Assessment of trends in the cardiovascular system from time interval measurements using physiological signals obtained at the limbs

Cardiovascular diseases are an increasing source of concern in modern societies due to their increasing prevalence and high impact on the lives of many people. Monitoring cardiovascular parameters in ambulatory scenarios is an emerging approach that can provide better medical access to patients while decreasing the costs associated to the treatment of these diseases. This work analyzes systems and methods to measure time intervals between the electrocardiogram (ECG), impedance plethysmogram (IPG), and the ballistocardiogram (BCG), which can be obtained at the limbs in ambulatory scenarios using simple and cost-effective systems, to assess cardiovascular intervals of interest, such as the pulse arrival time (PAT), pulse transit time (PTT), or the pre-ejection period (PEP). The first section of this thesis analyzes the impact of the signal acquisition system on the uncertainty in timing measurements in order to establish the design specifications for systems intended for that purpose. The minimal requirements found are not very demanding yet some common signal acquisition systems do not fulfill all of them while other capabilities typically found in signal acquisition systems could be downgraded without worsening the timing uncertainty. This section is also devoted to the design of systems intended for timing measurements in ambulatory scenarios according to the specifications previously established. The systems presented have evolved from the current state-of-the-art and are designed for adequate performance in timing measurements with a minimal number of active components. The second section is focused on the measurement of time intervals from the IPG measured from limb to limb, which is a signal that until now has only been used to monitor heart rate. A model to estimate the contributions to the time events in the measured waveform of the different body segments along the current path from geometrical properties of the large arteries is proposed, and the simulation under blood pressure changes suggests that the signal is sensitive to changes in proximal sites of the current path rather than in distal sites. Experimental results show that the PAT to the hand-to-hand IPG, which is obtained from a novel four-electrode handheld system, is correlated to changes in the PEP whereas the PAT to the foot-to-foot IPG shows good performance in assessing changes in the femoral PAT. Therefore, limb-to-limb IPG measurements significantly increase the number of time intervals of interest that can be measured at the limbs since the signals deliver information from proximal sites complementary to that of other measurements typically performed at distal sites. The next section is devoted to the measurement of time intervals that involve different waves of the BCG obtained in a standing platform and whose origin is still under discussion. From the relative timing of other physiological signals, it is hypothesized that the IJ interval of the BCG is sensitive to variations in the PTT. Experimental results show that the BCG I wave is a better surrogate of the cardiac ejection time than the widely-used J wave, which is also supported by the good correlation found between the IJ interval and the aortic PTT. Finally, the novel time interval from the BCG I wave to the foot of the IPG measured between feet, which can be obtained from the same bathroom scale than the BCG, shows good performance in assessing the aortic PAT. The results presented reinforce the role of the BCG as a tool for ambulatory monitoring since the main time intervals targeted in this thesis can be obtained from the timing of its waves. Even though the methods described were tested in a small group of subjects, the results presented in this work show the feasibility and potential of several time interval measurements between the proposed signals that can be performed in ambulatory scenarios, provided the systems intended for that purpose fulfill some minimal design requirements.Les malalties cardiovasculars són una tema de preocupació creixent en societats modernes, degut a l’augment de la seva prevalença i l'elevat impacte en les vides dels pacients que les sofreixen. La mesura i monitoratge de paràmetres cardiovasculars en entorns ambulatoris és una pràctica emergent que facilita l’accés als serveis mèdics i permet reduir dràsticament els costos associats al tractament d'aquestes malalties. En aquest treball s’analitzen sistemes i mètodes per la mesura d’intervals temporals entre l’electrocardiograma (ECG), el pletismograma d’impedància (IPG) i el balistocardiograma (BCG), que es poden obtenir de les extremitats i en entorns ambulatoris a partir de sistemes de baix cost, per tal d’avaluar intervals cardiovasculars d’interès com el pulse arrival time (PAT), pulse transit time (PTT) o el pre-ejection period (PEP). En la primera secció d'aquesta tesi s’analitza l’impacte del sistema d’adquisició del senyal en la incertesa de mesures temporals, per tal d’establir els requeriments mínims que s’han de complir en entorns ambulatoris. Tot i que els valors obtinguts de l’anàlisi no són especialment exigents, alguns no són assolits en diversos sistemes habitualment utilitzats mentre que altres solen estar sobredimensionats i es podrien degradar sense augmentar la incertesa en mesures temporals. Aquesta secció també inclou el disseny i proposta de sistemes per la mesura d’intervals en entorns ambulatoris d’acord amb les especificacions anteriorment establertes, a partir de l’estat de l’art i amb l’objectiu de garantir un correcte funcionament en entorns ambulatoris amb un nombre mínim d’elements actius per reduir el cost i el consum. La segona secció es centra en la mesura d’intervals temporals a partir de l’IPG mesurat entre extremitats, que fins al moment només s’ha fet servir per mesurar el ritme cardíac. Es proposa un model per estimar la contribució de cada segment arterial per on circula el corrent a la forma d’ona obtinguda a partir de la geometria i propietats físiques de les artèries, i les simulacions suggereixen que la senyal entre extremitats és més sensible a canvis en arteries proximals que en distals. Els resultats experimentals mostren que el PAT al hand-to-hand IPG, obtingut a partir d’un innovador sistema handheld de quatre elèctrodes, està fortament correlacionat amb els canvis de PEP, mentre que el PAT al foot-to-foot IPG està correlat amb els canvis en PAT femoral. Conseqüentment, l’ILG entre extremitats augmenta de manera significativa els intervals d’interès que es poden obtenir en extremitats degut a que proporciona informació complementària a les mesures que habitualment s’hi realitzen. La tercera secció està dedicada a la mesura d’intervals que inclouen les ones del BCG vertical obtingut en plataformes, de les que encara se’n discuteix l’origen. A partir de la posició temporal relativa respecte altres ones fisiològiques, s’hipostatitza que l’interval IJ del BCG es sensible a variacions del PTT. Els resultats experimentals mostren que la ona I del BCG és un millor indicador de l’ejecció cardíaca que el pic J, tot i que aquest és el més utilitzat habitualment, degut a la bona correlació entre l’interval IJ i el PTT aòrtic. Finalment, es presenta un mètode alternatiu per la mesura del PTT aòrtic a partir de l’interval entre el pic I del BCG i el peu del foot-to-foot IPG, que es pot obtenir de la mateixa plataforma que el BCG i incrementa la robustesa de la mesura. Els resultats presentats reforcen el paper del BCG com a en mesures en entorns ambulatoris, ja que els principals intervals objectiu d’aquesta tesi es poden obtenir a partir de les seves ones. Tot i que els mètodes descrits han estat provats en grups petits de subjectes saludables, els resultats mostren la viabilitat i el potencial de diversos intervals temporals entre les senyals proposades que poden ésser realitzats en entorns ambulatoris, sempre que els sistemes emprats compleixin els requisits mínims de disseny.Postprint (published version

Smart helmet: wearable multichannel ECG & EEG

Modern wearable technologies have enabled continuous recording of vital signs, however, for activities such as cycling, motor-racing, or military engagement, a helmet with embedded sensors would provide maximum convenience and the opportunity to monitor simultaneously both the vital signs and the electroencephalogram (EEG). To this end, we investigate the feasibility of recording the electrocardiogram (ECG), respiration, and EEG from face-lead locations, by embedding multiple electrodes within a standard helmet. The electrode positions are at the lower jaw, mastoids, and forehead, while for validation purposes a respiration belt around the thorax and a reference ECG from the chest serve as ground truth to assess the performance. The within-helmet EEG is verified by exposing the subjects to periodic visual and auditory stimuli and screening the recordings for the steady-state evoked potentials in response to these stimuli. Cycling and walking are chosen as real-world activities to illustrate how to deal with the so-induced irregular motion artifacts, which contaminate the recordings. We also propose a multivariate R-peak detection algorithm suitable for such noisy environments. Recordings in real-world scenarios support a proof of concept of the feasibility of recording vital signs and EEG from the proposed smart helmet

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

Signal Processing Methods for Heart Rate Detection Using the Seismocardiogram

Cardiac diseases are one of the major causes of death. Heart monitoring/diagnostic techniques have been developed over decades to address this concern. Monitoring a vital sign such as heart rate is a powerful technique for heart abnormalities detection (e.g., arrhythmia). The novelty of this work is that offers new heart rate detection methods which are both robust and adaptive compared to existing heart rate detec- tion methods. Utilized data sets in this research have been provided from two sources of PhysioNet and a research group. In this work, utilized methods for heart rate detection include Signal Energy Thresholding (SET), Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT). To the best of the author’s knowledge, this work is the first to use EMD and EWT for heart rate detection from Seismocardiogram (SCG) signal. Obtained result from applying SET to ECG signal is selected as our ground truth. Then, all three methods are used for heart rate detection from the SCG signal. The average error of SET method, EWT and EMD respectively 13.9 ms, 13.8 ms and 16 ms. Based on the obtained results, EMD and EWT are promising techniques for heart rate detection and interpretation from the SCG signal. Another contribution of this work is arrhythmia detection using EWT. EWT provides us with the instantaneous frequency changes of the corresponding modes to ECG signal. Based on the estimated power spectral density of each mode, power spectral density of arrhythmia affected ECG is higher (≥ 50dB) compared to the power spectral density of a normal ECG (≤ 20dB). This provides the potential for arrhythmia detection using EWT

Signal Processing Methods for Heart Rate Detection Using the Seismocardiogram

Cardiac diseases are one of the major causes of death. Heart monitoring/diagnostic techniques have been developed over decades to address this concern. Monitoring a vital sign such as heart rate is a powerful technique for heart abnormalities detection (e.g., arrhythmia). The novelty of this work is that offers new heart rate detection methods which are both robust and adaptive compared to existing heart rate detec- tion methods. Utilized data sets in this research have been provided from two sources of PhysioNet and a research group. In this work, utilized methods for heart rate detection include Signal Energy Thresholding (SET), Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT). To the best of the author’s knowledge, this work is the first to use EMD and EWT for heart rate detection from Seismocardiogram (SCG) signal. Obtained result from applying SET to ECG signal is selected as our ground truth. Then, all three methods are used for heart rate detection from the SCG signal. The average error of SET method, EWT and EMD respectively 13.9 ms, 13.8 ms and 16 ms. Based on the obtained results, EMD and EWT are promising techniques for heart rate detection and interpretation from the SCG signal. Another contribution of this work is arrhythmia detection using EWT. EWT provides us with the instantaneous frequency changes of the corresponding modes to ECG signal. Based on the estimated power spectral density of each mode, power spectral density of arrhythmia affected ECG is higher (≥ 50dB) compared to the power spectral density of a normal ECG (≤ 20dB). This provides the potential for arrhythmia detection using EWT

Recent development of respiratory rate measurement technologies

Respiratory rate (RR) is an important physiological parameter whose abnormity has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to do, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies

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

Vauvojen unen luokittelu patja-sensorilla ja EKG:lla

Infants spend the majority of their time asleep. Although extensive studies have been carried out, the role of sleep for infant cognitive, psychomotor, temperament and developmental outcomes is not clear. The current contradictory results may be due to the limited precision when monitoring infant sleep for prolonged periods of time, from weeks to even months. Sleep-wake cycle can be assessed with sleep questionnaires and actigraphy, but they cannot separate sleep stages. The gold standard for sleep state annotation is polysomnography (PSG), which consist of several signal modalities such as electroencephalogram, electrooculogram, electrocardiogram (ECG), electromyogram, respiration sensor and pulse oximetry. A sleep clinician manually assigns sleep stages for 30 sec epochs based on the visual observation of these signals. Because method is obtrusive and laborious it is not suitable for monitoring long periods. There is, therefore, a need for an automatic and unobtrusive sleep staging approach. In this work, a set of classifiers for infant sleep staging was created and evaluated. The cardiorespiratory and gross body movement signals were used as an input. The different classifiers aim to distinguish between two or more different sleep states. The classifiers were built on a clinical sleep polysomnography data set of 48 infants with ages ranging from 1 week to 18 weeks old (a median of 5 weeks). Respiration and gross body movements were observed using an electromechanical film bed mattress sensor manufactured by Emfit Ltd. ECG of the PSG setup was used for extracting cardiac activity. Signals were preprocessed to remove artefacts and an extensive set of features (N=81) were extracted on which the classifiers were trained. The NREM3 vs other states classifier provided the most accurate results. The median accuracy was 0.822 (IQR: 0.724-0.914). This is comparable to previously published studies on other sleep classifiers, as well as to the level of clinical interrater agreement. Classification methods were confounded by the lack of muscle atonia and amount of gross body movements in REM sleep. The proposed method could be readily applied for home monitoring, as well as for monitoring in neonatal intensive care units.Vauvat nukkuvat suurimman osan vuorokaudesta. Vaikkakin laajasti on tutkittu unen vaikutusta lapsen kognitioon, psykomotoriikkaan, temperamenttiin ja kehitykseen, selkeää kuvaa ja yhtenäistä konsensusta tiedeyhteisössä ei ole saavutettu. Yksi syy tähän on että ei ole olemassa menetelmää, joka soveltuisi jatkuva-aikaiseen ja pitkäkestoiseen unitilan monitorointiin. Vauvojen uni-valve- sykliä voidaan selvittää vanhemmille suunnatuilla kyselyillä ja aktigrafialla, mutta näillä ei voi havaita unitilojen rakennetta. Kliinisenä standardina unitilojen seurannassa on polysomnografia, jossa samanaikaisesti mitataan mm. potilaan elektroenkelografiaa, elektro-okulografiaa, elektrokardiografiaa, electromyografiaa, hengitysinduktiivisesta pletysmografiaa, happisaturaatiota ja hengitysvirtauksia. Kliinikko suorittaa univaiheluokittelun signaaleista näkyvien, vaiheille tyypillisten, hahmojen perusteella. Työläyden ja häiritsevän mittausasetelman takia menetelmä ei sovellu pitkäaikaiseen seurantaan. On tarvetta kehittää tarkoitukseen sopivia automaattisia ja huomaamattomia unenseurantamenetelmiä. Tässä työssä kehitettiin ja testattiin sydämen syke-, hengitys ja liikeanalyysiin perustuvia koneluokittimia vauvojen unitilojen havainnointiin. Luokittimet opetettiin kliinisessa polysomnografiassa kerätyllä datalla 48 vauvasta, joiden ikä vaihteli 1. viikosta 18. viikkoon (mediaani 5 viikkoa). Vauvojen hengitystä ja liikkeitä seurattiin Emfit Oy:n valmistamalla elektromekaaniseen filmiin pohjatuvalla patja-sensorilla. Lisäksi ECG:lla seurattiin sydäntä ja opetuksessa käytettiin lääkärin suorittamaa PSG-pohjaista luokitusta. Esikäsittelyn jälkeen signaaleista laskettiin suuri joukko piirrevektoreita (N=81), joihin luokittelu perustuu. NREM3-univaiheen tunnistus onnistui parhaiten 0.822 mediaani-tarkkuudella ja [0.724,0.914] kvartaaleilla. Tulos on yhtenevä kirjallisuudessa esitettyjen arvojen kanssa ja vastaa kliinikkojen välistä toistettavuutta. Muilla luokittimilla univaiheet sekoituivat keskenään, mikä on oletattavasti selitettävissä aikuisista poikeavalla REM-unen aikaisella lihasjäykkyydellä ja kehon liikkeillä. Työ osoittaa, että menetelmällä voi seurata vauvojen uniluokkien oskillaatiota. Järjestelmää voisi käyttää kotiseurannassa tai vastasyntyneiden teholla unenvalvontaan

Dirty RF Signal Processing for Mitigation of Receiver Front-end Non-linearity

Moderne drahtlose Kommunikationssysteme stellen hohe und teilweise gegensätzliche Anforderungen an die Hardware der Funkmodule, wie z.B. niedriger Energieverbrauch, große Bandbreite und hohe Linearität. Die Gewährleistung einer ausreichenden Linearität ist, neben anderen analogen Parametern, eine Herausforderung im praktischen Design der Funkmodule. Der Fokus der Dissertation liegt auf breitbandigen HF-Frontends für Software-konfigurierbare Funkmodule, die seit einigen Jahren kommerziell verfügbar sind. Die praktischen Herausforderungen und Grenzen solcher flexiblen Funkmodule offenbaren sich vor allem im realen Experiment. Eines der Hauptprobleme ist die Sicherstellung einer ausreichenden analogen Performanz über einen weiten Frequenzbereich. Aus einer Vielzahl an analogen Störeffekten behandelt die Arbeit die Analyse und Minderung von Nichtlinearitäten in Empfängern mit direkt-umsetzender Architektur. Im Vordergrund stehen dabei Signalverarbeitungsstrategien zur Minderung nichtlinear verursachter Interferenz - ein Algorithmus, der besser unter "Dirty RF"-Techniken bekannt ist. Ein digitales Verfahren nach der Vorwärtskopplung wird durch intensive Simulationen, Messungen und Implementierung in realer Hardware verifiziert. Um die Lücken zwischen Theorie und praktischer Anwendbarkeit zu schließen und das Verfahren in reale Funkmodule zu integrieren, werden verschiedene Untersuchungen durchgeführt. Hierzu wird ein erweitertes Verhaltensmodell entwickelt, das die Struktur direkt-umsetzender Empfänger am besten nachbildet und damit alle Verzerrungen im HF- und Basisband erfasst. Darüber hinaus wird die Leistungsfähigkeit des Algorithmus unter realen Funkkanal-Bedingungen untersucht. Zusätzlich folgt die Vorstellung einer ressourceneffizienten Echtzeit-Implementierung des Verfahrens auf einem FPGA. Abschließend diskutiert die Arbeit verschiedene Anwendungsfelder, darunter spektrales Sensing, robuster GSM-Empfang und GSM-basiertes Passivradar. Es wird gezeigt, dass nichtlineare Verzerrungen erfolgreich in der digitalen Domäne gemindert werden können, wodurch die Bitfehlerrate gestörter modulierter Signale sinkt und der Anteil nichtlinear verursachter Interferenz minimiert wird. Schließlich kann durch das Verfahren die effektive Linearität des HF-Frontends stark erhöht werden. Damit wird der zuverlässige Betrieb eines einfachen Funkmoduls unter dem Einfluss der Empfängernichtlinearität möglich. Aufgrund des flexiblen Designs ist der Algorithmus für breitbandige Empfänger universal einsetzbar und ist nicht auf Software-konfigurierbare Funkmodule beschränkt.Today's wireless communication systems place high requirements on the radio's hardware that are largely mutually exclusive, such as low power consumption, wide bandwidth, and high linearity. Achieving a sufficient linearity, among other analogue characteristics, is a challenging issue in practical transceiver design. The focus of this thesis is on wideband receiver RF front-ends for software defined radio technology, which became commercially available in the recent years. Practical challenges and limitations are being revealed in real-world experiments with these radios. One of the main problems is to ensure a sufficient RF performance of the front-end over a wide bandwidth. The thesis covers the analysis and mitigation of receiver non-linearity of typical direct-conversion receiver architectures, among other RF impairments. The main focus is on DSP-based algorithms for mitigating non-linearly induced interference, an approach also known as "Dirty RF" signal processing techniques. The conceived digital feedforward mitigation algorithm is verified through extensive simulations, RF measurements, and implementation in real hardware. Various studies are carried out that bridge the gap between theory and practical applicability of this approach, especially with the aim of integrating that technique into real devices. To this end, an advanced baseband behavioural model is developed that matches to direct-conversion receiver architectures as close as possible, and thus considers all generated distortions at RF and baseband. In addition, the algorithm's performance is verified under challenging fading conditions. Moreover, the thesis presents a resource-efficient real-time implementation of the proposed solution on an FPGA. Finally, different use cases are covered in the thesis that includes spectrum monitoring or sensing, GSM downlink reception, and GSM-based passive radar. It is shown that non-linear distortions can be successfully mitigated at system level in the digital domain, thereby decreasing the bit error rate of distorted modulated signals and reducing the amount of non-linearly induced interference. Finally, the effective linearity of the front-end is increased substantially. Thus, the proper operation of a low-cost radio under presence of receiver non-linearity is possible. Due to the flexible design, the algorithm is generally applicable for wideband receivers and is not restricted to software defined radios

Unen mittaaminen voimasensoreilla

This thesis presents methods for comfortable sleep measurement at home. Existing medical sleep measurement systems are costly, disturb sleep quality, and are only suited for short-term measurement. As sleeping problems are affecting about 30% of the population, new approaches for everyday sleep measurement are needed. We present sleep measurement methods that are based on measuring the body with practically unnoticeable force sensors installed in the bed. The sensors pick up forces caused by heartbeats, respiration, and movements, so those physiological parameters can be measured. Based on the parameters, the quality and quantity of sleep is analyzed and presented to the user. In the first part of the thesis, we propose new signal processing algorithms for measuring heart rate and respiration during sleep. The proposed heart rate detection method enables measurement of heart rate variability from a ballistocardiogram signal, which represents the mechanical activity of the heart. A heartbeat model is adaptively inferred from the signal using a clustering algorithm, and the model is utilized in detecting heartbeat intervals in the signal. We also propose a novel method for extracting respiration rate variation from a force sensor signal. The method solves a problem present with some respiration sensors, where erroneous cyclicity arises in the signal and may cause incorrect measurement. The correct respiration cycles are found by filtering the input signal with multiple filters and selecting correct results with heuristics. The accuracy of heart rate measurement has been validated with a clinical study of 60 people and the respiration rate method has been tested with a one-person case study. In the second part of the thesis, we describe an e-health system for sleep measurement in the home environment. The system measures sleep automatically, by uploading measured force sensor signals to a web service. The sleep information is presented to the user in a web interface. Such easy-to-use sleep measurement may help individuals to tackle sleeping problems. The user can track important aspects of sleep such as sleep quantity and nocturnal heart rate and learn how different lifestyle choices affect sleep.Unen mittaaminen voimasensoreilla Noin joka kolmannella on ongelmia unen kanssa. Nukahtamisvaikeus, heräily, huono unen laatu sekä erilaiset unenaikaiset hengitysongelmat ovat yleisiä. Helppo ja mukava unen seuranta voisi auttaa unenlaadun parantamisessa. Nykyiset mittausmenetelmät ovat kuitenkin epämukavia ja suunniteltu lähinnä lääketieteellisten diagnoosien tekemiseen. Ne eivät siis sovellu unen mittaamiseen itsenäisesti kotona. Tämä väitöskirja esittelee uuden mittausmenetelmän, joka mahdollistaa unen määrän sekä laadun mittaamisen helposti omassa sängyssä. Lakanan alle laitetaan pehmeästä kalvosta tehty anturi, joka mittaa nukkujan liikkeitä, sydämen sykettä sekä hengitystä. Anturi tunnistaa näiden mittausten perusteella useita uneen liittyviä asioita, kuten unenmäärä, kuorsaaminen sekä yön aikana mitattu leposyke. Uni-informaatio näytetään laitteen käyttäjälle verkkopalvelun tai mobiililaitteen avulla. Väitöskirjassa esitellyn unenmittausmenetelmän etu on, että syke- ja hengitystieto saadaan mitattua siitä huolimatta että anturi ei ole suoraan kosketuksissa nukkujan kehon kanssa. Kehitetyt signaalinkäsittelymenetelmät pystyvät erottamaan signaalista sykkeen ja hengityksen, sillä erilaisten mittaushäiriöiden ilmaantuminen signaaliin on otettu huomioon. Uutta unimittausmenetelmää on ehditty jo soveltaa käytännössä. Kehitetty tuote toimii siten, että mittaus lähetetään sensorilta langattomasti mobiililaitteelle, jossa unitiedot näytetään käyttäjälle. Mobiilisovellus antaa ohjeita unen parantamiseksi mittausten sekä käyttäjän profiilin perusteella