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<p>The last two decades have shown a boom in the field of wearable sensing technology. Particularly in the consumer industry, growing trends towards personalized health have pushed new devices to report many vital signs, with a demand for high accuracy and reliability. The most common technique used to gather these vitals is photoplethysmography or PPG. PPG devices are ideal for wearable applications as they are simple, power-efficient, and can be implemented on almost any area of the body. Traditionally PPGs were utilized for capturing just heart rate, however, recent advancements in hardware and digital processing have led to other metrics including respiratory rate (RR) and peripheral oxygen saturation (SpO2), to be reported as well. Our research investigates the potential for wearable devices to be used for outpatient apnea monitoring, and particularly the ability to detect opioid misuse resulting in respiratory depression. Ultimately, the long-term goal of this work is to develop a wearable device that can be used in the rehabilitation process to ensure both accountability and safety of the wearer. This document details contributions towards this goal through the design, development, and evaluation of a device called “Kick Ring”. Primarily, we investigate the ability of Kick Ring to record heartrate (HR), RR, and SpO2. Moreover, we show that the device can calculate RR in real time and can provide an immediate indication of abnormal events such as respiratory depression. Finally, we explore a novel method for reporting apnea events through the use of several PPG characteristics. Kick Ring reliably gathers respiratory metrics and offers a combination of features that does not exist in the current wearables space. These advancements will help to move the field forward, and eventually aid in early detection of life-threatening events.</p>

The electro-optic technique of measuring the cardiovascular pulse wave known as photoplethysmography (PPG) is clinically utilised for noninvasive characterisation of physiological components by dynamic monitoring of tissue optical absorption. There has been a resurgence of interest in this technique in recent years, driven by the demand for a low cost, compact, simple and portable technology for primary care and community-based clinical settings, and the advancement of computer-based pulse wave analysis techniques. PPG signal provides a means of determining cardiovascular properties during the cardiac cycle and changes with ageing and disease. This thesis focuses on the photoplethysmographic signal for cardiovascular assessment. The contour of the PPG pulse wave is influenced by vascular ageing. Contour analysis of the PPG pulse wave provides a rapid means of assessing vascular tone and arterial stiffness. In this thesis, the parameters extracted from the PPG pulse wave are examined in young adults. The results indicate that the contour parameters of the PPG pulse wave could provide a simple and noninvasive means to study the characteristic change relating to arterial stiffness. The pulsatile component of the PPG signal is due to the pumping action of the heart, and thus could reveal the circulation changes of a specific vascular bed. Heart rate variability (HRV) represents one of the most promising quantitative markers of cardiovascular control. Calculation of HRV from the peripheral pulse wave using PPG, called pulse rate variability (PRV), is investigated. The current work has confirmed that the PPG signal could provide basic information about heart rate (HR) and its variability, and highly suggests a good alternative to understanding dynamics pertaining to the autonomic nervous system (ANS) without the use of an electrocardiogram (ECG) device. Hence, PPG measurement has the potential to be readily accepted in ambulatory cardiac monitoring due to its simplicity and comfort. Noncontact PPG (NPPG) is introduced to overcome the current limitations of contact PPG. As a contactless device, NPPG is especially attractive for physiological monitoring in ambulatory units, NICUs, or trauma centres, where attaching electrodes is either inconvenient or unfeasible. In this research, a prototype for noncontact reflection PPG (NRPPG) with a vertical cavity surface emitting laser (VCSEL) as a light source and a high-speed PiN photodiode as a photodetector is developed. The results from physiological experiments suggest that NRPPG is reliable to extract clinically useful information about cardiac condition and function. In summary, recent evidence demonstrates that PPG as a simple noninvasive measurement offers a fruitful avenue for noninvasive cardiovascular monitoring. Key words: Photoplethysmography (PPG), Cardiovascular assessment, Pulse wave contour analysis, Arterial stiffness, Heart rate (HR), Heart rate variability (HRV), Pulse rate variability (PRV), Autonomic nervous system (ANS), Electrocardiogram (ECG).

The aim of this diploma thesis is to assess the applicability of pulse wave propagation monitoring in the cardiovascular system in the field of prediction and early diagnosis of abdominal aortic aneurysm (AAA). The very first part is focused on description of heart and blood vessels with its pathological changes in presence of aneurysm. For this reason, current methods of monitoring and surgical treating of AAA were mentioned. Due to their difficult clinical use widely in the population, new methods based on pulse wave monitoring were presented. Using an analytical approach we estimated the difference in the arrival of the pulse wave at measurable locations between healthy and pathological aorta in the order of miliseconds. By experimental monitoring using photoplethysmographic sensors, we observed significant changes of pulse wave velocity with respect to the mechanical properties of the artery wall (mainly associated with age), which we tried to implement by hyperelastic material models used in computational simulations of pulse wave proagation on simplified geometries by fluid structure interaction method. These analyzes should verify applicability of FSI simulations in further development of diagnostic methods of AAA.

Camera-based photoplethysmography (cbPPG) is a novel measurement technique that allows the continuous monitoring of vital signs by using common video cameras. In the last decade, the technology has attracted a lot of attention as it is easy to set up, operates remotely, and offers new diagnostic opportunities. Despite the growing interest, cbPPG is not completely established yet and is still primarily the object of research. There are a variety of reasons for this lack of development including that reliable and autonomous hardware setups are missing, that robust processing algorithms are needed, that application fields are still limited, and that it is not completely understood which physiological factors impact the captured signal. In this thesis, these issues will be addressed. A new and innovative measuring system for cbPPG was developed. In the course of three large studies conducted in clinical and non-clinical environments, the system’s great flexibility, autonomy, user-friendliness, and integrability could be successfully proven. Furthermore, it was investigated what value optical polarization filtration adds to cbPPG. The results show that a perpendicular filter setting can significantly enhance the signal quality. In addition, the performed analyses were used to draw conclusions about the origin of cbPPG signals: Blood volume changes are most likely the defining element for the signal's modulation. Besides the hardware-related topics, the software topic was addressed. A new method for the selection of regions of interest (ROIs) in cbPPG videos was developed. Choosing valid ROIs is one of the most important steps in the processing chain of cbPPG software. The new method has the advantage of being fully automated, more independent, and universally applicable. Moreover, it suppresses ballistocardiographic artifacts by utilizing a level-set-based approach. The suitability of the ROI selection method was demonstrated on a large and challenging data set. In the last part of the work, a potentially new application field for cbPPG was explored. It was investigated how cbPPG can be used to assess autonomic reactions of the nervous system at the cutaneous vasculature. The results show that changes in the vasomotor tone, i.e. vasodilation and vasoconstriction, reflect in the pulsation strength of cbPPG signals. These characteristics also shed more light on the origin problem. Similar to the polarization analyses, they support the classic blood volume theory. In conclusion, this thesis tackles relevant issues regarding the application of cbPPG. The proposed solutions pave the way for cbPPG to become an established and widely accepted technology.

Photoplethysmogram (PPG) signals detect blood volume variations during the heart cycle. They are useful to track physiological parameters of an individual, such as heart rate, heart rate variability or oxygen saturation. They are typically obtained using smart wearables and pulse oximeters, but our goal is to create remote PPG (rPPG) signals from video cameras. Since the signals obtained from a video camera are the RGB channels, we carried out an empirical study of the performance of each channel. RGB channels can be used to generate rPPG signals, but also as input to other processes that do so. As reference ground truth, we use contact PPG (cPPG) readings from pulse oximeters in the fingertip. In terms of several metrics, including dynamic time warping (DTW), Pearson’s correlation coefficient, root mean squared error (RMSE), and Beats-per-minute Difference (|∆BPM|), the green channel produced the best results, followed by the blue and red channels. Despite the green channel consistently outperforming the blue and red channels, the outcomes varied greatly depending on the dataset. We also applied different methods to obtain rPPG signals from the RGB channels, including CHROM-based rPPG, local group invariance (LGI), and plane-orthogonal-to-skin (POS). These techniques were contrasted with our novel technique based on a machine learning approach. For that, we made use of a variety of architectures, including convolutional neural networks and long short-term memory. The results were favourable for the ML approach in terms of DTW, r and |∆BPM|. / Fotopletysmogram (PPG)-signaler upptäcker variationer av blodvolym under hjärtcykeln. De är användbara för att spåra fysiologiska parametrar för en individ, såsom hjärtfrekvens, hjärtfrekvensvariabilitet eller syremättnad. De erhålls vanligtvis med smarta bärbara sensorer och pulsoximetrar, men vårt mål är att skapa fjärr-PPG (rPPG)-signaler från videokameror. Eftersom signalerna erhållna från en videokamera är RGB -kanalerna genomförde vi en empirisk studie av prestandan för varje kanal. RGB-kanaler kan användas för att generera rPPG-signaler, men också som input till andra processer som gör det. Som referens använder vi kontakt-PPG (cPPG) avläsningar från pulsoximetrar i fingertoppen. När det gäller flera mätvärden, inklusive Dynamic Time Warping (DTW), Pearsons korrelationskoefficient, Root Mean Squared Error (RMSE) och Beats-Per-minut-skillnaden (|∆BPM|). Uppnåddes bästa resultat med den gröna kanalen, följt av de blå och röda kanalerna. Trots att den gröna kanalen konsekvent överträffade de blå och röda kanalerna varierade resultaten mycket beroende på datasetet. Vi använde också olika metoder för att erhålla rPPG-signaler från RGB-kanalerna, inklusive CHROM-baserad rPPG, lokal gruppinvarians (LGI) och plan-ortogonal-till-hud (POS). Dessa tekniker kontrasterades med vår nya teknik baserat på en maskininlärningsstrategi. För det använde vi en mängd olika arkitekturer, inklusive konvolutionella neurala nätverk och LSTM-nätverk. Resultaten var gynnsamma för ML-metoden när det gäller DTW, R och |∆BPM|.

Objective. After skin flap transplants, perfusion strength monitoring is essential for the early detection of tissue perfusion disorders and thus to ensure the survival of skin flaps. Camera-based photoplethysmography (cbPPG) is a non-contact measurement method, using video cameras and ambient light, which provides spatially resolved information about tissue perfusion. It has not been researched yet whether the measurement depth of cbPPG, which is limited by the penetration depth of ambient light, is sufficient to reach pulsatile vessels and thus to measure the perfusion strength in regions that are relevant for skin flap transplants. Approach. We applied constant negative pressure (compared to ambient pressure) to the anterior thighs of 40 healthy subjects. Seven measurements (two before and five up to 90 min after the intervention) were acquired using an RGB video camera and photospectrometry simultaneously. We investigated the performance of different algorithmic approaches for perfusion strength assessment, including the signal-to-noise ratio (SNR), its logarithmic components logS and logN, amplitude maps, and the amplitude height of alternating and direct signal components. Main results. We found strong correlations of up to r = 0.694 (p < 0.001) between photospectrometric measurements and all cbPPG parameters except SNR when using the green color channel. The transfer of cbPPG signals to POS, CHROM, and O3C did not lead to systematic improvements. However, for direct signal components, the transformation to O3C led to correlations of up to r = 0.744 (p < 0.001) with photospectrometric measurements. Significance. Our results indicate that a camera-based perfusion strength assessment in tissue with deep-seated pulsatile vessels is possible.

Heart rate can say a lot about a person's health. While most conventional methods for heart rate measurement require contact with the subject, these are not always applicable. In this thesis, a non-invasive method for pulse detection is implemented and analyzed. Different signals from the color of the forehead—including the green channel, the hue channel and different ICA and PCA components—are inspected, and their resulted heart rates are weighted together according to the significance of their FFT peaks. The system is tested on videos with different difficulties regarding the amount of movement and setting of the scene. The results show that the approach of weighting measurements from different signals together has great potential. The system in this thesis, however, does not perform very well on videos with a lot of movement because of motion noise. Though, with better, less noisy signals, good results can be expected. / En människas puls säger en hel del om dennes hälsa. För att mäta pulsenanvänds vanligtvis metoder som vidrör människan, vilket iblandär en nackdel. I det här examensarbetet tas en metod för pulsmätningpå avstånd fram, som endast använder klipp från en vanlig videokamera. Färgen i pannan mäts och utifrån den genereras flera signalersom analyseras, vilket resulterar i olika mätvärden för pulsen. Genomatt värdera dessa mätvärden med avseende på hur tydliga signalernaär, beräknas ett viktat medelvärde som ett slutgiltigt estimat på medelpulsen. Metoden testas på videoklipp med varierande svårighetsgrad,beroende på hur mycket rörelser som förekommer och på vilketavstånd från kameran försökspersonen står. Resultaten visar att metodenhar mycket god potential och att man kan man förvänta sig finaresultat med bättre, mindre brusiga signaler.

In the context of health improving, the measurement of vital parameters such as heart rate (HR) can provide solutions for health monitoring, prevention and screening for certain chronic diseases. Among the different technologies for HR measuring, photoplethysmography (PPG) technique embedded in smart watches is the most commonly used in the field of consumer electronics since it is comfortable and does not require any user intervention. To be able to provide an all day and night long HR monitoring method, difficulties associated with PPG signals vulnerability to Motion Artifact (MA) must be overcome. Conventional signal processing solutions (power spectral density analysis) have limited generalization capability as they are specific to certain types of movements, highlighting the interest of machine learning tools, particularly deep learning (DL). Since DL models in the literature are trained on data from a different sensor than the internal sensor, transfer learning may prove unsuccessful. This work proposes a DL approach for estimating HR from wrist PPG signals. The model is trained on internal data with a greater demographic diversity of participants. This project also illustrates the contribution of multi-path and multi-wavelength PPG instead of the conventional single green PPG solution. This work presents several models, called DeepTime, with selected input channels and wavelengths: Mono_Green, Multi_Green, Multi_Wavelength, and Multi_Channel_Multi_Wavelength. They take temporal PPG signals as inputs along with 3D acceleration and provide HR estimation every 2 seconds with an 8-second initialization. This convolutional neural network comprised of several input branches improves the existing Withings internal method’s overall Mean Absolute Error (MAE) from 9.9 BPM to 6.9 BPM on the holdout test set. This work could be completed and improved by adding signal temporal history using recurrent layers, such as Long-Short-Term-Memory (LSTM), training the model with a bigger dataset, improving preprocessing steps or using a more elaborate loss function that includes a trust score. / I sammanhanget av förbättring av hälsouppföljning kan mätning av vitala parametrar som hjärtfrekvens (HR) erbjuda lösningar för förebyggande och screening av vissa kroniska sjukdomar. Bland olika tekniker för mätning av HR är fotoplethysmografi (PPG) integrerad i smartklockor den vanligast använda inom elektronikområdet eftersom den är bekväm och inte kräver något användaringripande. För att erbjuda en kontinuerlig HRövervakningsmetod utgör sårbarheten hos PPG-signaler för rörelseartefakter (MA) en stor utmaning. Konventionella signalbehandlingslösningar (analys av effektspektraltäthet) har begränsad generaliseringsförmåga eftersom de är specifika för vissa typer av rörelser, vilket betonar intresset för maskininlärningsverktyg, särskilt djupinlärning (DL). Eftersom DL-modeller i litteraturen tränas på data från en annan sensor än den interna sensorn kan överföringsinlärning vara misslyckad. Detta arbete föreslår en DL-ansats för att uppskatta HR från PPG-signaler på handleden. Modellen tränas på interna data med en större demografisk mångfald bland deltagarna. Detta projekt illustrerar även bidraget från flervägs- och flervågs-PPG istället för den konventionella enkla gröna PPG-lösningen. Detta arbete presenterar flera modeller, kallade DeepTime, med utvalda ingångskanaler och våglängder: Mono_Green, Multi_Green, Multi_Wavelength och Multi_Channel_Multi_Wavelength. De tar in temporära PPG-signaler tillsammans med 3D-acceleration och ger HR-uppskattning var 2:a sekund med en initialisering på 8 sekunder. Detta konvolutionella neurala nätverk, som består av flera ingångsgrenar, förbättrar den totala medelabsoluta felet (MAE) från 9,9 BPM (befintlig intern metod) till 6,9 BPM på testuppsättningen. Detta arbete kan kompletteras och förbättras genom att integrera den temporala historiken hos signalen med hjälp av återkommande lager (som LSTM), träna modellen på mer data, förbättra förbehandlingsstegen eller välja en mer sofistikerad förlustfunktion som inkluderar ett konfidensvärde.

Objective: The heart rate is an essential vital sign that can be measured remotely with camera-based photoplethysmography (cbPPG). Systems for cbPPG typically use cameras that deliver red, green, and blue (RGB) channels. The combination of these channels has been proven to increase signal-to-noise ratio (SNR) and heart rate measurement accuracy (ACC). However, many combinations remain untested, the comparison of proposed combinations on large datasets is insufficiently investigated, and the interplay with skin tone is rarely addressed. Methods: Eight regions of interest and eight color spaces with a total of 25 color channels were compared in terms of ACC and SNR based on the Binghamton-Pittsburgh-RPI Multimodal Spontaneous Emotion Database (BP4D+). Additionally, two systematic grid searches were performed to evaluate ACC in the space of linear combinations of the RGB channels. Results: Glabella and forehead regions of interest provided highest ACC (up to 74.1 %) and SNR (> -3 dB) with the hue channel H from HSV color space and the chrominance channel Q from NTSC color space. The grid searches revealed a global optimum of linear RGB combinations (ACC: 79.2 %). This optimum occurred for all skin tones, although ACC dropped for darker skin tones. Conclusion: Through systematic grid searches we were able to identify the skin tone independent optimal linear RGB color combination for measuring heart rate with cbPPG. Our results proved on a large dataset that the identified optimum outperformed conventionally used color channels. Significance: The presented findings provide useful evidence for future considerations of algorithmic approaches for cbPPG.