Sleep detection with photoplethysmography for wearable-based health monitoring

Remote health monitoring has gained increasing attention in the recent years. Detecting sleep patterns provides users with insights on their personal health issues, and can help in the diagnosis of various sleep disorders. Conventional methods are focused on the acceleration data, or are not suitable for continuous monitoring, like the polysomnography. Wearable devices enable a way to continuously measure photoplethysmography signal. Photoplethysmography signal contains information on multiple physiological systems, and can be used to detect sleep patterns. Sleep detection using wearable-based photoplethysmography signal offers a convenient and easy way to monitor health. In this thesis, a photoplethysmography-based sleep detection method for wearable-based health monitoring is described. This technique aims to separate wakefulness and asleep states with adequate accuracy. To examine the importance of good quality data in sleep detection, the quality of the signal is assessed. The proposed method uses statistical and heart rate based features extracted from the photoplethysmography signal. Using the most relevant features, various supervised learning algorithms are trained, compared and evaluated. These algorithms are logistic regression, decision tree, random forest, support vector machine, k-nearest neighbors, and Naive Bayes. The best performance is obtained by the random forest classifier. The method received an overall accuracy of 81 percent. It was able to detect the sleep periods with 86 percent accuracy and the awake periods with 74 percent accuracy. Motion artifacts occurring during the awake time caused distortion to the signal. Features related to the shape of the signal improved the accuracy of sleep detection, since signal distortion was associated with the awake time. It is concluded that photoplethysmography signal provides a good alternative for wearable-based sleep detection. Future studies with more comprehensive sleep level analysis could be conducted to provide valuable information on the quality of sleep.Viime vuosina etänä tapahtuva terveyden seuranta on saanut yhä enemmän huomiota. Unen tunnistaminen antaa käyttäjille tietoa heidän henkilökohtaisista terveysongelmistaan ja voi auttaa erilaisten unihäiriöiden diagnosoinnissa. Tavanomaiset menetelmät käyttävät kiihtyvyyteen perustuvaa dataa, tai eivät ole soveltuvia jatkuvaan seurantaan, kuten polysomnografia. Puettavan teknologian avulla fotopletysmografiasignaalin jatkuva mittaus on mahdollista. Fotopletysmografiasignaali sisältää tietoa useista fysiologisista järjestelmistä ja sitä voidaan käyttää unen tunnistamiseen. Puettavan teknologian avulla mitatun fotopletysmografiasignaalin käyttö unen tunnistuksessa tarjoaa kätevän ja helpon tavan seurata terveyttä. Tässä diplomityössä kuvataan fotopletysmografiaan perustuva unenhavaitsemismenetelmä, joka soveltuu puettavaa teknologiaa hyödyntävään terveyden seurantaan. Tekniikalla pyritään erottamaan hereillä olo ja uni riittävän tarkasti. Signaalin laatu arvioidaan, jotta voidaan tutkia datan laadun tärkeys unen tunnistuksessa. Kehitetty menetelmä käyttää tilastollisia ja sykkeeseen perustuvia ominaisuuksia, jotka on erotettu fotopletysmografiasignaalista. Tärkeimpiä ominaisuuksia käyttämällä erilaisia valvottuja oppimisalgoritmeja koulutetaan, vertaillaan ja arvioidaan. Käytetyt algoritmit ovat logistinen regressio, päätöspuu, satunnainen metsä, tukivektorikone, k-lähimmät naapurit ja Naive Bayes. Paras tulos saadaan käyttämällä satunnainen metsä -algoritmia. Menetelmällä saavutetaan 81 prosentin kokonaistarkkuus. Uni pystytään tunnistamaan 86 prosentin tarkkuudella ja hereillä olo 74 prosentin tarkkuudella. Hereillä ollessa liikkeestä johtuvat häiriöt aiheuttavat vääristymää signaaliin. Signaalin muotoon liittyvät ominaisuudet paransivat unentunnistuksen tarkkuutta, koska signaalin vääristyminen yhdistettiin hereilläoloaikaan. Tutkimuksen tuloksista voidaan tehdä johtopäätös, että fotopletysmografiasignaali tarjoaa hyvän vaihtoehdon puettavaa teknologiaa hyödyntävään unen tunnistamiseen. Tulevaisuudessa unen eri vaiheita voitaisiin tutkia kattavammin, jolloin saataisiin arvokasta tietoa unen laadusta

Noncontact Vital Signs Detection

Human health condition can be accessed by measurement of vital signs, i.e., respiratory rate (RR), heart rate (HR), blood oxygen level, temperature and blood pressure. Due to drawbacks of contact sensors in measurement, non-contact sensors such as imaging photoplethysmogram (IPPG) and Doppler radar system have been proposed for cardiorespiratory rates detection by researchers.The UWB pulse Doppler radars provide high resolution range-time-frequency information. It is bestowed with advantages of low transmitted power, through-wall capabilities, and high resolution in localization. However, the poor signal to noise ratio (SNR) makes it challenging for UWB radar systems to accurately detect the heartbeat of a subject. To solve the problem, phased-methods have been proposed to extract the phase variations in the reflected pulses modulated by human tiny thorax motions. Advance signal processing method, i.e., state space method, can not only be used to enhance SNR of human vital signs detection, but also enable the micro-Doppler trajectories extraction of walking subject from UWB radar data.Stepped Frequency Continuous Wave (SFCW) radar is an alternative technique useful to remotely monitor human subject activities. Compared with UWB pulse radar, it relieves the stress on requirement of high sampling rate analog-to-digital converter (ADC) and possesses higher signal-to-noise-ratio (SNR) in vital signs detection. However, conventional SFCW radar suffers from long data acquisition time to step over many frequencies. To solve this problem, multi-channel SFCW radar has been proposed to step through different frequency bandwidths simultaneously. Compressed sensing (CS) can further reduce the data acquisition time by randomly stepping through 20% of the original frequency steps.In this work, SFCW system is implemented with low cost, off-the-shelf surface mount components to make the radar sensors portable. Experimental results collected from both pulse and SFCW radar systems have been validated with commercial contact sensors and satisfactory results are shown

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

Continuous sensing and quantification of body motion in infants:A systematic review

Abnormal body motion in infants may be associated with neurodevelopmental delay or critical illness. In contrast to continuous patient monitoring of the basic vitals, the body motion of infants is only determined by discrete periodic clinical observations of caregivers, leaving the infants unattended for observation for a longer time. One step to fill this gap is to introduce and compare different sensing technologies that are suitable for continuous infant body motion quantification. Therefore, we conducted this systematic review for infant body motion quantification based on the PRISMA method (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this systematic review, we introduce and compare several sensing technologies with motion quantification in different clinical applications. We discuss the pros and cons of each sensing technology for motion quantification. Additionally, we highlight the clinical value and prospects of infant motion monitoring. Finally, we provide suggestions with specific needs in clinical practice, which can be referred by clinical users for their implementation. Our findings suggest that motion quantification can improve the performance of vital sign monitoring, and can provide clinical value to the diagnosis of complications in infants.</p

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included

Contact and remote breathing rate monitoring techniques: a review

ABSTRACT: Breathing rate monitoring is a must for hospitalized patients with the current coronavirus disease 2019 (COVID-19). We review in this paper recent implementations of breathing monitoring techniques, where both contact and remote approaches are presented. It is known that with non-contact monitoring, the patient is not tied to an instrument, which improves patients’ comfort and enhances the accuracy of extracted breathing activity, since the distress generated by a contact device is avoided. Remote breathing monitoring allows screening people infected with COVID-19 by detecting abnormal respiratory patterns. However, non-contact methods show some disadvantages such as the higher set-up complexity compared to contact ones. On the other hand, many reported contact methods are mainly implemented using discrete components. While, numerous integrated solutions have been reported for non-contact techniques, such as continuous wave (CW) Doppler radar and ultrawideband (UWB) pulsed radar. These radar chips are discussed and their measured performances are summarized and compared

NON-CONTACT TECHNIQUES FOR HUMAN VITAL SIGN DETECTION AND GAIT ANALYSIS

Human vital signs including respiratory rate, heart rate, oxygen saturation, blood pressure, and body temperature are important physiological parameters that are used to track and monitor human health condition. Another important biological parameter of human health is human gait. Human vital sign detection and gait investigations have been attracted many scientists and practitioners in various fields such as sport medicine, geriatric medicine, bio-mechanic and bio-medical engineering and has many biological and medical applications such as diagnosis of health issues and abnormalities, elderly care and health monitoring, athlete performance analysis, and treatment of joint problems. Thoroughly tracking and understanding the normal motion of human limb joints can help to accurately monitor human subjects or patients over time to provide early flags of possible complications in order to aid in a proper diagnosis and development of future comprehensive treatment plans. With the spread of COVID-19 around the world, it has been getting more important than ever to employ technology that enables us to detect human vital signs in a non-contact way and helps protect both patients and healthcare providers from potentially life-threatening viruses, and have the potential to also provide a convenient way to monitor people health condition, remotely. A popular technique to extract biological parameters from a distance is to use cameras. Radar systems are another attractive solution for non-contact human vital signs monitoring and gait investigation that track and monitor these biological parameters without invading people privacy. The goal of this research is to develop non-contact methods that is capable of extracting human vital sign parameters and gait features accurately. To do that, in this work, optical systems including cameras and proper filters have been developed to extract human respiratory rate, heart rate, and oxygen saturation. Feasibility of blood pressure extraction using the developed optical technique has been investigated, too. Moreover, a wideband and low-cost radar system has been implemented to detect single or multiple human subject’s respiration and heart rate in dark or from behind the wall. The performance of the implemented radar system has been enhanced and it has been utilized for non-contact human gait analysis. Along with the hardware, advanced signal processing schemes have been enhanced and applied to the data collected using the aforementioned radar system. The data processing algorithms have been extended for multi-subject scenarios with high accuracy for both human vital sign detection and gait analysis. In addition, different configurations of this and high-performance radar system including mono-static and MIMO have been designed and implemented with great success. Many sets of exhaustive experiments have been conducted using different human subjects and various situations and accurate reference sensors have been used to validate the performance of the developed systems and algorithms

Novel Methods for Weak Physiological Parameters Monitoring.

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