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

A study on the effect of contact pressure during physical activity on photoplethysmographic heart rate measurements

Heart rate (HR) as an important physiological indicator could properly describe global subject’s physical status. Photoplethysmographic (PPG) sensors are catching on in field of wearable sensors, combining the advantages in costs, weight and size. Nevertheless, accuracy in HR readings is unreliable specifically during physical activity. Among several identified sources that affect PPG recording, contact pressure (CP) between the PPG sensor and skin greatly influences the signals. Methods: In this study, the accuracy of HR measurements of a PPG sensor at different CP was investigated when compared with a commercial ECG-based chest strap used as a test control, with the aim of determining the optimal CP to produce a reliable signal during physical activity. Seventeen subjects were enrolled for the study to perform a physical activity at three different rates repeated at three different contact pressures of the PPG-based wristband. Results: The results show that the CP of 54 mmHg provides the most accurate outcome with a Pearson correlation coefficient ranging from 0.81 to 0.95 and a mean average percentage error ranging from 3.8% to 2.4%, based on the physical activity rate. Conclusion: Authors found that changes in the CP have greater effects on PPG-HR signal quality than those deriving from the intensity of the physical activity and specifically, the individual best CP for each subject provided reliable HR measurements even for a high intensity of physical exercise with a Bland–Altman plot within ±11 bpm. Although future studies on a larger cohort of subjects are still needed, this study could contribute a profitable indication to enhance accuracy of PPG-based wearable devices

Machine learning algorithm development of SPO2 sensor for improved robustness in wearables

Wearable devices application in the digital measurement of health has gained attention by researchers. These devices allow for data acquisition during real-life activities, resulting in higher data availability. They often include photoplethysmography (PPG) sensors, the sensor behind pulse oximetry. Pulse oximetry is a non-invasive method for continuous oxygen saturation (SpO2) measurements, a standard monitor for anesthesia procedures, an essential tool for managing patients undergoing pulmonary rehabilitation and an effective method for assessing sleep-disordered breathing. However, the current market focuses on heart rate measurements and lacks the robustness of clinical applications for SpO2 assessment. In addition, the most common obstacle in PPG measurements is the signal quality, especially in the form of motion artifacts. Thus, this work aims at increasing the clinical robustness in this devices by evaluating its quality and then extracting relevant metrics. Firstly, a data acquisition protocol was developed, focused on acquiring data during daily activities. This resulted in a dataset with different signal qualities, which was manually annotated to be used as the base for the Machine Learning models. A second protocol was also developed especially designed for the extraction of the SpO2 measurement. Several Machine Learning models were developed to evaluate the signal in three distinct qualities (corrupted, suboptimal, optimal) in real time. A Random Forest classifier achieved accuracies of 79% and 80% for the binary models capable of differentiating between usable and unusable signals, and accuracies of 74% and 80% when distinguishing between optimal and suboptimal signals, for the two utilized channels. The multi-class models achieved accuracies of 66% and 65% for the two utilized channels. Three clinically relevant metrics were also extracted from the PPG signal: heart rate, respiratory rate and SpO2. The heart rate and respiratory rate algorithms resulted in performances similar to the ones found in the literature and in other devices currently on the market. However, while promising, more data is needed to reach statistical significance for the SpO2 measurement.A monitorização do estado de saúde de pacientes em ambulatório utilizando dispositivos wearables tem vindo a ser cada vez mais investigada. Estes dispositivos permitem uma aquisição de dados durante o dia a dia, resultando num maior conjunto de dados. Frequentemente, estes dispositivos incluem fotopletismógrafos (PPG), o sensor por detrás da oximetria de pulso. A oximetria de pulso é um método não invasivo para a medição da saturação de oxigénio no sangue (SpO2) de forma contínua. É um equipamento padrão para procedimentos com anestesia, uma ferramenta essencial para monitorizar pacientes em reabilitação pulmonar e um método eficaz para avaliar respiração desordenada do sono. Ainda assim, o mercado atual foca-se principalmente em medições da frequência cardíaca e carece robustez para aplicações clínicas da medição de SpO2. Para além disso, o obstáculo mais comum em medições com PPG é a qualidade do sinal. Consequentemente, este trabalho procura melhorar a robustez clínica destes dispositivos analisando a qualidade do sinal e, posteriormente, extrair métricas relevantes. Primeiramente, foi desenvolvido um protocolo para aquisição de dados de atividades do dia a dia. Assim, foram adquiridos dados com diferentes qualidades, que foram avaliados manualmente de forma a servir de base para os vários modelos de Machine Learning. Também foi desenvolvido um segundo protocolo para a extração do valor de SpO2. Diferentes modelos de Machine Learning foram desenvolvidos para avaliar em tempo real a qualidade do sinal em três qualidades (corrompido, subótimo, ótimo) . Um classificador baseado em Random Forest atingiu exatidões de 79% e 80% em classificadores binários capazes de distinguir entre sinais úteis e inúteis, e exatidões de 74% e 80% a diferenciar entre um sinal subótimo e ótimo, para os dois canais usados. Os classificadores multi-classe atingiram exatidões de 66% e 65% para os dois canais usados. Três medidas clinicamente relevantes foram também extraídas do sinal de PPG: frequências cardíaca e respiratória, cujos algoritmos atingiram resultados semelhantes aos encontrados na literatura e em aparelhos no mercado, e SpO2 que, ainda que promissores, mais dados seriam necessários para os resultados serem estatisticamente significativo

Novel Methods for Weak Physiological Parameters Monitoring.

M.S. Thesis. University of Hawaiʻi at Mānoa 2017

Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring

Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision. The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring. In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively. Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time. These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces

Evaluation of Wearable Optical Heart Rate Monitoring Sensors

Heart rate monitoring provides valuable information about an individual’s physiological condition. The information obtained from heart rate monitoring can be used for a wide range of purposes such as clinical diagnostics, assessment of the efficiency of training for sports and fitness, or of sleep quality and stress levels in wellbeing applications. Other useful parameters for describing a person’s fitness, such as maximal oxygen uptake and energy expenditure, can also be estimated using heart rate measurement. The traditional ‘gold standard’ for heart rate monitoring is the electrocardiograph, but nowadays there are a number of alternative methods too. Of these, optical sensors provide a relatively simple, lowcost and unobtrusive technology for monitoring heart rate and they are widely accepted by users. There are many factors affecting the measurement of optical signals that have an effect on the accuracy of heart rate estimation. However, there is a lack of standardized and unified methodology for comparing the accuracy of optical heart rate sensors to the ‘gold standard’ methods of measuring heart rate. The widespread use of optical sensors for different purposes has led to a pressing need for a common objective methodology for the evaluation of how accurate these sensors are. This thesis presents a methodology for the objective evaluation of optical heart-rate sensors. The methodology is applied in evaluation studies of four commercially available optical sensors. These evaluations were carried out during both controlled and non-controlled sporting and daily life activities. In addition, evaluation of beat detection accuracy was carried out in non-controlled sleep conditions. The accuracy of wrist-worn optical heart-rate sensors in estimating of maximal oxygen uptake during submaximal exercise and energy expenditure during maximal exercise using heart rate as input parameter were also evaluated. The accuracy of a semi-continuous heart rate estimation algorithm designed to reduce power consumption for long-term monitoring was also evaluated in various conditions. The main findings show that optical heart-rate sensors may be highly accurate during rhythmic sports activities, such as jogging, running, and cycling, including ramp-up running during maximal exercise testing. During non-rhythmic activities, such as intermittent hand movements, the sensors’ accuracy depends on where they are worn. During sleep and motionless conditions, the optical heart-rate sensors’ estimates for beat detection and inter-beat interval showed less than one percent inaccuracy against the values obtained using standard measurement techniques. The sensors were also sufficiently accurate at measuring the interbeat intervals to be used for calculating the heart rate variability parameters. The estimation accuracy of the fitness parameters derived from measured heart rate can be described as follows. An assessment of the maximal oxygen uptake estimation during a sub-maximal outdoor exercise had a precision close to a sport laboratory measurement. The energy expenditure estimation during a maximal exercise was more accurate during higher intensity of exercise above aerobic threshold but the accuracy decreased at lower intensity of exercise below the aerobic threshold, in comparison with the standardized reference measurement. The semi-continuous algorithm was nearly as accurate as continuous heart-rate detection, and there was a significant reduction in the power consumption of the optical chain components up to eighty percent. The results obtained from these studies show that, under certain conditions, optical sensors may be similarly accurate in measuring heart rate as the ‘gold standard’ methods and they can be relied on to monitor heart rate for various purposes during sport, everyday activities, or sleep

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

Error Prevention in Sensors and Sensor Systems

Achievements in all fields of engineering and fabrication methods have led towards optimization and integration of multiple sensing devices into a concise system. These advances have caused significant innovation in various commercial, industrial, and research efforts. Integrations of subsystems have important applications for sensor systems in particular. The need for reporting and real time awareness of a device’s condition and surroundings have led to sensor systems being implemented in a wide variety of fields. From environmental sensors for agriculture, to object characterization and biomedical sensing, the application for sensor systems has impacted all modern facets of innovation. With these innovations, however, additional sources of errors can occur, that can cause new but exciting challenges for such integrated devices. Such challenges range from error correction and accuracy to power optimization. Researchers have invested significant time and effort to improve the applicability and accuracy of sensors and sensor systems. Efforts to reduce inherent and external noise of sensors can range from hardware to software solutions, focusing on signal processing and exploiting the integration of multiple signals and/or sensor types. My research work throughout my career has been focused on deployable and integrated sensor systems. Their integration not only in hardware and components but also in software, machine learning, pattern recognition, and overall signal processing algorithms to aid in error correction and noise tailoring in all their hardware and software components

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods