Spontaneous synchronization to speech reveals neural mechanisms facilitating language learning

We introduce a deceptively simple behavioral task that robustly identifies two qualitatively different groups within the general population. When presented with an isochronous train of random syllables, some listeners are compelled to align their own concurrent syllable production with the perceived rate, whereas others remain impervious to the external rhythm. Using both neurophysiological and structural imaging approaches, we show group differences with clear consequences for speech processing and language learning. When listening passively to speech, high synchronizers show increased brain-to-stimulus synchronization over frontal areas, and this localized pattern correlates with precise microstructural differences in the white matter pathways connecting frontal to auditory regions. Finally, the data expose a mechanism that underpins performance on an ecologically relevant word-learning task. We suggest that this task will help to better understand and characterize individual performance in speech processing and language learning

Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study

Background: Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient’s quality of life. TheTrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)—Android and iOS—to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider. Objective: In this study, we explored whether the mobile OS—Android and iOS—used during user assessments can be predicted by the dynamic daily-life TYT data. Methods: TYT mainly applies the paradigms ecological momentary assessment (EMA) and mobile crowdsensing to collect dynamic EMA (EMA-D) daily-life data. The dynamic daily-life TYT data that were analyzed included eight questions as part of the EMA-D questionnaire. In this study, 518 TYT users were analyzed, who each completed at least 11 EMA-D questionnaires. Out of these, 221 were iOS users and 297 were Android users. The iOS users completed, in total, 14,708 EMA-D questionnaires; the number of EMA-D questionnaires completed by the Android users was randomly reduced to the same number to properly address the research question of the study. Machine learning methods—a feedforward neural network, a decision tree, a random forest classifier, and a support vector machine—were applied to address the research question. Results: Machine learning was able to predict the mobile OS used with an accuracy up to 78.94% based on the provided EMA-D questionnaires on the assessment level. In this context, the daily measurements regarding how users concentrate on the actual activity were particularly suitable for the prediction of the mobile OS used. Conclusions: In the work at hand, two particular aspects have been revealed. First, machine learning can contribute to EMA-D data in the medical context. Second, based on the EMA-D data of TYT, we found that the accuracy in predicting the mobile OS used has several implications. Particularly, in clinical studies using mobile devices, the OS should be assessed as a covariate, as it might be a confounder

Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform

Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case

Comprehensive insights into the TrackYourTinnitus database

The ubiquity of smart mobile devices facilitates data collection in the healthcare domain. Two of the concepts, which can be applied in this context, are mobile crowdsensing (MCS) and ecological momentary assessment (EMA). TrackYourTinnitus (TYT) is an advanced mobile healthcare platform that combines both concepts enabling the monitoring and evaluation of the users’ individual variability of tinnitus symptoms. This paper describes the underlying data set and structure of the TYT mobile platform and highlights selected issues whose investigation provides advanced insights into the users of this mobile platform as well as their data

Evaluating Sensor Data in the Context of Mobile Crowdsensing

With the recent rise of the Internet of Things the prevalence of mobile sensors in our daily life experienced a huge surge. Mobile crowdsensing (MCS) is a new emerging paradigm that realizes the utility and ubiquity of smartphones and more precisely their incorporated smart sensors. By using the mobile phones and data of ordinary citizens, many problems have to be solved when designing an MCS-application. What data is needed in order to obtain the wanted results? Should the calculations be executed locally or on a server? How can the quality of data be improved? How can the data best be evaluated? These problems are addressed by the design of a streamlined approach of how to create an MCS-application while having all these problems in mind. In order to design this approach, an exhaustive literature research on existing MCS-applications was done and to validate this approach a new application was designed with its help. The procedure of designing and implementing this application went smoothly and thus shows the applicability of the approach

Konzeption einer modernen Web-Application zur Verwaltung von Dynamischen Mobile Crowdsensing Plattformen im Healthcare Bereich

Steigender Stress und Leistungsdruck in der Schule oder später im Berufsleben können unter anderem zu psychischen Erkrankungen, Tinnitus oder Bluthochdruck führen. Viele Betroffene finden keine Zeit mehr für sich selber um den angesammelten Stress und Leistungsdruck abbauen zu können. Durchgeführte Studien belegen Zusammenhänge zwischen Stress und Tinnitus und zeigen die Auswirkungen auf die Betroffenen. Dieses Projekt wurde gegründet, um noch mehr spezifische Daten zu erhalten, diese zu analysieren und um den Betroffenen anschließend direkt persönliche Informationen hierzu bereitstellen zu können. Die Basis hierfür bildet eine Vielzahl an bereits vorhandenen Studien, sowie die darin enthaltenen Fragebögen, welche intervallabhängig wiederholt werden können. Die Benutzer der Webapplikation können bequem von zu Hause aus die Fragebögen beantworten und erhalten anschließend individuelle, auf den Benutzer angepasste Informationen. Dabei werden die Informationen graphisch dargestellt um eine schnelle und übersichtliche Anschauung zu ermöglichen und um Veränderungen direkt erkennen zu können

Konzeption und Implementierung einer Feature-rich Webplattform zur Unterstützung von Studien zur Stressreduzierung auf Basis des Mindful Walking Paradigmas

Immer häufiger werden im Gesundheitssektor mobile Anwendungen zur Gesundheitsvorsorge oder Behandlung von chronischen Krankheiten eingesetzt. Aufgrund ihrer ständigen Verfügbarkeit und Ausstattung mit diversen Sensoren eröffnen Smartphones Chancen für die Entwicklung von Anwendungen, mit deren Hilfe Nutzer gezielt bei der Reduzierung von Krankheitssymptomen unterstützt werden können. Gerade im Zeitalter der Leistungsgesellschaft hat ständig auftretender Stress einen negativen Einfluss auf die Gesundheit der Menschen. Durch den Einsatz von Achtsamkeitsübungen kann das Stresslevel effektiv gesenkt und die damit einhergehenden Symptome abgeschwächt werden. Ein Beispiel für eine körperbezogene Achtsamkeitsübung stellt Mindful Walking dar, bei der Nutzer ihre Aufmerksamkeit vollständig auf das Gehen und die dazugehörigen Bewegungen lenken. Durch den Einsatz von Smartphone-Apps ist es möglich, Nutzer adäquat anzuleiten, Achtsamkeitsübungen wie Mindful Walking korrekt durchzuführen. Darüber hinaus können Daten von Benutzern dieser Anwendungen gesammelt und von Forschern analysiert werden, um wertvolle Informationen über die Auswirkungen dieser Übungen auf die Gesundheit abzuleiten. In der vorliegenden Arbeit wird eine Webplattform entwickelt, um Daten von Teilnehmern der Mindful Walking Studie des Instituts für Datenbanken und Informationssysteme der Universität Ulm, die mit einer bestehenden mobilen Anwendung gesammelt werden, zu verarbeiten. Dazu wird eine geeignete Schnittstelle und Webanwendung implementiert, um ausgefüllte Fragebögen und Übungsdaten von Nutzern dauerhaft zu speichern und für Forscher geeignet über das System auszuwerten bzw. in Form von Diagrammen zu visualisieren

Machine Learning Findings on Geospatial Data of Users from the TrackYourStress mHealth Crowdsensing Platform

Mobile apps are increasingly utilized to gather data for various healthcare aspects. Furthermore, mobile apps are used to administer interventions (e.g., breathing exercises) to individuals. In this context, mobile crowdsensing constitutes a technology, which is used to gather valuable medical data based on the power of the crowd and the offered computational capabilities of mobile devices. Notably, collecting data with mobile crowdsensing solutions has several advantages compared to traditional assessment methods when gathering data over time. For example, data is gathered with high ecological validity, since smartphones can be unobtrusively used in everyday life. Existing approaches have shown that based on these advantages new medical insights, for example, for the tinnitus disease, can be revealed. In the work at hand, data of a developed mHealth crowdsensing platform that assesses the stress level and fluctuations of the platform users in daily life was investigated. More specifically, data of 1797 daily measurements on GPS and stress-related data in 77 users were analyzed. Using this data source, machine learning algorithms have been applied with the goal to predict stress-related parameters based on the GPS data of the platform users. Results show that predictions become possible that (1) enable meaningful interpretations as well as (2) indicate the directions for further investigations. In essence, the findings revealed first insights into the stress situation of individuals over time in order to improve their quality of life. Altogether, the work at hand shows that mobile crowdsensing can be valuably utilized in the context of stress on one hand. On the other, machine learning algorithms are able to utilize geospatial data of stress measurements that was gathered by a crowdsensing platform with the goal to improve the quality of life of its participating crowd users