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

Heart rate measurement using the built-in triaxial accelerometer from a commercial digital writing device

Wearable devices are on the rise. Smart watches and phones, fitness trackers or smart textiles now provide unprecedented access to our own personal data. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor the heart's and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heartrate when they are attached to the chest. They can also help filter out some noise in ECG signal from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen), with a standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is done with eight volunteers, writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685x10$^{-3}$ comparing the DigiPen's computed ${\Delta}$t (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates from the pen accurately correlate with the reference ECG signals

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

The design and evaluation of discrete wearable medical devices for vital signs monitoring

The observation, recording and appraisal of an individual’s vital signs, namely temperature, heart rate, blood pressure, respiratory rate and blood oxygen saturation (SpO2), are key components in the assessment of their health and wellbeing. Measurements provide valuable diagnostic data, facilitating clinical diagnosis, management and monitoring. Respiratory rate sensing is perhaps the most under-utilised of all the vital signs, being routinely assessed by observation or estimated algorithmically from respiratory-induced beat-to-beat variation in heart rate. Moreover there is an unmet need for wearable devices that can measure all or most of the vital signs. This project therefore aims to a) develop a device that can measure respiratory rate and b) develop a wearable device that can measure all or most of the vital signs. An accelerometer-based clavicular respiratory motion sensor was developed and compared with a similar thoracic motion sensor and reference using exhalatory flow. Pilot study results established that the clavicle sensor accurately tracked the reference in monitoring respiratory rate and outperformed the thoracic device. An Ear-worn Patient Monitoring System (EPMS) was also developed, providing a discrete telemonitoring device capable of rapidly measuring tympanic temperature, heart rate, SpO2 and activity level. The results of a comparative pilot study against reference instruments revealed that heart rate matched the reference for accuracy, while temperature under read (< 1°C) and SpO2 was inconsistent with poor correlation. In conclusion, both of the prototype devices require further development. The respiratory sensor would benefit from product engineering and larger scale testing to fully exploit the technology, but could find use in both hospital and community-based The design and evaluation of discrete wearable medical devices for vital signs monitoring DG Pitts ii Cranfield University monitoring. The EPMS has potential for clinical and community use, having demonstrated its capability of rapidly capturing and wirelessly transmitting vital signs readings. Further development is nevertheless required to improve the thermometer probe and resolve outstanding issues with SpO2 readings

Wearable devices and IoT applications for symptom detection, infection tracking, and diffusion containment of the COVID-19 pandemic: a survey

Until a safe and effective vaccine to fight the SARS-CoV-2 virus is developed and available for the global population, preventive measures, such as wearable tracking and monitoring systems supported by Internet of Things (IoT) infrastructures, are valuable tools for containing the pandemic. In this review paper we analyze innovative wearable systems for limiting the virus spread, early detection of the first symptoms of the coronavirus disease COVID-19 infection, and remote monitoring of the health conditions of infected patients during the quarantine. The attention is focused on systems allowing quick user screening through ready-to-use hardware and software components. Such sensor-based systems monitor the principal vital signs, detect symptoms related to COVID-19 early, and alert patients and medical staff. Novel wearable devices for complying with social distancing rules and limiting interpersonal contagion (such as smart masks) are investigated and analyzed. In addition, an overview of implantable devices for monitoring the effects of COVID-19 on the cardiovascular system is presented. Then we report an overview of tracing strategies and technologies for containing the COVID-19 pandemic based on IoT technologies, wearable devices, and cloud computing. In detail, we demonstrate the potential of radio frequency based signal technology, including Bluetooth Low Energy (BLE), Wi-Fi, and radio frequency identification (RFID), often combined with Apps and cloud technology. Finally, critical analysis and comparisons of the different discussed solutions are presented, highlighting their potential and providing new insights for developing innovative tools for facing future pandemics

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALTH

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALT

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

Deep Learning Algorithms for Time Series Analysis of Cardiovascular Monitoring Systems

This thesis investigates and develops methods to enable ubiquitous monitoring of the most examined cardiovascular signs, blood pressure, and heart rate. Their continuous measurement can help improve health outcomes, such as the detection of hypertension, heart attack, or stroke, which are the leading causes of death and disability. Recent research into wearable blood pressure monitors sought predominately to utilise a hypothesised relationship with pulse transit time, relying on quasiperiodic pulse event extractions from photoplethysmography local signal characteristics and often used only a fraction of typically bivariate time series. This limitation has been addressed in this thesis by developing methods to acquire and utilise fused multivariate time series without the need for manual feature engineering by leveraging recent advances in data science and deep learning methods that showed great data analysis potential in other domains

Physiological and behavior monitoring systems for smart healthcare environments: a review

Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressedinfo:eu-repo/semantics/publishedVersio