Robert Richer

Robert Richer, M. Sc.

Researcher & PhD Candidate

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

Room: Room 01.026
Carl-Thiersch-Straße 2b
91052 Erlangen
Germany

LinkedIn GoogleScholar ORCID ResearchGateGitHub


Since March ’18, Nils and me are the CEOs of Portabiles, a spin-off company from our MaD Lab.

 

since 06/2017 Researcher and PhD Candidate

Machine Learning and Data Analytics Lab,
Department of Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

10/2016-04/2017 Visiting Student and Research Assistant

Responsive Environments Group,
MIT Media Lab, Massachusetts Institute of Technology

06/2015-05/2017 M.Sc. in Medical Engineering, “Medical Image and Data Processing”

Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

Majors: Pattern Recognition, Image and Signal Processing, Human Computer Interaction
Master’s Thesis: “Exploring Interaction Concepts for a Context-aware Smart Office Prototype

06/2015-04/2017 Scientific Assistant

Central Institute for Healthcare Engineering (ZiMT)Pattern Recognition Lab (CS5),
Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

12/2011-06/2015 Student Assistant

Central Institute for Healthcare Engineering (ZiMT),
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

10/2011-06/2015 B.Sc. in Medical Engineering, “Medical Imaging Systems”

Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

Majors: Image and Signal Processing, Embedded Systems
Bachelor’s Thesis: “Novel Human Computer Interaction Concepts for Cardiac Feedback using Google Glass and Android Wear

2022

2021

2020

2019

2018

2017

2016

2015

2014

Projektseminar (PJS)

  • Projektseminar (Kurs G): Digital Psychology Lab

    Wahlpflichtveranstaltung im MSC; Studienleistung: Vorbereiten, durchführen, auswerten, präsentieren einer praktisch-empirischen Studie; Anfertigen eines Gruppenberichts über das Projekt

    • 2 SWS; ECTS-Studium (ECTS-Credits: 4)
    • Termin:
      • Mo 14:15-15:45, Room 00.010 ICS

Seminar (SEM)

  • Digital Psychology Lab

    Voraussetzungen: Im Vordergrund des Kurses steht die Verarbeitung und Analyse von biopsychologischen Daten in Python im Vordergrund. Daher sind (gute) Kenntnisse der Programmiersprache Python und damit verbundene Programmbibliotheken (NumPy, Pandas, SciPy, Matplotlib, etc.), bzw. das Interesse, sich diese Kenntnisse als Vorbereitung oder im Laufe des Kurses anzueignen, erwünscht.

    Wenn Sie Ihr Wissen prüfen oder auffrischen wollen, empfehlen wir Ihnen die folgenden Vorlesungen und Online-Ressourcen: Beachten Sie jedoch, dass einige von ihnen bei vielen Themen über die Anforderungen dieses Kurses hinausgehen!

    • [Udacity Kurs: Introduction to Python]https://www.udacity.com/course/introduction-to-python--ud1110
    • [Stanford Kurs: Introduction to Scientific Python]https://stanford.edu/~schmit/cme193/
    • [SciPy Lecture Notes]https://scipy-lectures.org/intro/index.html
    • [Introduction to Pandas for Data Science]https://www.kdnuggets.com/2020/06/introduction-pandas-data-science.html
    • [American Psychological Association – A brief introduction to Python for psychological science research]https://www.apa.org/science/about/psa/2019/07/python-research
    • [Introduction to Programming for Psychological Scientists

    ]https://github.com/ContextLab/cs-for-psych

    • [Kursaufzeichnungen Digital Psychology Lab - WS 20/21]https://www.fau.tv/course/id/1803

    Anmeldung: Studierende der Medizintechnik melden sich bei Interesse an der Lehrveranstaltung bitte vorab per Email an mad-dipsylab@fau.de an.

    • 2 SWS; Expected participants: 30; ECTS-Studium (ECTS-Credits: 5)
    • Termin:
      • Mo 14:15-15:45, Room 00.010 (exclude vac) ICS

Year Name Title
2021 Benjamin Zenke

(Master’s Thesis)

2021 Daniel Krauß Benchmarking of Sleep/Wake Detection Algorithms using Wearable Sensors and Machine Learning
(Master’s Thesis)
2021 Veronika Koch group:Influence of Acute Psychosocial Stress on Body Posture and Movement
(Master’s Thesis)
2020 Johanna Happold group:Towards Vertigo Assessment at Home: Evaluation of Orthostatic Reaction in Free-living Environments
(Bachelor’s Thesis)
2019 Katharina Jäger group:Detecting Labor in Pregnant Women with a Wearable Pregnancy Tracker using Machine Learning
(Master’s Thesis)
2019 Janis Zenkner Assessment of Cold Face Test for Stress Reduction
(Bachelor’s Thesis)
2019 Linda Vorberg Clustering of Stress Responder Types based on Diurnal Cortisol Profiles
(Bachelor’s Thesis)
2019 Lea Henrich Evaluation of IMU Orientation Estimation Algorithms Using a Three-Axis Gimbal (co-supervision
(Bachelor’s Thesis)
2019 Anke Müller A Prototype for Automated Tool Parameter Adjustments based on Machine Learning in Semiconductor Manufacturing
(Master’s Thesis)
2019 Georg Wieland Development and Evaluation of a Context-Aware Data Concentrator for Clinical Gait Analysis Systems in Home-Monitoring Scenarios (co-supervision)
(Master’s Thesis)
2018 Felix Tuchnitz Node.js-based Sensor Library Framework for a Data Concentrator in Home Monitoring
(Master’s Project)
2018 Luca Abel Classification and prediction of acute stress-induced response patterns
(Bachelor’s Thesis)
2018 Daniel Krauß Heart Rate Variability Analysis for Unsupervised Tilt Table Testing during Daily-life Activities
(Bachelor’s Thesis)
2018 Sophia Leierseder Continuous Blood Pressure Measurement During Daily-life Activities Using Pulse Transit Time
(Bachelor’s Thesis)
2018 Michael Hopfengärtner An open-source sensor platform for analysis of group dynamics
(Master’s Thesis)
2017 Insa Suchantke Application for controlling a wearable electrostimulator for the prediction of migraine attacks
(Bachelor’s Thesis)
2017 Andrea Stefke Steuerung eines Elektrostimulators zur Messung veränderter Schmerzwahrnehmung bei Migränepatienten
(Bachelor’s Thesis)