Ann-Kristin Seifer

Ann-Kristin Seifer, M. Sc.

Researcher & PhD Candidate

Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Maschinelles Lernen und Datenanalytik

Room 01.010
Carl-Thiersch-Str. 2b
91052 Erlangen
Germany

Phone number: +49 9131 85-20380
Email: ann-kristin.seifer@fau.de

LinkedIn: Page of Ann-Kristin Seifer

Academic CV

since 10/2020	Researcher and PhD Candidate Machine Learning and Data Analytics Lab Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
10/2017-09/2020	M.Sc. in Medical Engineering, “Medical Image and Data Processing” Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Majors: Pattern Recognition, Image Processing, Biomedical Signal Processing, Deep Learning
12/2019-06/2020	Master’s Thesis Title: “Predicting Hearing Aid Settings Based on Audiometric and Subject-Related Data: A Machine Learning Approach” WS Audiology, Erlangen Pattern Recognition Lab, Computer Science, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU)
02/2018-11/2019	Working Student Audiology Department at WS Audiology, Erlangen
10/2013-03/2018	B.Sc. in Medical Engineering, “Medical Imaging Systems” Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Majors: Image and Signal Processing Bachelor’s Thesis: “Gait Analysis Using Foot-mounted Sensors and Optimal Control Simulation ”
11/2016-02/2017	Internship Philips Research, Hamburg Topic: Compensation of Segmentation Differences Between Different Imaging Modalities

Research Projects

Activity Recognition using IMU Sensors integrated in Hearing Aids

Publications

2023

Seifer AK., Dorschky E., Küderle A., Moradi H., Hannemann R., Eskofier B.:
EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors
In: Sensors 23 (2023), Article No.: 6565
ISSN: 1424-8220
DOI: 10.3390/s23146565
URL: https://www.mdpi.com/1424-8220/23/14/6565
BibTeX: Download

2022

Moradi H., Roth N., Seifer AK., Eskofier B.:
Detection of distorted gait and wearing-off phenomenon in Parkinson's disease patients during Levodopa therapy
4th IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) / 18th IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) (Ioannina)
In: 2022 IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI) JOINTLY ORGANISED WITH THE IEEE-EMBS INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN'22), NEW YORK: 2022
DOI: 10.1109/BHI56158.2022.9926873
BibTeX: Download
Ollenschläger M., Küderle A., Mehringer W., Seifer AK., Winkler J., Gaßner H., Eskofier B., Kluge F.:
MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data
In: Sensors 22 (2022)
ISSN: 1424-8220
DOI: 10.3390/s22155849
BibTeX: Download
Truong MT., Nwosu OB., Gaytan Torres ME., Segura Vargas MP., Seifer AK., Nitschke M., Ibrahim A., Knitza J., Krusche M., Eskofier B., Schett G., Morf H.:
A Yoga Exercise App Designed for Patients With Axial Spondylarthritis: Development and User Experience Study
In: JMIR Formative Research 6 (2022), p. e34566
ISSN: 2561-326X
DOI: 10.2196/34566
URL: https://formative.jmir.org/2022/6/e34566
BibTeX: Download

2020

Seifer AK., Schinkel-Bielefeld N., Schröter H., Escalante Banuelos A., Hoppe U., Maier A.:
Predicting Hearing Aid Fittings Based on Audiometric and Subject-Related Data: A Machine Learning Approach
Virtual Conference on Computational Audiology (VCCA2020) (Virtual, 19. June 2020 - 19. June 2020)
In: Virtual Conference on Computational Audiology (VCCA2020) 2020
URL: https://computationalaudiology.com/predicting-hearing-aid-fittings-based-on-audiometric-and-subject-related-data-a-machine-learning-approach/
BibTeX: Download

2019

Dorschky E., Nitschke M., Seifer AK., van den Bogert AJ., Eskofier B.:
Estimation of Gait Kinematics and Kinetics from Inertial Sensor Data Using Optimal Control of Musculoskeletal Models
In: Journal of Biomechanics (2019)
ISSN: 0021-9290
DOI: 10.1016/j.jbiomech.2019.07.022
URL: https://www.mad.tf.fau.de/files/2019/08/201908_JBiomech_Dorschky.pdf
BibTeX: Download

2018

Nitschke M., Dorschky E., Seifer AK., Schlarb H., van den Bogert AJ., Eskofier B.:
Optimal Control Simulation of a 2D Biomechanical Model for Sensor-Based Gait Analysis
Summer School "Humans in Motion" (Heidelberg, 23. September 2018 - 28. September 2018)
URL: https://www.mad.tf.fau.de/files/2018/10/2018_09_18_Nitschke_HumansInMotion.pdf
BibTeX: Download

Teaching

Scrum Master of several teams in the Innovation Lab for Wearable and Ubiquitous Computing:
- swimolution
- Traffix
- HeaR
- YogiTherapy

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Student Supervision

Year	Type	Student	Titel
2023	Student Assistant	Lukas Jahnel	Implementation and Evaluation of a Gait Sequence Detector
2023	Student Assistant	Luzie Richter	Data acquisition and analysis
2023	Master Thesis	Johanna Schwarz	Speed Estimation using Wrist-Worn IMUs (in progress)
2023	Master Thesis	Friederike Popp	Sit-to-stand transfer (in progress)
2022	Student Assistant	Alexander Schmieg	Implementation of a Deep Learning model for gait parameter estimation
2022	Master Thesis	Obioma Nwosu	Development of E-Health Application for Rheumatic Patients (YogiTherapyApp)
2022	Master Thesis	Maximilian Vogel	Compressed Deep Learning Model for Human Activity Recognition
2022	Student Assistant	Fatemeh Rezaei	Motion Data Analysis
2021	Bachelor Thesis	Svenja Mitrovics	Gait Event Detection using IMU Sensors Integrated Into Hearing Aids
2021	Student Assistant	Lara Dittmann	Motion Data Analysis
2021	Bachelor Thesis	Jana Kiederle	Motion Analysis using IMU Sensors Integrated Into Hearing Aids