Dr.-Ing. Jennifer Zeilfelder

Research Scientist

Werdegang

Jennifer Zeilfelder studied mechanical engineering at the Karlsruhe Institute of Technology (KIT) with a focus on mechatronics and medical technology. Already in 2013, she began working as a student assistant at the FZI and gained expertise in the field of biosignal detection and processing. In her master thesis entitled "Development of a Concept for Continuous, Non-Invasive Blood Pressure Measurement in the Ear", which she also wrote at the FZI, she was able to realize her own idea. From September 2015 to May 2016 she was a scholarship holder and since June 2016 she has been working as a research assistant at FZI.

 

Publications

Conference Proceedings (7)

1. A jaw based human-machine interface with machine learningInfoDetails

   Tobias Busch, Jennifer Zeilfelder, Kai Zhou, Wilhelm Stork, 2019

   For a successful classification of jaw movement signals three different machine learning methods are implemented and evaluated: A Naive Bayes classifier, a Support Vector Machine (SVM) and a Convolutional Neuronal Network (CNN). In this case the present paper deals with the definition of intuitive jaw movements and their detection by a prototypical system for non-invasive measurement of pressure values in the closed external auditory canal. For this purpose, a database with 1013 recordings was created. The evaluation shows that all three learning methods provide a good recognition level. The Bayes classifier achieves with the used, small dataset with nearly 95% the best detection probability with realtime data. At the same time, the study shows that individual pressure profiles can severely impair recognition. Hence, the creation of a comprehensive database is required. Overall, this paper demonstrates that successful detection and differentiation of up to five jaw movement directions is possible. In total it could be shown using machine learning for jaw movement classification is a stable method with a high recognition level. However, the quality of classification increases with the size and diversity of the dataset.

2. Concept for a Permanent, Non-Invasive Blood Pressure Measurement in the EarInfoDetails

   Jennifer Zeilfelder, Matthias Diehl, Christian Pylatiuk, Wilhelm Stork , 2019

   In this paper a concept for a new method for a permanent, non-invasive blood pressure measurement in the ear is presented. Currently, blood pressure is measured about once a day and used as the basis for therapy. In order to enable an individual and as mild as possible therapy, a permanent, noninvasive measuring method is required. With every heartbeat, the arteries in the body expand and contract. Blood pressure is the pressure acting on an artery, the higher the pressure the greater the enlargement. If the external auditory canal is closed airtight, the increase and decrease in size of the arteries during the heartbeat causes a change in volume of the closed air chamber and thus a pressure fluctuation within it. The theory is, that these fluctuations represent the blood pressure. In order to prove the theory a prototype was set up, containing a pressure sensor (integrated into an Alpine InEar for airtight sealing), a micro controller and a reference ECG. The sensor was placed airtight in the ear. One test person showed perfect results, in which after each heartbeat in the reference system a corresponding signal was also visible in the ear system. The recorded curve resembles a blood pressure curve, which proves that it is in principle possible to measure blood pressure with such a system. For absolute blood pressure values, the system must be supplemented with further components, this is being researched in the project MikroBO 1 funded by the Federal Ministry of Education and Research of Germany.

3. Evaluation of a smart drink monitoring deviceDetails

   Christoph Zimmermann, Jennifer Zeilfelder, Timon Blöcher, Matthias Diehl, Stephan Essig, Wilhelm Stork, 2017

4. Akzeptanz und Marktpotenzial eines intelligenten Trinkassistenz-SystemsDetails

   C. Zimmermann, N. Müller, N. Göpper, J. Zeilfelder, T. Blöcher, F. Gauger, J. Schneider, W. Stork, T. Vetter , S. Jahn und A. Gross, 2016

5. Kontinuierliches Patienten Monitoring bei der Intensivpflege im häuslichen UmfeldDetails

   J. Zeilfelder, F. Mohr, C. Zimmermann, J. Parada, T. Blöcher, J. Schneider, F. Gauger, W. Stork, 2016

6. Portable auricular device for real-time swallow and chew detectionDetails

   C. Steimer, C. Zimmermann, J. Zeilfelder, C. Pylatiuk, W. Stork, 2016

7. A human-machine interface based on tongue and jaw movementsInfoDetails

   Jennifer Zeilfelder, Tobias Busch, Christoph Zimmermann, Wilhelm Stork

   This paper describes a human-machine interface (HMI) that works on the basis of pressure changes, caused by tongue and jaw movements, in the closed, external auditory canal This work covers the definition of specific tongue movement sequences which are suitable for detection in the outer auditory canal. Furthermore an algorithmic concept for detection and differentiation of four movements based on pressure changes in the auditory canal is developed. Using the correlation coefficient of a recorded calibration and the current signal the problem of a deviating temporal course, caused by different speeds of movement and variation of the amplitude spectrums, is solved. As the calculation requires the complete capture of the movement signals, a real-time detection is not possible. However, for the recognition it is necessary to detect the entire motion signal. Due to the observed dependency between signal generation and jaw movement an additional examination of two jaw movements was conducted. After movement definition, a real-time capable algorithm for detection and differentiation was developed and subsequently implemented on an existing system for pressure value detection. Afterwards both resulting systems were tested and evaluated on study participants. Thereby it was demonstrated that the detection und differentiation of four tongue movements is possible. In total it could be shown that tongue and jaw movements are suitable input mechanisms for human machine interfaces and bear a great potential for future developments.

Theses (1)

1. Parametererfassung am menschlichen Ohr auf Basis von Druckänderungen im äußeren GehörgangDetails

   Jennifer Zeilfelder, 2019

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Kontakt

Telefon: +49 721 9654-752
E-Mail: Zeilfelder@dont-want-spam.fzi.de

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