Licentiatavhandling (sammanfattning) Linköping : Linköpings universitet, 2009. / Härtill 2 uppsatser.

Recording and monitoring vital signs is an essential aspect of home-based healthcare. Using contact sensors to record physiological signals can cause discomfort to patients, especially after prolonged use. Hence, remote physiological measurement approaches have attracted considerable attention as they do not require physical contact with the patient’s skin. Several studies proposed techniques to measure Heart Rate (HR) and Heart Rate Variability (HRV) by detecting the Blood Volume Pulse (BVP) from human facial video recordings while the subject is in a resting condition. In this thesis, we focus on the measurement of HR. We adopt an algorithm that uses the Independent Component Analysis (ICA) to separate the source (physiological) signal from noise in the RGB channels of a facial video. We generalize existing methods to support subject movement during video recording. When a subject is moving, the face may be turned away from the camera. We utilize multiple cameras to enable the algorithm to monitor the vital sign continuously, even if the subject leaves the frame or turns away from a subset of the system’s cameras. Furthermore, we improve the accuracy of existing methods by implementing a light equalization scheme to reduce the effect of shadows and unequal facial light on the HR estimation, a machine learning method to select the most accurate channel outputted by the ICA module, and a regression technique to adjust the initial HR estimate. We systematically test our method on eleven subjects using four cameras. The proposed method decreases the RMSE by 27% compared to the state of the art in the rest condition. When the subject is in motion, the proposed method achieves a RMSE of 1.12 bpm using a single camera and RMSE of 0.96 bpm using multiple camera.

When it comes to calculating physical activity, nowadays a wide variety of tools is at our disposal.The phones used daily incorporate functions that calculate, for example, the number of steps taken throughout the day. Using the tools to set personal goals or to review which physical properties deviate. This work entails collecting data via a mobile camera using an application with the ability to measure luminance for the purpose of calculating heart rate. This thesis is done on behalf of Mobistudy, who wishes to calculate the heart rate during a common exercise test, the Queen’s College Step test. This is intended for use with a telephone held in the hand with the camera's index finger throughout the period, and the light intensity signal is affected by movement disturbances. The purpose of this work is to detect the heart rate in a way that is robust to these disturbances. The results were presented and, in the thesis, it was concluded that the majority of disturbances could be removed, but with the exception of a few tests.

<div>Measurement of vital cardiovascular health attributes, e.g., pulse rate variability, and estimation of exertion level of a person can help in diagnosing potential cardiovascular diseases, musculoskeletal injuries and thus monitoring an individual's well-being. Cumulative exposure to repetitive and forceful activities may lead to musculoskeletal injuries which not only reduce workers' efficiency and productivity, but also affect their quality of life. Existing techniques for such measurements pose a great challenge as they are either intrusive, interfere with human-machine interface, and/or subjective in the nature, thus are not scalable. Non-contact methods to measure these metrics can eliminate the need for specialized piece of equipment and manual measurements. Non-contact methods can have additional advantages since they are potentially scalable, portable, can be used for continuous measurements, and can be used on patients and workers with varying levels of dexterity and independence, from people with physical impairments, shop-floor workers to infants. In this work, we use face videos and the photoplethysmography (PPG) signals to extract relevant features and build a regression model that can predict pulse rate, and pulse rate variability, and a classification model that can predict force exertion levels of 0%, 50%, and 100% (representing rest, moderate effort, and high effort), thus providing a non-intrusive and scalable approach. Efficient feature extraction has resulted in high accuracy for both the models.</div>

- To be trapped in a roller-coaster That ́s how some of the patients are describing the situa- tion of living with heart failure. To be stuck in their home not knowing if they will have to seek care today and not knowing if they will have the energy to attend that birth- day party later this week.Each year 2-3% of the Swedish population are diagnosed with heart failure, which results in 30,000 new individu- als with the disorder. Cardiovascular disease is rapidly in- creasing in the world and before 2030, people with heart failure will increased by 100%. Our improved living condi- tions and better health have allowed us to grow older and made us more dependent on other types of care, care at home. Today the patients are only looking for changes in weight and if they have excessive water in their body. What if we could see changes in values before they develop in to symptoms? We could then use a smaller dosage of medicine and minimize the risk of damaging other organs.During my work I have met with patients, husbands, wives and care givers to gain a better insight and understanding of the different stakeholders problems. The common factor is the insufficient methods for monitoring that are being used today, which aren’t optimal for patients, relatives or care givers.That led me to look at non-invasive testing methods (PPG), which would make it possible to see a trend of a patients health. Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. PPG is most often employed non-invasively and operates at a red or a near infrared wavelength due to its properties to permeate tissue. By sending out different wavelengths and recorde how much is transmit- ted, scattered and absorb, the sensor can measure levels in the blood. When the station is done with the test it will send the result automatically to “Mina vårdkontakter” which will be accessible for both care givers and the patient. The profile will provide the patients with test results and a first contact holding advice for immediate and awaiting care. This will educate the patients and lead to them gaining a better understanding of their current health situation.

The overall aim of this thesis was to combine optical methods into a system with the ability to simultaneously measure blood flow changes at different tissue depths. The goal of such a system was to reveal vascular mechanisms relevant to pressure ulcer etiology under clinically relevant conditions and in relation to the evaluation of pressure-redistribution support surfaces. This thesis consists of four quantitative, cross-sectional studies measuring blood flow responses before, during, and after pressure exposure of the sacral tissue. Two optical methods – photoplethysmography and laser Doppler flowmetry – were combined in a newly developed system that has the ability to discriminate blood flows at different tissue depths. Studies I and II explored blood flow responses at different depths in 17 individuals. In Study I the blood flow was related to tissue thickness and tissue compression during pressure exposure of ≥ 220 mmHg. In Study II, the sacral tissue was loaded with 37.5 mmHg and 50.0 mmHg, and the variation in blood flow was measured. Studies III and IV included 42 healthy individuals &lt; 65 years, 38 healthy individuals ≥ 65 years, and 35 patients ≥ 65 years. Study III included between-subject comparisons of blood flow and pressure between individuals in the three study groups lying in supine positions on a standard hospital mattress. Study IV added within-subject comparisons while the individual was lying on four different types of mattress. The studies explored the vascular phenomena pressure-induced vasodilation (PIV) and reactive hyperemia (RH). The most common blood flow response to tissue exposure in this thesis was PIV, although a decrease in blood flow (a lack of PIV) was observed in some individuals. The patients tended to have higher interface pressure during pressure exposure than the healthy groups but no differences in blood flow responses were seen. Our results showed that pressure levels that are normally considered to be harmless could have a significant effect on the microcirculation in different tissue structures. Differences in individual blood flow responses in terms of PIV and RH were seen, and a larger proportion of individuals lacked these responses in the deeper tissue structures compared to more superficial tissue structures. This thesis identified PIV and RH that are important vascular mechanisms for pressure ulcer development and revealed for the first time that PIV and RH are present at different depths under clinically relevant conditions. The thesis also identified a population of individuals not previously identified who lack both PIV and RH and seem to be particularly vulnerable to pressure exposure. Further, this thesis has added a new perspective to the microcirculation in pressure ulcer etiology in terms of blood flow regulation and endothelial function that are anchored in clinically relevant studies. Finally, the evaluation of pressureredistribution support surfaces in terms of mean blood flow during and after tissue exposure was shown to be unfeasible, but the assessment of PIV and RH could provide a new possibility for measuring individual physiological responses that are known to be related to pressure ulcer development.

Diss. (sammanfattning) Linköping : Univ., 2005. / Härtill 5 uppsatser.

Heart Rate Variability (HRV) analysis has been garnering attention from researchers due to its wide range of applications. Medical researchers have always been interested in Heart Rate (HR) and HRV analysis, but nowadays, investigators from variety of other fields are also probing the subject. For instance, variation in HR and HRV is connected to emotional arousal. Therefore, knowledge from the fields of affective computing and psychology, can be employed to devise machines that understand the emotional states of humans. Recent advancements in non-contact HR and HRV measurement techniques will likely further boost interest in emotional estimation through . Such measurement methods involve the extraction of the photoplethysmography (PPG) signal from the human's face through a camera. The latest approaches apply Independent Component Analysis (ICA) on the color channels of video recordings to extract a PPG signal. Other investigated methods rely on Eulerian Video Magnification (EVM) to detect subtle changes in skin color associated with PPG. The effectiveness of the EVM in HR estimation has well been established. However, to the best of our knowledge, EVM has not been successfully employed to extract HRV feature from a video of a human face. In contrast, ICA based methods have been successfully used for HRV analysis. As we demonstrate in this thesis, these two approaches for HRV feature extraction are highly sensitive to noise. Hence, when we evaluated them in indoor settings, we obtained mean absolute error in the range of 0.012 and 28.4. Therefore, in this thesis, we present two approaches to minimize the error rate when estimating physiological measurements from recorded facial videos using a standard camera. In our first approach which is based on the EVM method, we succeeded in extracting HRV measurements but we could not get rid of high frequency noise, which resulted in a high error percentage for the result of the High frequency (HF) component. Our second proposed approach solved this issue by applying ICA on the red, green and blue (RGB) colors channels and we were able to achieve lower error rates and less noisy signal as compared to previous related works. This was done by using a Buterworth filter with the subject's specific HR range as its Cut-Off. The methods were tested with 12 subjects from the DISCOVER lab at the University of Ottawa, using artificial lights as the only source of illumination. This made it a challenge for us because artificial light produces HF signals which can interfere with the PPG signal. The final results show that our proposed ICA based method has a mean absolute error (MAE) of 0.006, 0.005, 0.34, 0.57 and 0.419 for the mean HR, mean RR, LF, HF and LF/HF respectively. This approach also shows that these physiological parameters are highly correlated with the results taken from the electrocardiography (ECG).

The thesis describes design, algorithmization and realization of graphical application for recording EKG and PPG signal using LabJack UE9 tool in Matlab program, it also describes subsequent deposition of recorded signals and their processing, such as optional selection, cropping and filtering. Furthermore there are described types of filters, methods for detecting basic parameters of EKG and PPG signals and methods for detecting R waves and Systolic peaks. Based on detection of those parameters, algorithms for computing average heart rate and finding arrhythmias were designed and tested. Last part of the thesis includes an evaulation which compares values detected by designed algorithms with values from public database which includes reference annotation.

Imaging photoplethysmography (iPPG) has attracted much attention over the last years. The vast majority of works focuses on methods to reliably extract the heart rate from videos. Only a few works addressed iPPGs ability to exploit spatio-temporal perfusion pattern to derive further diagnostic statements. This work directs at the spatio-temporal analysis of blood perfusion from videos. We present a novel algorithm that bases on the two-dimensional representation of the blood pulsation (perfusion map). The basic idea behind the proposed algorithm consists of a pairwise estimation of time delays between photoplethysmographic signals of spatially separated regions. The probabilistic approach yields a parameter denoted as perfusion speed. We compare the perfusion speed versus two parameters, which assess the strength of blood pulsation (perfusion strength and signal to noise ratio). Preliminary results using video data with different physiological stimuli (cold pressure test, cold face test) show that all measures are in fluenced by those stimuli (some of them with statistical certainty). The perfusion speed turned out to be more sensitive than the other measures in some cases. However, our results also show that the intraindividual stability and interindividual comparability of all used measures remain critical points. This work proves the general feasibility of employing the perfusion speed as novel iPPG quantity. Future studies will address open points like the handling of ballistocardiographic effects and will try to deepen the understanding of the predominant physiological mechanisms and their relation to the algorithmic performance.