Design and Implementation of a Scalable Crowdsensing Platform for Geospatial Data of Tinnitus Patients

Smart devices and low-powered sensors are becoming increasingly ubiquitous and nowadays almost all of these devices are connected, which is a promising foundation for crowdsensing of data related to various environmental phenomena. Resulting data is especially meaningful when it is related to time and location. Interestingly, many existing approaches built their solution on monolithic backends that process data on a per-request basis. However, for many scenarios, such technical setting is not suitable for managing data requests of a large crowd. For example, when dealing with millions of data points, still many challenges arise for modern smartphones if calculations or advanced visualization features must be accomplished directly on the smartphone. Therefore, the work at hand proposes an architectural design for managing geospatial data of tinnitus patients, which combines a cloudnative approach with Big Data concepts used in the Internet of Things. The presented architectural design shall serve as a generic foundation to implement (1) a scalable backend for a platform that covers the aforementioned crowdsensing requirements as well as to provide (2) a sophisticated stream processing concept to calculate and pre-aggregate incoming measurement data of tinnitus patients. Following this, this paper presents a visualization feature to provide users with a comprehensive overview of noise levels in their environment based on noise measurements. This shall help tinnitus or hearing-impaired patients to avoid locations with a burdensome sound level

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

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

Differential Analysis of Acoustical Smartphone Recording Capabilities - a Contribution towards Smartphone-modulated Perception of Tinnitus

Loud noise is a common risk factor for physical and mental health in our industrialized world, which can trigger different sorts of health issues like permanent hearing loss and tinnitus. To mitigate noise-induced problems in daily life, smartphones can be used as an easy way to observe noise levels. As recording quality differs depending on smartphone models and calibration techniques, standardized methods are needed to acquire comparable results. To examine such possibilities in more detail, several acoustical experiments were performed regarding the recording capabilities of in-build smartphone microphones compared to an external microphone to figure out optimal smartphone recording conditions as this further increases measurement accuracy. Additionally, various different calibration approaches differing in effort and accuracy are evaluated. Results show that smartphones are capable of measuring sound pressure levels accurately with only small deviations of about +-3 dB(A). Moreover, smartphone microphones are heavily frequency dependent, which is why an approach was presented to normalize for these variations. Gathered calibration data was further brought in conjunction with sound perception data of tinnitus probands, to show an application in health issues. The presented methods provide a straightforward approach to measure sound levels with a smartphone and compare them to other device conditions, opening the use of smartphones in the modulation of sound perception in tinnitus and other conditions