia, which makes it often more difficult to accurately detect individual heartbeats. This is demonstrated for example in article by Harju As said, so far PPG-based arrhythmia detection research has mainly focused on AF. The challenge with AFL, which is another relatively common arrhythmia, is that it is often manifested as a very stable rhythm caused by a supraventricular re-entry activation combined with every second, third or fourth flutter activation triggering a ventricular contraction. Due to the resulting stable rhythm, methods based on the detection of increased heartbeat interval variability are not effective. The diminished variation, which is clearly seen in ECG-based Poincaré (or Lorenz) plot might be used for detecting AFL but the inevitable uncertainty of PPG-based heartbeat interval estimation caused by variance in pulse arrival time and PPG waveform disturbances induce a challenge for reliability. Features extracted from the pulse waveforms could provide additional information for identifying AFL. Pulse signal waveforms vary between individuals, but data recorded during sinus rhythm could be used to provide individual baseline information on the waveform.et alet al have proposed a method for the detection of VES Sološenko et al  and Accuracy (Acc). Some of the more transparent papers have also identified errors in derived metrics such as total sleep time, REM efficiency and sleep latency, to provide a deeper insight into how any performance translates into downstream diagnosis. Of course, all these metrics are a function of the distribution of sleep classes within the testing and training databases, and they are rarely reported. Moreover, there are no articles at this time which have evaluated how the quoted performance maps into a diagnostic or screening performance. The published results have varied widely, with the state-of-the-art being reported to be k ≅ 0.7, which is close to inter-human expert concordance level. However, without public code and data, it is impossible to verify these surprising claims, and may reflect imbalances in sleep state prevalence in the data . It is therefore recommended that in future studies, at least one pathological class is included in the population and any sleep staging algorithm is also assessed in terms of a diagnostic outcome on the population being studied . While this doesn’t fulfil the entire diagnostic criteria, it does provide an insight into how the errors in a given sleep staging algorithm affect a down-stream diagnosis.Recent efforts to apply deep learning to PPG have ranged from learning from beat-to-beat intervals , the sensor can create localized heating, sweating, and cause discomfort. This leads users to wear the device loosely. This can be partially solved by using adhesive materials  is the high inter-rater disagreement levels. For sleep staging, values of A fourth problem, rarely addressed, is the effect of abnormal physiology on sleep. A simple example is the effect of cardiovascular rhythm and abnormal beat types on physiology-based sleep staging. Arrhythmias, or even lack of variability or respiratory sinus arrhythmia , are likely to confound any approach based on such variability changes within sleep states. Since sleep is being used as an intermediate metric to diagnose a medical problem, having a medical problem that affects the physiology of sleep and cardiovascular rhythm/variability creates a serious confounder. Either an algorithm must be trained on all possible pathologies, or pathology-specific algorithms must be used, which are selected through a separate screening process. In the latter case, the algorithm can be used for tracking changes over time in the appropriate population, but cannot be used as a screening or diagnostic agent. This appears to be a promising future direction, which would include a screening interview, followed by a baseline setting simultaneous EEG and PPG recording, then a personalized PPG-based sleep algorithm that could be developed through transfer learning, for example.et alet alet alFifth, there is little coherence in the literature on the metrics and databases used or the rationale (and description of) how different patients within databases were excluded or distributed during training, particularly with respect to demographics and health conditions  and the use of multimodal machine learning may provide an optimal method for combining these data to classify sleep stages. Moreover, there is a need to integrate downstream metrics (such as diagnoses) into the optimization process  for sleep staging, but significant barriers remain. The current push to keep raw data inaccessible in commercial devices continues to hold the field back and is likely to exacerbate existing biases. There is a clear need for standardized databases, open access code, open-source software, and reference hardware to help accelerate the utility of this key sensor modality for sleep monitoring.This work was partially funded by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378 and the NIMH under Award Number U01MH110925. We also thank to Qiao Li for his assistance in creating table 14.1Faculty of Biomedical Engineering, Technion Institute of Technology, Haifa, Israel2Faculty of Electrical and Computer Engineering, Technion Institute of Technology, Haifa, Israelet alet alet alet alet alet alObstructive sleep apnea (OSA) is a respiratory disease, caused by pharyngeal collapse during sleep and characterized by frequent awakenings. It is a highly prevalent condition with an estimated 425 million (95% CI 399–450) adults worldwide aged 30–69 years (men and women) having moderate to severe OSA, in a review of 17 studies  is recorded via transmission of the light through the finger whereas smartwatches can record the PPG via reflectance of the light through the skin surface. Although the reflectance photoplethysmography signal will often be of a lower quality than the transmission one, smartwatches (or similar wearables) bring the opportunity to ease remote diagnosis of OSA. However, the quality of the physiological measurements obtained using these new wearables is still questionable and there is currently no proof that these may be used for diagnostic purposes . Repetitive and maladaptive responses to stress might be behind the autonomic imbalance and reduced autonomic reactivity observed in mental health disorders , derived from the ECG, is the most widely used and commonly accepted measure of ANS regulation of the heart, mainly in resting conditions. In a recent systematic review HRV was identified as the most useful physiological metric for stress and anxiety detection  dynamics are influenced by changing factors such as respiration, activity, and time of the day, the contextualization of HRV measures is important for their correct interpretation. For example, in Varon et al  HRV measet al  in order to allow for the development of personalized models.In summary, the main challenges that should be addressed when using wearable PPG technology for mental health assessment include: (i) low signal quality with significant data losses during daily life which requires the development of dedicated signal quality algorithms; (ii) the importance of assessing ANS in response to stressful stimuli, rather than just in basal conditions, and the use of subject-specific measures; (iv) the inclusion of contextualization and combination with other sources.et alet alDespite the challenges and limitations of wearable PPG technology for obtaining robust ANS markers during daily-life activities, there is evidence for the potential for this modality to significantly contribute to mental health monitoring, especially when combined with other information such as physical activity, sleep quality, home stay, social interactions, cognitive function and self-reported status  through Instituto de Salud Carlos III, FEDER and Gobierno de Aragon (BSICoS T39-20R).16.1School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China2Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE) at Hong Kong Science and Technology Park, Hong Kong 999077, People’s Republic of China3Department of Biomedical Engineering, City University of Hong Kong, Hong Kong 999077, People’s Republic of Chinaet alet alet alCardiovascular diseases (CVDs) remain the No. 1 killer over the world, and high blood pressure (BP), aka hypertension, is one of the most important modifiable risk factors of CVDs. Studies have shown that 24 h ambulatory BP, particularly night-time BP, is superior to office BP in predicting total and cardiovascular mortality , continuous BP waveform via TAG and beat-by-beat BP measurement in a continuous and unobtrusive manner but without a cuff has become possible, with the translation of cardiovascular signal features to BP by either mechanism model or data-driven model figure .et alPhotoplethysmography (PPG) has been extensively used for cuffless BP measurement, since PPG reflects blood volume change that is related with BP variation. With multiple PPG signals or a PPG signal and other sensing modalities, such as electrocardiogram (ECG), pulse transit time (PTT) can be obtained to indicate BP changes. There have been various studies on PTT-based BP estimation, due to the advantages of the PTT technique being low-cost, ease of use, continuous and noninvasive  and BP also seem to vary across a whole range of dimensions, such as age, gender, level of physical activity, mental status, environment, and so on. This emphasizes the importance of research into the mechanism.Second, the sensing technology that is robust to interruptions and noise—in particular, motion artefacts—determines the practicability of the TAG or cuffless beat-by-beat BP monitoring technique. Signal processing methods to remove the interferences would further enhance the acquired signal quality. The immature sensing technology would result in poor studies in wearable and unobtrusive monitoring system in practice for TAG waveform and beat-by-beat BP estimation.Another main challenge is the insufficiency of current estimation models. A physiological mechanism-based model is usually based upon a few PPG-derived BP indicators, e.g. PTT/PWV and PIR, and the model is rather simple with limited factors being considered, leading to inadequate estimation accuracy. For a data-driven model, sufficient data that covers various possibility of BP changes, interpretability, as well as personalisation problems are the key issues.et alLast but not the least, as the bridge between the research in the lab and the application in practice, clinical validation of the technique requires standard protocol, in which the reference method to measure continuous BP or TAG signal and the duration of measurement are the important factors among others, such as sample size and dynamic range of BP. An accurate reference BP measurement method is the key to evaluate the performance of new methods or devices. However, there are two main issues of the commonly used ground truth measurement methods, e.g. auscultatory or oscillometric method, intra-arterial BP measurement. One is the synchronization problem between the reference BP and estimated BP. Because of the different nature of the reference method and the cuffless BP measurement method and each of them requires one sensing location but BP may vary at different sites of the body, it is very challenging to synchronize them. The other issue is that the reference BP measurement method can be inaccurate. For example, the auscultatory method may be affected by the experience of the observer. Even intra-arterial BP—the gold standard of BP measurement in clinical—can be distorted due to improper placement of the BP sensor and other noises. With regard to the duration of measurement, although there are standards for cuffless BP measurement addressing these issues, e.g. the IEEE Standard for Wearable Cuffless Blood Pressure Measuring Device IEEE , many vaAs mentioned above, the biggest dilemma facing cuffless BP measurement is its restricted performance due to the imperfect mechanism, motion artifacts in signal acquisition system, modelling with limited data support, as well as clinical validation standard. Efforts should therefore be undertaken to address these challenges such as to advance the science and technology.First of all, we need to clarify the mechanism of using unobtrusive CVD signals (e.g. PPG) for TAG signal and beat-by-beat BP estimations. This requires us to understand the relationship between the CVD signals and BP in more depth. More specifically, questions should be elucidated including whether the signals contain the information that represents BP changes, what kind of features can be extracted from the signals to indicate BP changes, and what is the effective model that maps the indicators to systolic and diastolic BPs and TAG waveform.et alNext, robust multi-modal sensing systems and signal processing algorithms are required to achieve reliable, unobtrusive and continuous BP measurement. A robust yet neat sensing system is critical to acquire signals for real time monitoring of BP in an unobtrusive manner. Multi-wavelength PPG is such an example for TAG monitoring  without the inflatable cuff that is commonly required by current BP monitors. However, the imperfect mechanism underlying cuffless BP measurement leads to unsound estimation models. Sensing technology and clinical validation issues further contribute to the limited accuracy of this technology that confines its wide application. As such, it would be very valuable to clarify the mechanism of cuffless BP by investigating the fundamental relationship between wearable PPG and other modal signals with BP, to make the sensing system robust to interference, to build models that can accurately estimate BP, and to standardize the clinical validation protocol, for promoting the TAG technology into practice.This work is supported by Sichuan Science and Technology Program (2021YFH0179) and InnoHK scheme by ITC.17.Department of Engineering Science, University of Oxfordet alet alet alet alet alet alet alet alet alet alet alet alet alet alet alet alet alet alet alet alA pulse oximeter has long been considered a routine measurement device for patient monitoring in clinical settings (Tarassenko 2) can then be calculated using proprietary software. The black-box approach using proprietary software to extract SpO2 or other physiological signals makes PPG-based wearable devices unattractive as they are usually not generalisable to all patients with different age groups and diseases. Furthermore, there are currently only a handful of devices on the market that provide access to the PPG waveforms, with the cost of those devices limiting the use of PPG in low-resource settings. Signal quality issues such as motion artefacts and data connectivity, and device battery life, are further barriers to the use of PPG in clinical monitoring. In addition, despite active research into techniques for measuring the signal quality of PPG waveforms, such techniques are mostly tested in healthy subjects  and on only a small number of participants, which makes it difficult to generalise findings to clinical settings. For long-term monitoring of bed-bound patients, existing PPG-based devices are limited by battery life and Bluetooth connectivity issues.Despite the flexibility and low-cost advantages of PPG, its applications in patient monitoring remain limited due to multiple challenges from data collection to disease detection. Most commercially available wearable devices capture PPG in the background, where the signal is unprocessed and noisy depending on the connectivity of the device and the patient’s movement. Vital signs such as oxygen saturation  Oxford Biomedical Research Centre (BRC) and by the Hong Kong Centre for Cerebro-cardiovascular Health Engineering (COCHE) funded by the Hong Kong ITC. The views expressed are those of the authors and not necessarily those of InnoHK-ITC. TZ was supported by the Royal Academy of Engineering under the Research Fellowship scheme. DAC acknowledges support from the Oxford Suzhou Centre for Advanced Research (OSCAR). DAC was supported by a Royal Academy of Engineering Research Chair; an NIHR Research Professorship; the NIHR Oxford Biomedical Research Centre; and the InnoHK Hong Kong Centre for Centre for Cerebro-cardiovascular Engineering (COCHE). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health or ITC.18.1Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom2Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom3Department of Physics/Electro-Optic Engineering, Lev Academic Center, Jerusalem, Israel4Southern University of Science and Technology, Shenzhen, People’s Republic of ChinaThe PPG signal is composite in nature with a heartbeat synchronous pulse superimposed on a range of lower frequency components attributed to respiration, vasomotion, autonomic nervous system activity and thermoregulation Allen . Valuablet alAssessment of PPG variability has included studies of the low frequency pulse amplitude, timing changes and/or morphology changes which are also likely to be linked with autonomic function in humans Allen . Particuet alet alet alet alet alet alet alIn several studies the VLF fluctuations of the PPG amplitude and baseline were studied in the time domain. The VLF variability of the baseline and amplitude showed high correlation between right and left limbs and between fingers and toes of healthy adults , mental health and wellbeing, and for unique and challenging environments such as micro-gravity/space and military research , and validated for use in specific applications. There are clear challenges however to overcome. For example, in order to reliably assess the lower PPG variability frequencies, accurate low-artefact waveforms are needed for feature analysis and quantification of signal dynamics. The lack of standardisation in PPG measurements and analysis is also a concern as it can limit generalisability of a test beyond the centre that it was developed in. Three pressing challenge areas for future research are (i) sensing, (ii) measurements, data and analysis, and (iii) translation to use in clinical applications.•The optimal body sites for autonomic function assessments are not yet known. Skin PPG measurements are often made at the extremities, but other sites are sometimes needed—e.g. at the forehead which is regulated by both sympathetic (‘SNS’) and parasympathetic (‘PSNS’) branches, and for when scalp access allows, to assess PPG variability there that could be linked to cerebral tissue.•PPG sensor attachment needs to be reliable. Designs should minimise ‘probe-tissue’ movement artefact, which is very important to help reduce noise, especially if ambulatory measurements are to be performed.•Technology miniaturisation for portability could include integrating all system components on a wearable sensor itself, but this would not be trivial to achieve.•Calibration methods are a challenge, e.g. pin-pointing specific SNS function from the measured parameters of the VLF PPG variability.•The measurements of VLF PPG variability in preterm newborn babies are of utmost importance because early detection of central or peripheral SNS dysfunction enables effective treatment, but their spontaneous limbs movements cannot be controlled. Efficient signal analysis techniques are required to reliably remove patient movement related noise.•There are not yet internationally standardised measurement protocols for specific PPG autonomic assessments. Challenges include introducing and getting technologies clinically accepted.•Developing analysis algorithms is challenging. There will be variability and uncertainty in measurements linking to mental state, i.e. psychophysiological changes in PPG.•et alet alAs the heart-beat period and consequently heart rate can be measured by PPG  PPG fluctuations, as well as extracting the ANS component of HF PPG fluctuations .•Challenges in developing representative physiological models describing the generation and modulation of the PPG, and its interaction/regulation with other measurements.•Training in PPG technology and particularly for the study of VLF/ULF is limited.•Reliably measuring and understanding correlations between VLF PPG fluctuations in two limbs, for the assessment of ANS dysfunction. This approach is important for diabetic patients , for preterm neonates , and for elderly persons . An important question is what the ground truth could be for such assessments.•Challenges with reliably monitoring ANS during sleep.•ANS assessment in patients with orthostatic hypotension.•There are many challenges to extreme environment monitoring, such as space exploration and sub-sea diving.•Different body skin sites are differently regulated by the SNS and PSNS. More scientific studies are needed to understand the impact of measurement factors . This could include computational studies of light interaction with tissue.•Sensor materials, i.e. wearable/flexible technologies must be considered, including skin-inspired organic electronics and skin-interfaced sensors.•Technology miniaturisation can be made and at low cost.•Calibration in BP low frequency variability assessments—the next generation ‘cuffless’ BP technologies to be developed.•Robust standardised measurement protocols: repeatability and reproducibility data are needed for measurements from wearable sensing solutions.•To facilitate sharing of PPG datasets collected under various conditions and open-sourced standardised PPG processing code for multi-centre comparative studies.•To better understand PPG noise, artefact rejection and resilience. This could include AI techniques to facilitate this.•Differentiation between LF and VLF (and ULF) fluctuations. Explainable AI could have a role in developing and validating different autonomic function tests based on PPG, and to help explain the signals.•Short time series analysis techniques exist from other science and engineering fields, hence there is scope for multi-disciplinary research and development to improve algorithms enabling reliable biomarker extraction and tracking over time.•Advances in machine learning techniques including deep learning lends itself to the utilisation of ‘in the cloud’ analysis of the PPG signal.•Mathworks Inc.), to develop physiological models describing the generation and modulation of the PPG, and its interaction/regulation with other measurements.Utilise computer simulation software, e.g. Simulink  is a series of diseases caused by heart and blood vessel lesions and is the leading cause of death worldwide. These deaths are mainly attributed to the long-term effects of atherosclerosis , brachial-ankle pulse wave velocity (ba-PWV), and carotid-femoral pulse wave velocity (cf-PWV). In clinical practice, cf-PWV is the gold standard for non-invasive assessing arterial stiffness  has provided a convenient method for assessing vascular function. Some researchers have developed technologies to simultaneously collect PPG signals from different body sites to obtain PWV. In addition, PPG waveform features could also be analysed to assess arterial compliance and vascular age. Manufacturers have implemented PPG technologies into different formats of portable devices: smart watches, smart headphones, smartphones, etc .•Calculation of pulse transit time (PTT) from the simultaneous acquisition of multiple PPG signals, or pulse arrival time (PAT) from an ECG signal and a PPG signal.Although, in recent years, the measurement of PWV has been widely used to evaluate arterial stiffness for clinical research, this approach has not been widely adopted for clinical use. There are currently two main methods for assessing vascular age and arterial compliance by PPG figure :•PPG pulet alet alet al   and measurement posture. Calculating PAT from the ECG and PPG has been widely investigated. However, PAT is the sum of the PTT and pre-ejection period (PEP), making it challenging to obtain a PTT from PAT measurements that truly reflect vascular function changes. Finally, the vast majority of studies have been mainly conducted on healthy individuals, lacking clinical validation against atherosclerosis  could also be used to eliminate the noise. Third, PPG signal quality evaluation standards should be established to determine the classification criteria of different quality PPG signal, and to select the appropriate features for specific applications in assessing vascular age and arterial compliance. Fourth, in order to avoid the effect of PEP on PAT, the ECG could be replaced by other physiological signals (such as the Seismocardiogram (SCG), Ballistocardiogram (BCG), or impedance cardiography (ICG)). Fifth, when assessing vascular age and arterial compliance by regression models or deep learning, separate assessment models can be specifically developed for different sites, postures, and people with different skin pigmentation. Sixth, in order to improve the generalisability, robustness, and evaluation accuracy of the models, a variety of datasets from different populations should be developed to provide enough data for validation. Seventh, the extracted PPG features need to be validated in clinical practice, including in patients with different diseases such as atherosclerosis and diabetes. Finally, for PPG sensors, the relative position of the LED to the photodiode should also be investigated to improve the quality of the PPG signal, which can be used to develop medical devices for assessing vascular age and arterial compliance.et alWearable PPGs are emerging as a potential tool for assessing vascular age and arterial compliance, and more attention has been paid to the use of wearable devices for telemonitoring since the start of the COVID-19 pandemic  is a vascular optics technique that can provide composite information about the micro- and macro-circulation Allen . PPG haset alet alPAD usually affects the lower limb arteries  and/or vascular ultrasound (VU) or imaging with contrast CT or MRI angiography. PPG is not currently routinely used, despite its potential advantages of speed, low cost, and minimal training requirements. PPG PAD technology can be miniaturized, with portable devices being developed for clinical settings such as primary care diagnostics. PPG diagnostics have tended to be restricted to non-ambulatory assessments, which limit test applicability and may potentially miss valuable diagnostic/predictive information about the peripheral circulation.To our knowledge wearable sensing systems do not currently provide PAD diagnostics but we believe there is a potential role for them. The main current clinical need is for rapid single test in older patients with leg pain, utilising portable equipment suitable for use in primary care and non-hospital environments and for community screening. Wearable sensors, in a shoe or sock for example, could be also be relevant in patients who have undergone interventions, such as bypass grafting or stenting for PAD, to provide monitoring for graft failure or complications and in those with borderline perfusion in the distal limb/foot due to PAD, often in combination with microvascular disease, such as in diabetics, where there may be variation during the day with posture and blood pressure changes and a high risk of foot ulceration or necrosis. Such patients may develop ischaemic foot complications without warning and if high risk could be monitored by a wearable sensor system.There are three pressing areas for future research in PAD diagnostics with PPG: (i) sensors (e.g. wearable devices), (ii) measurements, data and analysis , and (iii) clinical application of the PPG technologies in PAD. Algorithms need to work reliably when there are co-existent conditions such as diabetes and/or cardiac arrhythmias, such as atrial fibrillation (AF). Test cost acceptability and additional knowledge of the barriers and facilitators of technology adoption could aid the drive toward improving standardization in PPG measurements. Understanding what makes the PPG pulse damped in vascular disease is still not fully understood, but further analytics and modelling including using ambulatory measurements should help boost our knowledge in this area.•Optimal body sites for PAD detection are not yet known. Measurements are usually made on the skin at the (great) toe pads, but other sites are needed for ambulatory measurements . There should also be consideration of angiosomes, where different parts of the leg/foot are supplied from different blood vessels, so testing just one toe or one position may not always be sufficient. With all this in mind one would expect consumer devices not to be sufficient and that tailored clinical wearables may be required for PAD assessments.•The PPG sensor–tissue attachment needs to be reliable, repeatable, and safe, but there is no standardization currently in probe design. There are challenges to miniaturization onto a wearable sensor for portability. A range of potential wearable sensor form factors would need evaluating to home in on the ideal sensor probe design for PAD assessments.•et alCuffless blood pressure (BP) measurement using PPG is a vogue area, with validations so far done for arm (finger and wrist) measurement sites  for PAD detection algorithms that also involve a microvascular disease component remain unknown.•Reliable PAD detection is needed in cases with co-existent diabetes mellitus and/or cardiac arrhythmia.•Test result communication to the patient and operator also requires further refinement including borderline positive results that may need further investigation.•A barrier to advancing the technology could come from the lack of understanding of the nature of the PPG pulse and why it becomes damped in vascular disease.•et alAlgorithm optimization is needed for sensitivity to early disease. Disease severity is likely to be different for different measurement settings, e.g. hospitals, primary care, and home-based assessments, and a ‘real-world’ device would need to account for this .•Sensor attachment: wearable/flexible technologies for the sensor must be considered, to allow measurement during ambulation which may improve accuracy.•Technology miniaturization: miniaturization must be possible at a low cost and with ‘lab on a chip’ sensing capability.•Lower limb ABPI from (great) toe PPG is conceptually possible; this would include using bespoke sensor design and machine learning techniques for cuffless BP’s.•Standardized measurement protocols: interlinking sensor design, data analytics, algorithm noise resilience, patient acceptability, and training will be complex. Protocols also need adequate validation and should be co-created with public, patient, technologist, and clinician inputs. Novel disease-detection communication methods should be explored.•Training in the use of PPG technology is limited. Ideally, measurements should be automated and devices designed to be as resilient and operator-independent as possible.•Adequate normative data across age, gender, and ethnicity should be collected, and a reliable transferable measurement protocol should be used.•Optimized noise resilience techniques should be developed and applied to ambulatory measurements.•Physiological models describing the generation and modulation of PPG and its components, as well as the interaction/regulation between PPG and other measurements, should also be considered figure . It is v•Advances in machine learning techniques, including deep learning and explainable artificial intelligence, are likely to have a significant role in the future in helping to classify and understand PPG signals.•It is vital that the algorithms are developed and tested for a range of clinical settings . It is possible that different PPG features will be best suited to specific clinical settings and disease prevalence.•Algorithms should be developed with macro- and microvascular diseases in mind, and PPG data sets should be collected using protocols addressing both strands.•Resilience algorithms need to be developed that can cope with the wide range of patients to be seen.•Standardization in test protocols is needed for reliable testing, reporting, and interpretation.•et alModelling and simulation could help in our understanding of what actually is being measured and why the pulse often becomes damped and delayed with vascular disease  waveform analysis—is being pursued by many for blood pressure (BP) monitoring  via transmission-mode infrared sensing . Such a database may be straightforward to establish for the hypotension surveillance application, as many surgical and intensive care patients are hemodynamically unstable and receive invasive BP monitoring for routine care. Note that popular databases (e.g. MIMIC) may not suffice, as the finger clip PPG waveforms therein are heavily processed and likely not representative of PPG waveforms from other sites  can be obtained from which to extract waveform features. This analysis goes beyond the assessment of pulse peaks, potentially allowing continuous monitoring of several health-related metrics. The photoplethysmogram (PPG) waveform and its derivatives provide a wealth of information on cardiac functionality , and, as such, do not require regulatory approval. For example, in the United States, currently less than ten among all wearable devices have received clearance for specific functionalities, from the Food and Drug Administration (FDA)  monitoring for the management of hypertension. Accurate and precise measurements, which are paramount when establishing diagnosis and treatment, are largely elusive due to sources of error in the PPG readings. As a result, personalized and repeated calibration of the PPG sensors through alternative systems (e.g. sphygmomanometer) are needed but are inconvenient and costly.et alet alet alThere are contradictory reports on the influence of skin tone on the functionality of wearable photoplethysmography. Many PPG sensors utilize green light, which is highly absorbed by hemoglobin and epidermal melanin, which give the skin its tone. While some studies show significant error in heart rate estimation  to use photoplethysmography for the characterization of blood pressure and other metrics for the management and prevention of cardiovascular disease.Further characterization of the error associated with the biological variables listed above is a priority. Specifically, better statistics and stratification of subjects are necessary when addressing skin tone, obesity, age, and gender. To date, our knowledge of the absorption coefficient of the epidermis in populations with elevated skin tone  is lacking and based on studies with extremely limited number of individuals Jacques . SimilarUsing longer wavelengths beyond the PPG’s standard green light sources could alleviate some issues related to skin variability in tone, thickness, and perfusion. Near-infrared light travels deeper into the tissue as its absorption by melanin and hemoglobin is lower. This, combined with an intelligent selection of source and detector separation, could be used to improve the signal. Similarly, using multiple wavelengths could prove helpful by probing different tissue depths and isolating various skin layer influences  (#1648451).23.1Department of Biomedical Engineering, Duke University, Durham, NC, United States of America2Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, United States of America3Duke Clinical Research Institute, Durham, NC, United States of Americaet alDigital health is a broad scope term encompassing mobile health, wearable devices, health information technology, telehealth and telemedicine, and personalized medicine Health 2020). Biometric Monitoring Technologies (BioMeTs) are digital health tools that process data captured by mobile sensors and use algorithms to generate measures of behavioral and/or physiological function  that is not a direct representation of the original analog signal. For example, ‘heart rate’ and ‘inter-beat interval’ are two processed data types that can be obtained from sample-level data . Processed data are usually calculated through two main steps, where step one involves preprocessing of the sample-level signal and step two involves directly modeling or estimation of the target metric, such as heart rate. The preprocessing step is usually an ensemble of different techniques, including filtering, detrending, transformations, outlier detection and missing value imputations. It is important to note that the processed data are not a direct representation of the original analog signal measured by the sensor; instead, an algorithm was applied to produce that new type of data.Digital biomarkers are calculated using BioMeTs prediction algorithms, and these are typically developed through a two-part process of training and testing models to predict a target variable using the sensor data as an input. Common predictive modeling tasks using BioMeT data include regression (continuous target variable) and classification , with a common pipeline of: (1) preprocessing and segmentation, (2) feature extraction and selection, and (3) predictive model building and testing figure . A commoMost consumer wearable BioMeT manufacturers do not provide sample-level sensor data, but instead provide processed data as aggregate metrics. For example, Apple recently developed a Food and Drug Administration-cleared algorithm for binary (yes/no) detection of atrial fibrillation using the Apple Watch wrist-based electrocardiogram (ECG) and PPG. These aggregate metrics may themselves act as digital biomarkers, or the metrics may be used and combined by researchers and clinicians to develop composite digital biomarkers.et alet alet alet alet alOne of the major challenges faced by the research and medical communities is that BioMeT manufacturer-developed algorithms are nearly always proprietary and information about their verification and validation is often not released to the public  from PPG and/or movement data. Investigations applying predictive modeling to BioMeT data are far from exhausted. In recent years, deep learning models have become increasingly popular, achieving great performance in many areas of biomedical applications  , Open Wearables Initiative (OWEAR), All of Us research program by NIH) and to standardize digital health data . We need to continue and expand our efforts to advance the state-of-the-art of BioMeTs and digital biomarker discovery, including the adoption of benchmark datasets for algorithm comparison, and to bridge gaps in evaluation and applications across academia, government, and industry to establish mobile and digital health as an evidence-based field worthy of our trust.Other challenges faced by BioMeTs include a lack of regulatory oversight, limited funding opportunities, cost of computation and storage, lack of standards and validation methods, general mistrust of sharing personal data, and a shortage of open-source data and code. The progress in the field of wearable health data has been further stymied by a lack of cohesion across related research endeavors. Technological advancements in hardware development to miniaturize sensors and to add on-board computation ability and in signal processing to improve algorithms and to compress data to reduce storage requirements and ease of transporting volumes of data between systems can improve the challenges with cost of computation and storage. There have been several notable efforts from academia, industry, and funding agencies to integrate and coordinate efforts in sharing BioMThe accessibility of mobile and wearable technology affords an unprecedented opportunity to provide healthcare globally, conveniently, and to populations with limited healthcare accessibility such as low-income and rural populations. Mobile and digital health monitoring and interventions are promising because they can improve the health monitoring of patients who are unable to make frequent visits to a healthcare facility. Tools that allow for collaboration in improving algorithms, validating known digital biomarkers, and discovering new digital biomarkers will enable much-needed standardization and interoperability in this space.24.Research Centre for Biomedical Engineering, City, University of London, London EC1V0HB, United Kingdom2). Over the past few decades, there has been a plethora of research in the field of PPG with potential applications beyond pulse oximetry, especially with the recent growth of wearable technologies utilising the technique of PPG. Despite the widespread use and acceptability of PPG, still the origin of the PPG signal has been the subject of continuing discussion and debate  is a non-invasive optical technique widely used for studying and monitoring the pulsations associated with changes in blood volume in a peripheral vascular bed . In general terms, this relative change in light absorbance gives rise to the PPG pulsatile waveform synchronous with each heartbeat  the red blood cell (RBC) orientation and deformation, (b) the volumetric distribution of the absorbers and blood volume variations, and (c) the mechanical movements of the capillariess.Some of the initial findings in this subject suggested that the changes in red blood cell orientation with the cardiac cycle are the leading causes of forming a PPG wave. This hypothesis is based on the electrophysiological characteristics, where at the end of diastole (i.e. low blood flow), the red blood cells orient themselves randomly due to reduced shear stress; as blood flow increases, the red blood cells tend to align themselves along with the flow; and during systole, the alignment is parallel to the direction of the flow  is an optical technique that can detect blood volume changes in the microvascular tissue bed. By using a pair of light emitting diode (LED) and detector, the pulsation of blood at a peripheral site  can be measured, digitized and displayed in details.et alet alet alThe research on PPG can be dated back to the 1930s Allen ; howeveret alAlthough PPG has been proposed independently to measure blood flow, blood pressure, and blood content analysis, these physical and biochemical quantities in fact represent different aspects of a vascular system. PPG, even with multi-wavelength analysis, may not be able to reflect the entire complex mix-up of the peripheral vasculature. In many cases, the measurement of one parameter is based on the assumption that many other parameters remain constant during the measurement period. This is an assumption that often holds valid only when the biological system is under certain stable conditions, but may be weakened when the body is in a disease state or during acute and transient phases. Understanding the vasculature using PPG is a big research challenge, as the vasculature involves not only the mechanical and physical properties of the vessels that give the pulsatile blood volume changes captured by PPG, but also many other components. For example, some other important aspects that must be considered include: the electrolyte analysis of the salts and minerals found in blood; the electrical impulses generated and communicated with other organ systems; and the protein-rich fluid that transport back and forth the circulatory system and lymphatic vessels to serve the important immune surveillance function. Alternatives to PPG can therefore provide different angles to understand the vasculature and its biology, for example, to understand systemic atherosclerotic disease  or acceleration PPG uses the second derivative of the waveform of a digital PPG to stabilize the baseline and to separate components of the waveform more easily .The authors Jing Liu and Ni Zhao have a non-provisional patent (US10542894B2) for the multi-wavelength photoplethysmography-based techniques for cardiovascular monitoring.No new data were created or analysed in this study.


Fermented foods from different raw materials  have been consumed by humanity for over 10,000 years . DiffereDifferent groups of volatile compounds have been studied as responsible for specific sensory properties of fermented foods. Alcohols and esters, some of the main contributors to the flavor of beer, sake, and wine, are produced by yeasts during the fermentation process and give the beverages their characteristic fruity, candy, and perfume-like aromas . VolatilTraditional fermentation processes, such as those conducted by households, small producers, and restaurants, and also precision fermentation processes developed by industry, could be improved by gaining knowledge on how to control and modulate the generation of specific volatile compounds. The utility of bioinformatics and advanced statistical and artificial intelligence tools have also been suggested as innovative approaches to better understand and predict how different microorganisms will transform raw materials into new aromas . The rolBecause of all these reasons, the present Special Issue collects a set a set of manuscripts that present data on (1) the volatile composition of fermented foods, either made using traditional or advanced biotechnological methods; (2) different methods to correctly identify and quantify volatile compounds during fermentation processes; (3) the relationships among volatile compounds and perceived aromas in fermented products; and (4) other aspects of food fermentation that involve its volatile composition. Therefore, this Special Issue will be of interest to food quality control professionals, food designers, sensory and consumer scientists, and fermented food producers (such as brewers and chefs) who are interested in learning more about how to control and improve the aroma of their products.


Guiding the development of longitudinal competencies in communication, ethics and professionalism underlines the role of portfolios to capture and evaluate the multiple multisource appraisals and direct personalised support to clinicians. However, a common approach to these combined portfolios continues to elude medical practice. A systematic scoping review is proposed to map portfolio use in training and assessments of ethics, communication and professionalism competencies particularly in its inculcation of new values, beliefs and principles changes attitudes, thinking and practice while nurturing professional identity formation. It is posited that effective structuring of portfolios can promote self-directed learning, personalised assessment and appropriate support of professional identity formation.Krishna’s Systematic Evidence-Based Approach (SEBA) is employed to guide this systematic scoping review of portfolio use in communication, ethics and professionalism training and assessment.PubMed, Embase, PsycINFO, ERIC, Scopus and Google Scholar databases.Articles published between 1 January 2000 and 31 December 2020 were included.The included articles are concurrently content and thematically analysed using the split approach. Overlapping categories and themes identified are combined using the jigsaw perspective. The themes/categories are compared with the summaries of the included articles in the funnelling process to ensure their accuracy. The domains identified form the framework for the discussion.12 300 abstracts were reviewed, 946 full-text articles were evaluated and 82 articles were analysed, and the four domains identified were indications, content, design, and strengths and limitations.This review reveals that when using a consistent framework, accepted endpoints and outcome measures, longitudinal multisource, multimodal assessment data fashions professional and personal development and enhances identity construction. Future studies into effective assessment tools and support mechanisms are required if portfolio use is to be maximised. The Systematic Evidence-Based Approach methodology adopts the structure of systematic reviews and flexibility of narrative reviews to synthesise a reproducible and accountable evaluation of diverse methodological sources, settings, physician populations and specialities.This review evaluates the impact of longitudinal development of communications, ethics and professionalism competencies and their impact on professional identity formation.Given that communications, ethics and professionalism are sociocultural constructs, geopolitical sociocultural differences may raise questions as to the applicability of these findings beyond the European and North American medical education systems.As the number of articles included is limited, the review’s ability to assess the long-term effectiveness of portfolios may be compromised.Evidence for the effective embodiment of ethical and professional principles, communication skills and appropriate use of empathy in clinical practice requires a longitudinal and often multisource perspective. With ethics and professionalism sharing longitudinal developmental trajectories and intimately entwined with communication skills and competencies, the three competencies are increasingly considered together. In practice longitudinal communication, ethics and professionalism (CEP) programmes appear in the curricula of the top 10 medical schools featured on the QS World University Rankings 2020.8–12However, while previous reviews into the teaching of ethics,25A Systematic Evidence-Based Approach (SEBA) guided Systematic Scoping Review (SSR) (henceforth SSR in SEBA) is proposed to map CEP portfolio use in medicine to guide the design, structuring and support of a proposed programme.16 27 28 35 41 48 51–5610.1136/bmjopen-2022-067048.supp1Supplementary dataAn expert team comprised of a medical librarian from the Yong Loo Lin School of Medicine (YLLSoM) at the National University of SingaporeWhat is known about CEP portfolios?’. The secondary research questions were ‘what role do CEP portfolios have in teaching and assessing CEP development?’.The research and expert team determined the primary research question: ‘A population, intervention, comparison and outcome (PICOs) format, outlined in 58 59The research team developed search strategies and carried out independent reviews for relevant publications in the PubMed, Embase, PsycINFO, ERIC, Scopus and Google Scholar databases  accessed through the National University of Singapore (NUS) library portal. These individual lists of titles were discussed online and Sandelowski and Barrosoarticulate, defend, and persuade others of the “cogency” or “incisiveness” of their points of view or show their willingness to abandon views that are no longer tenable’ was applied to achieve consensus on the final list of titles to be scrutinised.Six members of the research team created individual lists of titles to be included, while three other members of the research team carried out hand searches and ancestry searches of seven leading journals in medical education  were separated from grey literature, perspectives, editorials, letters and non-data-based articles drawn from bibliographic databases (henceforth non-data driven). These two groups were separately thematically analysed.The Best Evidence Medical Education Collaboration GuideThis SSR in SEBA suggest that overlaps in ethics, professionalism and communication skills, knowledge, attitudes and competencies reaffirm the concurrent training and assessments of CEP competencies in portfolios.86 120 121Yet to be effective, CEP portfolios require policing and underscore the need for trainedWith these requisites met, evidence of changes in CEP knowledge, skills and competencies suggests a shift in the thinking, attitudes and conduct of clinicians. The Krishna-Pisupati model posits that sustained changes in practice, beliefs, values and principles will result in changes in the clinician’s belief systems which, in turn, inform their self-concepts of identity and shape their PIF.The generalisability of the results of this study is limited by the identified studies selected for review. First, the majority of the papers included originate from Europe and North America. Given that CEP is a sociocultural construct, these geopolitical sociocultural differences raise questions as to the applicability of these findings beyond the European and North American medical education systems. Second, as the number of articles included is limited, the SSR in SEBA’s ability to assess the long-term effectiveness of portfolios may be compromised.The suggestion that CEP portfolios can capture, instruct and assess PIF requires further study. While there have been posits on how such evaluations could be made, such tools remain unrealised and must be the focus of coming studies. One possible starting point for the design of such tools may be theories such as the Krishna-Pisupati model, which attempts to link PIF to changes to belief systems and concepts of personhood. This may provide a grander view of PIF that spans the undergraduate and postgraduate settings and provide the basis for directing support and feedback for clinicians. As we look forward to furthering efforts to study the effects of CEP portfolios on the PIF of clinicians, we also believe the specific impact of these potentially resource-heavy and financially costly intervention should be at the centre of future initiatives.


Cancer mortality varies widely across Europe, and survival depends on where you live. In particular, the inequality between countries in Central and South-Eastern Europe (CEE) and Western Europe (WE) is striking. The COVID-19 pandemic has brought existing inequalities into sharp focus, and the economic disruption it has caused threatens to deepen them. The Central European Cooperative Oncology Group (CECOG) has created a platform with the aim to reduce health inequalities and to improve patient access to cancer care. The subject of discussion is the value of new treatments to create willingness to invest in improving cancer outcomes while managing the budget. The platform includes various stakeholders as scientific leaders, policy makers, payers, patients and industry. As recently noted, in the US cancer death rate continued to decline by 1.5% from 2019 to 2020, contributing to a 33% overall reduction since 1991 and an estimated 3.8 million deaths averted [Despite the positive news of declining cancer mortality rates future progress may be attenuated by rising incidence for breast, prostate, and uterine corpus cancers . Due to to reduce systemic health inequalities and improve patient access and funding to cancer care;to focus on prevention, screening, early diagnosis, access to state-of-the-art cancer diagnosis and treatment;to propose solutions which ensure progress in the implementation of cancer control plans.Local stakeholder involvement is critical for the success of policies addressing inequalities. Between 2018 and 2020 a multi-stakeholder group (CECOG GOIA group*) under the leadership of the Central European Cooperative Oncology Group (CECOG) convened a series of conferences in Vienna, Bucharest, Zagreb and Warsaw, with a three-fold objective.The purpose of the initiative, funded by CECOG, AstraZeneca and MSD, was to create a platform for stakeholder dialogue in the above-mentioned countries and to make recommendations for Europe’s Beating Cancer Plan. The outcome was the so-called CECOG Cancer Dashboard which identifies key metrics for Eastern European countries and shall contribute to the “Inequalities Registry” in the context of the European Commission’s “Europe’s Beating Cancer Plan” .This document includes prevention , diagnostics , treatments (e.g. time to access), clinical research  and involvement of patient advocacy groups (e.g. definition of patient pathways). The dashboard is based on two publications of the CECOG initiative which analyze the current state of cancer care in Central and Eastern Europe in the EU and provide recommendations for change , 8.Another result of the CECOG exchange meetings was the view that the impact of cancer policies on cancer patients’ lives depends on two critical factors. First, cancer control policies must be aligned with and integrated in public health strategies, particularly those addressing prevention related to disease prevention. Between 30 and 50% of all cancer cases are preventable . PreventSecond, besides integration in public health strategies cancer policies are successful if they are guided by objectives, create transparency and are driven by consensus-based actions. The Cancer Inequalities Registry of Europe’s Beating Cancer Plan will provide data points to inform activities , which iThe so-called European Semester process may serve as a role model or may integrate health outcomes targets including cancer . This Euit calls on stakeholders to agree on key indicators and targets;it requires to create and collect the data and to measure progress; andit triggers improvement at local level.A European Semester process for cancer would not only map the status quo but also ensure progress in cancer outcomes:A simple example may illustrate the last point: although nearly all Eastern European countries have National Cancer Control Plans (NCCP) in place, differences still exist compared to other European countries because of a lack of implementation and measuring progress. A recent study showed that breast cancer screening coverage is still lowest in Eastern European countries (49%). If the maximum of full coverage was reached, 23% of breast cancer deaths could be prevented in Eastern countries, two times as much as Northern countries .Europe’s Beating Cancer Plan, launched in 2021, represents a unique new opportunity for Central and Eastern European countries, considering the challenge cancer will be for future societies in Europe . The mid