The 2023 wearable photoplethysmography roadmap

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

Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring

Photoplethysmography (PPG) is a low-cost, noninvasive optical technique that uses change in light transmission with changes in blood volume within tissue to provide information for cardiovascular health and fitness. As remote health and wearable medical devices become more prevalent, PPG devices are being developed as part of wearable systems to monitor parameters such as heart rate (HR) that do not require complex analysis of the PPG waveform. However, complex analyses of the PPG waveform yield valuable clinical information, such as: blood pressure, respiratory information, sympathetic nervous system activity, and heart rate variability. Systems aiming to derive such complex parameters do not always account for realistic sources of noise, as testing is performed within controlled parameter spaces. A wearable monitoring tool to be used beyond fitness and heart rate must account for noise sources originating from individual patient variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external perturbations of the device itself (e.g., motion artifact, ambient light, and applied pressure to the skin). Here, we present a comprehensive review of the literature that aims to summarize these noise sources for future PPG device development for use in health monitoring

The 2023 wearable photoplethysmography roadmap

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

Pulse Rate Variability for the Assessment of Cardiovascular Changes

Pulse rate variability (PRV) describes the way pulse rate changes through time and is measured from pulsatile signals such as the photoplethysmogram (PPG). It has been proposed as a surrogate for heart rate variability (HRV). Nonetheless, the relationship between these variables is not entirely clear, probably due to both physiological and technical aspects involved in the extraction of PRV. Moreover, the effects of cardiovascular changes on PRV have not been elucidated. In this thesis, four studies were performed to (1) determine the best combination of some technical aspects for the extraction of PRV from PPG signals; (2) evaluate the relationship between PRV and HRV under different cardiovascular conditions; and (3) explore the effects of cardiovascular changes on PRV. First, PRV extraction gave lower errors when (1) signals were acquired for at least 120 s with a 256 Hz sampling rate and filtered with lower low cut-off frequencies and elliptic, equiripple or Parks-McClellan filter; (2) cardiac cycles were determined using the D2max algorithm and the a fiducial points; and (3) the Fast Fourier Transform was applied to obtain frequency spectra. Secondly, the relationship between HRV and PRV was found to be affected by cold exposure and changes in blood pressure, while PRV was found to be different at different body sites. Finally, PRV was affected by haemodynamic changes, such as target flow, stroke rate and blood pressure, both in an in-vitro model and in-vivo data. Additionally, PRV was found to be a potential tool for the estimation of blood pressure, with errors as low as 1:54 ± 0:17 mmHg, 1:07 ± 0:06 mmHg and 1:22 ± 0:09 mmHg for the estimation of systolic, diastolic and mean arterial pressure. Although more studies are needed to fully understand PRV and its clinical potential, PRV should not be regarded as the same as HRV, and it could be consider as a potential valuable biomarker for cardiovascular health

Secure Data Collection and Analysis in Smart Health Monitoring

Smart health monitoring uses real-time monitored data to support diagnosis, treatment, and health decision-making in modern smart healthcare systems and benefit our daily life. The accurate health monitoring and prompt transmission of health data are facilitated by the ever-evolving on-body sensors, wireless communication technologies, and wireless sensing techniques. Although the users have witnessed the convenience of smart health monitoring, severe privacy and security concerns on the valuable and sensitive collected data come along with the merit. The data collection, transmission, and analysis are vulnerable to various attacks, e.g., eavesdropping, due to the open nature of wireless media, the resource constraints of sensing devices, and the lack of security protocols. These deficiencies not only make conventional cryptographic methods not applicable in smart health monitoring but also put many obstacles in the path of designing privacy protection mechanisms. In this dissertation, we design dedicated schemes to achieve secure data collection and analysis in smart health monitoring. The first two works propose two robust and secure authentication schemes based on Electrocardiogram (ECG), which outperform traditional user identity authentication schemes in health monitoring, to restrict the access to collected data to legitimate users. To improve the practicality of ECG-based authentication, we address the nonuniformity and sensitivity of ECG signals, as well as the noise contamination issue. The next work investigates an extended authentication goal, denoted as wearable-user pair authentication. It simultaneously authenticates the user identity and device identity to provide further protection. We exploit the uniqueness of the interference between different wireless protocols, which is common in health monitoring due to devices\u27 varying sensing and transmission demands, and design a wearable-user pair authentication scheme based on the interference. However, the harm of this interference is also outstanding. Thus, in the fourth work, we use wireless human activity recognition in health monitoring as an example and analyze how this interference may jeopardize it. We identify a new attack that can produce false recognition result and discuss potential countermeasures against this attack. In the end, we move to a broader scenario and protect the statistics of distributed data reported in mobile crowd sensing, a common practice used in public health monitoring for data collection. We deploy differential privacy to enable the indistinguishability of workers\u27 locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks

Inferring Cognitive Load using Wireless Signals

From not disturbing a focused programmer, to entertaining a restless commuter waiting for a train, ubiquitous computing devices could greatly enhance their interaction with humans, should these devices only be aware of the user's cognitive load. However, current means of assessing cognitive load are, with a few exceptions, based on intrusive methods requiring physical contact of the measurement equipment and the user. In this thesis we propose Wi-Mind, a system for remote cognitive load assessment through wireless sensing. Wi-Mind is based on a software-defined radio-based radar that measures sub-millimeter movements related to a person's breathing and heartbeats, which, in turn allow us to infer the person's cognitive load. We built the system and tested it with 23 volunteers being engaged in different tasks. Results show that while Wi-Mind manges to detect whether one is engaged in a cognitively demanding task, the inference of the exact cognitive load level remains challenging

Inferring Cognitive Load using Wireless Signals

From not disturbing a focused programmer, to entertaining a restless commuter waiting for a train, ubiquitous computing devices could greatly enhance their interaction with humans, should these devices only be aware of the user's cognitive load. However, current means of assessing cognitive load are, with a few exceptions, based on intrusive methods requiring physical contact of the measurement equipment and the user. In this thesis we propose Wi-Mind, a system for remote cognitive load assessment through wireless sensing. Wi-Mind is based on a software-defined radio-based radar that measures sub-millimeter movements related to a person's breathing and heartbeats, which, in turn allow us to infer the person's cognitive load. We built the system and tested it with 23 volunteers being engaged in different tasks. Results show that while Wi-Mind manges to detect whether one is engaged in a cognitively demanding task, the inference of the exact cognitive load level remains challenging

Sensores de fibra ótica para arquiteturas e-Health

In this work, optical fiber sensors were developed and optimized for biomedical applications in wearable and non-intrusive and/or invisible solutions. As it was intended that the developed devices would not interfere with the user's movements and their daily life, the fibre optic sensors presented several advantages when compared to conventional electronic sensors, among others, the following stand out: size and reduced weight, biocompatibility, safety, immunity to electromagnetic interference and high sensitivity. In a first step, wearable devices with fibre optic sensors based in Fiber Bragg gratings (FBG) were developed to be incorporated into insoles to monitor different walking parameters based on the analysis of the pressure exerted on several areas of the insole. Still within this theme, other sensors were developed using the same sensing technology, but capable of monitoring pressure and shear forces simultaneously. This work was pioneering and allowed monitoring one of the main causes of foot ulceration in people with diabetes: shear. At a later stage, the study focused on the issue related with the appearance of ulcers in people with reduced mobility and wheelchair users. In order to contribute to the mitigation of this scourge, a system was developed composed of a network of fibre optic sensors capable of monitoring the pressure at various points of the wheelchair. It not only measures the pressure at each point, but also monitors the posture of the wheelchair user and advises him/her to change posture regularly to reduce the probability of this pathology occurring. Still within this application, another work was developed where the sensor not only monitored the pressure but also the temperature in each of the analysis points, thus indirectly measuring shear. In another phase, plastic fibre optic sensors were studied and developed to monitor the body posture of an office chair user. Simultaneously, software was developed capable of monitoring and showing the user all the acquired data in real time and warning for incorrect postures, as well as advising for work breaks. In a fourth phase, the study focused on the development of highly sensitive sensors embedded in materials printed by a 3D printer. The sensor was composed of an optical fibre with a FBG and the sensor body of a flexible polymeric material called "Flexible". This material was printed on a 3D printer and during its printing the optical fibre was incorporated. The sensor proved to be highly sensitive and was able to monitor respiratory and cardiac rate, both in wearable solutions (chest and wrist) and in "invisible" solutions (office chair).Neste trabalho foram desenvolvidos e otimizados sensores em fibra ótica para aplicações biomédicas em soluções vestíveis e não intrusivas/ou invisíveis. Tendo em conta que se pretende que os dispositivos desenvolvidos não interfiram com os movimentos e o dia-a-dia do utilizador, os sensores de fibra ótica apresentam inúmeras vantagens quando comparados com os sensores eletrónicos convencionais, de entre várias, destacam-se: tamanho e peso reduzido, biocompatibilidade, segurança, imunidade a interferências eletromagnéticas e elevada sensibilidade. Numa primeira etapa, foram desenvolvidos dispositivos vestíveis com sensores de fibra ótica baseados em redes de Bragg (FBG) para incorporar em palmilhas de modo a monitorizar diferentes parâmetros da marcha com base na análise da pressão exercida em várias zonas da palmilha. Ainda no âmbito deste tema, adicionalmente, foram desenvolvidos sensores utilizando a mesma tecnologia de sensoriamento, mas capazes de monitorizar simultaneamente pressão e forças de cisalhamento. Este trabalho foi pioneiro e permitiu monitorizar um dos principais responsáveis pela ulceração dos pés em pessoas com diabetes: o cisalhamento. Numa fase posterior, o estudo centrou-se na temática relacionada com o aparecimento de úlceras em pessoas com mobilidade reduzida e utilizadores de cadeiras de rodas. De modo a contribuir para a mitigação deste flagelo, procurou-se desenvolver um sistema composto por uma rede de sensores de fibra ótica capaz de monitorizar a pressão em vários pontos de uma cadeira de rodas e não só aferir a pressão em cada ponto, mas monitorizar a postura do cadeirante e aconselhá-lo a mudar de postura com regularidade, de modo a diminuir a probabilidade de ocorrência desta patologia. Ainda dentro desta aplicação, foi publicado um outro trabalho onde o sensor não só monitoriza a pressão como também a temperatura em cada um dos pontos de análise, conseguindo aferir assim indiretamente o cisalhamento. Numa outra fase, foi realizado o estudo e desenvolvimento de sensores de fibra ótica de plástico para monitorizar a postura corporal de um utilizador de uma cadeira de escritório. Simultaneamente, foi desenvolvido um software capaz de monitorizar e mostrar ao utilizador todos os dados adquiridos em tempo real e advertir o utilizador de posturas incorretas, bem como aconselhar para pausas no trabalho. Numa quarta fase, o estudo centrou-se no desenvolvimento de sensores altamente sensíveis embebidos em materiais impressos 3D. O sensor é composto por uma fibra ótica com uma FBG e o corpo do sensor por um material polimérico flexível, denominado “Flexible”. O sensor foi impresso numa impressora 3D e durante a sua impressão foi incorporada a fibra ótica. O sensor demonstrou ser altamente sensível e foi capaz de monitorizar frequência respiratória e cardíaca, tanto em soluções vestíveis (peito e pulso) como em soluções “invisíveis” (cadeira de escritório).Programa Doutoral em Engenharia Físic

Multimodal radar sensing for ambient assisted living

Data acquired from health and behavioural monitoring of daily life activities can be exploited to provide real-time medical and nursing service with affordable cost and higher efficiency. A variety of sensing technologies for this purpose have been developed and presented in the literature, for instance, wearable IMU (Inertial Measurement Unit) to measure acceleration and angular speed of the person, cameras to record the images or video sequence, PIR (Pyroelectric infrared) sensor to detect the presence of the person based on Pyroelectric Effect, and radar to estimate distance and radial velocity of the person. Each sensing technology has pros and cons, and may not be optimal for the tasks. It is possible to leverage the strength of all these sensors through information fusion in a multimodal fashion. The fusion can take place at three different levels, namely, i) signal level where commensurate data are combined, ii) feature level where feature vectors of different sensors are concatenated and iii) decision level where confidence level or prediction label of classifiers are used to generate a new output. For each level, there are different fusion algorithms, the key challenge here is mainly on choosing the best existing fusion algorithm and developing novel fusion algorithms that more suitable for the current application. The fundamental contribution of this thesis is therefore exploring possible information fusion between radar, primarily FMCW (Frequency Modulated Continuous Wave) radar, and wearable IMU, between distributed radar sensors, and between UWB impulse radar and pressure sensor array. The objective is to sense and classify daily activities patterns, gait styles and micro-gestures as well as producing early warnings of high-risk events such as falls. Initially, only “snapshot” activities (single activity within a short X-s measurement) have been collected and analysed for verifying the accuracy improvement due to information fusion. Then continuous activities (activities that are performed one after another with random duration and transitions) have been collected to simulate the real-world case scenario. To overcome the drawbacks of conventional sliding-window approach on continuous data, a Bi-LSTM (Bidirectional Long Short-Term Memory) network is proposed to identify the transitions of daily activities. Meanwhile, a hybrid fusion framework is presented to exploit the power of soft and hard fusion. Moreover, a trilateration-based signal level fusion method has been successfully applied on the range information of three UWB (Ultra-wideband) impulse radar and the results show comparable performance as using micro-Doppler signature, at the price of much less computation loads. For classifying ‘snapshot’ activities, fusion between radar and wearable shows approximately 12% accuracy improvement compared to using radar only, whereas for classifying continuous activities and gaits, our proposed hybrid fusion and trilateration-based signal level improves roughly 6.8% (before 89%, after 95.8%) and 7.3% (before 85.4%, after 92.7%), respectively