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

Analysis of Noisy Biosignals for Musical Performance

Proceeding volume: 7619Peer reviewe

Quantifying respiratory variation with force sensor measurements

Measuring the variation of the respiratory rate makes it possible to analyze the structure of sleep. The variation is high when awake or in REM sleep, and decreases in deep sleep. With sleep apnea, the respiratory variation is disturbed. We present a novel method for extracting respiratory rate variation from indirect measurements of respiration. The method is particularly suitable for force sensor signals, because, in addition to the respiratory phenomenon, they typically contain also other disturbing features, which makes the accurate detection of the respiratory rate difficult. Respiratory variation is calculated by low-pass filtering a force sensor signal at different cut-off frequencies and, at every time instant, selecting one of them for the determination of respiration cycles. The method was validated with a single-night reference recording, which showed that the proposed method detects the respiratory variation accurately. Of the 3421 calculated respiration cycle lengths, 95.9% were closer than 0.5 seconds to the reference.Peer reviewe

Sleep Musicalization: Automatic Music Composition from Sleep Measurements

Peer reviewe

Unobtrusive Online Monitoring of Sleep at Home

Peer reviewe

Adaptive Heartbeat Modeling for Beat-to-Beat Heart Rate Measurement in Ballistocardiograms

Peer reviewe

Data Musicalization

Data musicalization is the process of automatically composing music based on given data, as an approach to perceptualizing information artistically. The aim of data musicalization is to evoke subjective experiences in relation to the information, rather than merely to convey unemotional information objectively. This paper is written as a tutorial for readers interested in data musicalization. We start by providing a systematic characterization of musicalization approaches, based on their inputs, methods and outputs. We then illustrate data musicalization techniques with examples from several applications: one that perceptualizes physical sleep data as music, several that artistically compose music inspired by the sleep data, one that musicalizes on-line chat conversations to provide a perceptualization of liveliness of a discussion, and one that uses musicalization in a game-like mobile application that allows its users to produce music. We additionally provide a number of electronic samples of music produced by the different musicalization applications.Peer reviewe

Hengityksen piilomuuttujamalli mekaanisesti mitatuille sydämenlyönneille

In this thesis, a latent variable model for the respiratory variation of mechanically measured heartbeats is presented. The effect of respiration on heartbeats in a mechanically measured cardiac signal was noticed already in the first respiratory ballistocardiography studies in the 1920s. The effect was described as a modulation of the heartbeat amplitude, and that view has persisted. Although a reasonable approximation, amplitude modulation is not an accurate description of the respiratory effect. In the model presented here, the respiratory variation is described with a linear latent variable model, where the direction of variation can be distinct from the direction of amplitude. The model was evaluated with seven ballistocardiograms from three healthy test subjects. Based on a Bayesian information criterion analysis, it was found to describe the variation accurately in all the cases. As the modelling of the heartbeat shape is improved, existing heartbeat detection methods can be made more accurate by applying the proposed model.Tässä diplomityössä esitetään lineaarinen piilomuuttujamalli, joka kuvaa hengityksen aiheuttamaa sykemuodon vaihtelua. Hengityksen vaikutus sydämen mekaanista toimintaa mittaavaan signaaliin huomattiin jo 1920-luvulla ensimmäisissä ballistokardiografisissa tutkimuksissa, jotka keskittyivät hengityksen vaikutuksen mittaamiseen. Kyseisissä tutkimuksissa todettiin, että hengitys aiheuttaa sykemuodon amplitudin vaihtelua. Vaikka amplitudin vaihtelu onkin kohtuullinen approksimaatio, se ei kuvaa hengityksen aiheuttamia sykemuodon muutoksia tarkasti. Esitetty uusi malli kuvaa sykemuodon vaihtelun joustavammin, jolloin myös muutkin kuin amplitudin muutokset ovat mahdollisia. Piilomuuttujamallin tarkkuutta arvioitiin kolmelta koehenkilöltä mitattujen seitsemän ballistokardiogrammin perusteella. Malli on Bayesilaiseen informaatiokriteeriin perustuvan arvioinnin perusteella tarkka. Piilomuuttujamalli parantaa sykemuodon mallinnusta, joten sitä voidaan soveltaa olemassa olevien sykkeentunnistusmenetelmien parantamiseksi