Markus Schinle

Wissenschaftlicher Mitarbeiter

Werdegang

Markus Schinle studierte von 2009 bis 2012 Wirtschaftsinformatik (B. Sc.) an der Hochschule Karlsruhe und schloss sein Masterstudium der Informationswirtschaft (M. Sc.) am Karlsruher Institut für Technologie (KIT) mit Aufenthalt an der Eidgenössische Technische Hochschule Zürich (ETH) im Jahr 2015 ab. Seine Studienschwerpunkte lagen in der Entwicklung von Informationssystemen, mobilen Anwendungen, Geschäftsmodellen, -prozessen und Entrepreneurship. Seine Masterarbeit mit dem Thema "Entwicklung eines auf mobilen Endgeräten basierenden Smart-Home-Systems im Kontext des Internet-der-Dinge (IoT)" schrieb er bereits am FZI.

Seit Dezember 2015 ist er als wissenschaftlicher Mitarbeiter am FZI angestellt.

Weitere Informationen:

ResearchGate

LinkedIn

XING

Publikationen

Konferenzbeitrag (6)

1. Assistierte Kommunikation in ambulanten BetreuungsformenInfoDetails

   M. Schinle, T. Födisch und W. Stork, 2018

   Ziel des Projektes "Assistent" ist es, relevante Informationen für die Kommunikation zwischen informeller und professioneller Pflege gegenseitig im Sinne einer optimalen Betreuung bereitzustellen. Diese Informationen beziehen sich sowohl auf Alltagsaktivitäten (erkannt durch Monitoring-Systeme), versorgungsrelevante Informationen (z.B. Besuchszeiten) als auch auf vereinzelte Leistungen, welche über die Pflegedokumentation für die professionelle Pflege erfasst werden. Bisherige Lösungen befassen sich meist mit der Dokumentation der Pflege für jeweils entweder nur die informelle oder die professionelle Pflege. Durch Assistent soll Angehörigen und professionell Pflegenden ein einfacherer Austausch zur Betreuungssituation älterer, unterstützungsbedürftiger Personen ermöglicht werden.

2. Personalization of monitoring system parameters to support ambulatory care for dementia patientsInfoDetails

   M. Schinle, I. Papantonis und W. Stork, 2018

   Dementia affected 47 million people worldwide in 2016, which caused $ 818 billions of estimated costs in total. Due to high costs and inadequate governmental support, over 90 % of people with dementia in low- and middle-income countries need to be cared for at home. However, the multitude of implications overburdens both the people affected as well as their caregivers. Existing health monitoring systems for ambulatory care only offer dementia-specific support functions in combination with a variety of sensors, applications and administration efforts. Therefore, we present a method to support ambulatory care with simple ambient sensor settings. Our approach is intended to model the behaviour of the resident to derive habits and possible anomalies regarding dementia. To support planning of care measures and detect dementia onset, we start with day-night rhythm and night-time activity as relevant parameters, since they are associated with dementia and are beneficial for the implementation of care. Our primary objective is to automate the personalization of existing monitoring systems with as few ambient sensors as possible. The challenge of this work is to learn these parameters from a brief sensor data history about people who are living alone, are unable to handle wearable devices and cannot give autonomous feedback on their activities of daily living.

3. An Approach to digitalize Psychological Tests to support Diagnosis of Alzheimer’s Disease in Ambulatory CareInfoDetails

   M. Schinle, D. Wyszka, W. Stork, F. Schwärzler, K. Volz, M.-A. Ruby, E. Sejdinovic, 2018

   The number of people suffering dementia is expected to nearly triple until 2050. Unfortunately, diagnosis is often provided too late or not at all, although the progression of the disease could be positively influenced by early detection. Reasons for this can be seen in the stigmatizing effect of the diagnosis and the lack of medical care especially in low- and middle-income countries. For diagnosing dementia, a variety of causal diseases need to be differentiated. An initial etiological differentiation and classification can be made by testing based on the clinical characteristics, such as memory loss or limited learning ability. This allows the derivation of progress and treatment of this syndrome. Therefore, we digitalized a combination between established, empirically validated assessments and new approaches to get a variation of measurements about cognition-, memory- and behaviour-related symptoms regarding the most common type of dementia. This paper will work out how the digitalization and enhancement of standardized psychological tests can support the diagnosis of Alzheimer’s Disease (AD) and which challenges this entails.

4. A modular approach for smart home system architectures based on Android applicationsInfoDetails

   M. Schinle, J. Schneider, T. Blöcher, J. Zimmermann, S. Chiriac und W. Stork , 2017

   European market size for smart home systems is expected to grow by 20% to over 4.3 billion USD by 2017. This growth is mainly due to the development of luxury and premium markets towards a mass market. In 2014, more than one billion mobile devices with Google’s operating system Android were sold, which resulted in a market share of more than 80%. Thus, mobile devices have already reached the mass market. The main idea behind this work is the reuse of mobile devices as smart home systems to reduce market entry barrier for end users and to contribute to a more holistic use of mobile devices. Therefore, we developed a modular architecture concept for smart home systems based on Android devices. In order to ensure the usefulness of this concept, the characteristics of existing systems and the potential of mobile devices were taken into account. Our primary goal was the development of a generic software architecture for various smart home applications. Therefore, we present a plugin framework concept that allows modular system design. Our framework is implemented by using components without modifying the Android operating system itself.

5. An online PPGI approach for camera based heart rate monitoring using beat-to-beat detectionInfoDetails

   T. Blöcher, J. Schneider, M. Schinle und W. Stork, 2017

   In this work an online method for camera-based heart rate detection, also known as Photoplethysmography Imaging (PPGI), is presented. The pulse related signal is obtained from RGB videos of the human face, using an off the shelf camera under ambient light conditions. The algorithm for heart rate estimation is based on the beat-to-beat analysis of the PPGI signal, allowing the estimation of psychophysiological parameters like the heart rate variability. The system is tested indoors under laboratory conditions, and in field trials integrated in a car. Field tests are done as a step towards robust appraisal of psycho-physiological driver states. In measurements with eight subjects, a sensitivity of ~93% for pulse peak detection is reached under resting and constant light conditions. First test drives show good results for heart rate estimation while driving on highways outside of city traffic. The results indicate that PPGI can be a suitable solution for providing vital sign information of drivers in cars.

6. Die Wohnung als Standort der Gesundheitsversorgung – Informatik in der guten StubeDetails

   M. Behrends, M. Witte, R. Eckert, M. Schinle und M. Gietzelt, 2016

Export Suchergebnis .bib

Kontakt

Telefon: +49 721 9654-756
E-Mail: schinle@dont-want-spam.fzi.de

Zurück