Lehrstuhl für Maschinelles Lernen und Datenanalytik
A look inside the human body - gait analysis and simulation [GAS] -
- Lecturer:
- Anne Koelewijn
- Details:
- Vorlesung, 2 cred.h, ECTS: 2,5, nur Fachstudium, Organisation and slides via StudOn. Grading: written or oral exam
- Dates:
- Wed, 16:15 - 17:45, H15
Organisation and slides via StudOn.
- Fields of study:
- WPF INF-MA ab 1
WF EEI-MA ab 1
WF MT-MA-BDV ab 1
WF MT-MA-GPP ab 1
WF MT-MA-MEL ab 1
- Keywords:
- gait analysis, trajectory optimization, gait simulation, inverse kinetic, inverse kinematics, muscle modelling, musculoskeletal models, metabolic energy
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Algorithmen und Datenstrukturen (für Medizintechnik) Rechnerübung [AuD-MT-RUE] -
- Lecturers:
- Robert Richer, René Raab
- Details:
- Übung, 2 cred.h, ECTS: 2,5, für Anfänger geeignet, Frühstudium, Die Rechnerübungen beginnen in der zweiten Vorlesungswoche!
- Fields of study:
- PF MT-BA 1
WF MT-MA 1-2
PF WINF-BA 1
PF DS-BA 1
- Prerequisites / Organisational information:
- Alle Rechnerübungen finden ausschließlich digital (Zoom) statt.
- Keywords:
- Java, Objektorientierte Programmierung, Algorithmen, Datenstrukturen
| | Tue | 8:15 - 9:45 | Zoom-Meeting | |
Rottler, M. Martini, T. |
Alle Rechnerübungen finden digital (Zoom) statt. |
| Tue | 12:15 - 13:45 | Zoom-Meeting | |
Meißner, H. Simpetru, R.C. |
Alle Rechnerübungen finden digital (Zoom) statt. |
| Thu | 8:15 - 9:45 | Zoom-Meeting | |
Stühler, S. Schröder, C. |
Alle Rechnerübungen finden digital (Zoom) statt. |
| Thu | 10:15 - 11:45 | Zoom-Meeting | |
Burg, N. Meißner, H. |
Alle Rechnerübungen finden digital (Zoom) statt. |
| Thu | 12:15 - 13:45 | Zoom-Meeting | |
Krauß, D. Nguyen, Q. |
Alle Rechnerübungen finden digital (Zoom) statt. |
Algorithmen und Datenstrukturen (für Medizintechnik) Tafelübung [AuD-MT-TUE] -
- Lecturers:
- Robert Richer, René Raab
- Details:
- Übung, 2 cred.h, certificate, ECTS: 2,5, für Anfänger geeignet, Frühstudium, Die Tafelübung beginnt in der ersten, die Rechnerübungen in der zweiten Vorlesungswoche!
- Fields of study:
- PF DS-BA 1
PF MT-BA 1
WF MT-MA 1-2
PF WINF-BA 1
- Prerequisites / Organisational information:
- Alle Tafelübungen finden ausschließlich digital (Zoom) statt. Es stehen stehen Videoaufzeichnungen der Tafelübungsinhalte bereit, in den Zoom-Meetings werden diese Inhalte vertieft und durch Tutorien ergänzt.
- Keywords:
- Java, Objektorientierte Programmierung, Algorithmen, Datenstrukturen
| | Wed | 14:15 - 15:45 | Zoom-Meeting | |
Richer, R. Raab, R. |
Das Zoom-Meeting findet live als interaktives Tutorium statt |
Becoming an innovative engineer [InnoEng] -
- Lecturers:
- Björn Eskofier, Marlies Nitschke
- Details:
- Vorlesung, 2 cred.h, ECTS: 2,5
- Dates:
- to be determined
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Bewegungsanalyse und biomechanische Grenzgebiete [BABG] -
- Lecturer:
- Anne Koelewijn
- Details:
- Vorlesung, 2 cred.h, ECTS: 2,5
- Dates:
- to be determined
- Fields of study:
- WF MT-BA ab 4
WF MT-MA ab 1
WF MT-MA-BDV ab 1
WF MT-BA-BV ab 4
WF MT-MA-MEL ab 1
WF MT-MA-IDP ab 1
WF MT-BA-GP ab 4
- Prerequisites / Organisational information:
- Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt über: https://www.vhb.org/
- Contents:
- • Anatomie des menschlichen Bewegungsapparates
Muskeln, Sehnen, Bänder, Knochen, Knorpel• Gelenkmechanik • Kinematik
Bewegungsanalyse und Motion-Capturing-Systeme • Kinetik
Kraft- & Druckmessplatten, Bodenreaktionskräfte • Elektromyographie • 3D-Modellierung in der Biomechanik Segmentierung, 3D-Modelle • Simulation
FEM, MKS
- Recommended literature:
- Relevante Literatur ist im online-Kurs zu den jeweiligen Kapiteln angegeben.
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Human Computer Interaction [HCI] -
- Lecturer:
- Björn Eskofier
- Details:
- Vorlesung, 3 cred.h, ECTS: 3,75
- Dates:
- Mon, 8:15 - 9:45, H10
Fri, 10:15 - 11:45, H10
- Fields of study:
- WPF INF-MA ab 1
WPF INF-BA ab 5
WF CE-MA ab 1
WF ICT-MA ab 1
WF IuK-BA ab 5
WPF ICT-MA-ES ab 1
WPF ICT-MA-MPS ab 1
WPF ICT-MA-NDC ab 1
WF EEI-MA ab 1
WF EEI-BA ab 5
WPF MT-MA-BDV ab 1
WF MT-MA-GPP ab 1
WF MT-MA-MEL ab 1
WPF MT-BA ab 5
WPF ASC-MA ab 1
WPF AI-MA ab 1
- Prerequisites / Organisational information:
- Folien zur Vorlesung und Organisation über Studon.
- Contents:
- Studon Kurs:
https://www.studon.fau.de/crs3663032.html
Aufgrund der derzeitigen Corona-Lage findet die Vorlesung digital statt. Für weitere Informationen, wie man sich in die digitalen Räume einloggen kann, besuchen Sie bitte unseren zugehörigen StudOn Kurs.
- Keywords:
- human-computer interaction, Mensch-Maschine-Schnittstelle, grafische Benutzerschnittstellen, mobile Mensch-Computer-Interaktion, Mensch-Maschine-Interaktion im Fahrzeug, ubiquitäre und eingebettete interaktive Systeme
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Human Computer Interaction Exercises [HCI-E] -
- Lecturer:
- Wolfgang Mehringer
- Details:
- Übung, 1 cred.h, ECTS: 1,25
- Dates:
- Mon, 14:15 - 15:45, K1-119 Brose-Saal
- Fields of study:
- WPF INF-MA ab 1
WF INF-BA ab 5
WF CE-MA ab 1
WF ICT-MA ab 1
WF IuK-BA ab 5
WPF ICT-MA-MPS ab 1
WPF ICT-MA-ES ab 1
WPF ICT-MA-NDC ab 1
WF EEI-MA ab 1
WF EEI-BA ab 5
WPF MT-MA-BDV ab 1
WPF MT-BA ab 5
WPF ASC-MA ab 1
WPF AI-MA ab 1
- Contents:
- Aufgrund der derzeitigen Corona-Lage findet die Übung digital statt. Für weitere Informationen, wie man sich in die digitalen Räume einloggen kann, besuchen Sie bitte unseren zugehörigen StudOn Kurs.
- Keywords:
- human-computer interaction, Mensch-Maschine-Schnittstelle, grafische Benutzerschnittstellen, mobile Mensch-Computer-Interaktion, Mensch-Maschine-Interaktion im Fahrzeug, ubiquitäre und eingebettete interaktive Systeme
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Innovationslabor für Wearable und Ubiquitous Computing [InnoLab] -
- Lecturers:
- Björn Eskofier, Matthias Zürl, Philipp Dumbach, Marlies Nitschke, Malte Ollenschläger, Nils Roth, Johannes Link, Maike Stöve, Martin Ullrich, Michael Nissen, Mohamad Wehbi, Imrana Abdullahi Yari, Alzhraa Ahmed, Ann-Kristin Seifer, Philipp Schlieper
- Details:
- Praktikum, 4 cred.h, graded certificate, geeignet als Schlüsselqualifikation
- Dates:
- Tue, 16:15 - 17:45, 00.010
Thu, 12:15 - 13:45, 00.010
The first lectures will take place as online courses. More information including the link to the online courses can be found on StudOn.
- Fields of study:
- WF INF-MA ab 1
WF MT-MA ab 1
WF MB-MA ab 1
WF ME-MA ab 1
WF WING-MA ab 1
WF CE-MA ab 1
WF IIS-MA ab 1
WF Ph-MA ab 1
- Prerequisites / Organisational information:
- Themenvergabe und Terminfindung in der ersten Woche des Semesters.
Anmeldung bis 28.03.2021 unter Studon.
Die Vergabe der Plätze im Kurs erfolgt nach einem Aufnahmeantrag. In diesem sollen Studierende ihren Studiengang, ihr Fachsemester und den angestrebten Abschluss nennen (Bachelor/Master). Sind zu viele Anmeldungen eingegangen und der Kurs ist voll, gibt es eine Warteliste.
Das Praktikum steht Studierenden der genannten Studienrichtungen ab dem 5. Bachelorsemester (und aller Mastersemester) ebenfalls offen. Für weitere Studiengänge und ECTS-Verteilungen bitte unter Matthias.Zuerl@fau.de nachfragen.
Weitere Informationen zu der Lehrveranstaltung können auch unserer Webseite entnommen werden: https://www.mad.tf.fau.de/teaching/innolab/ ,
Anmeldung: https://www.studon.fau.de/crs3647677.html
Die Anmeldung ist möglich ab dem 01.03.2021 bis einschließlich 28.03.2021
- Contents:
- Mini-Computer, die unseren Lebensrhythmus dokumentieren, EKG-Sensoren, die jedes Detail aufzeichnen, Brillen, die uns in eine andere Realität versetzen – diesen Technologien begegnen wir mittlerweile ständig im Alltag. Im Innovationslabor für Wearable und Ubiquitous Computing werden solche Technologien von Studierenden entwickelt und gleichzeitig aufgezeigt, wie man mit diesen ein eigenes Startup gründen könnte. Gefördert wird das Labor vom Zentrum Digitalisierung Bayern (ZD.B).
Die innovativen Technologien werden dabei prototypisch in Gruppenarbeit (5-8 Studierende) unter Nutzung von agilen Entwicklungsmethoden (Scrum) geschaffen. Den Studierenden steht dabei der Zugang zum Innovationslabor offen, welches mit der nötigen Infrastruktur für die Entwicklung der Prototypen ausgestattet ist. Die Ideen für die Projekte stammen dabei entweder von kooperierenden Firmen oder von den Studierenden selbst.
Neben dem Prototyping erlernen die Teilnehmer in Tutorials die Grundlagen für innovatives Arbeiten wie Design Thinking und Patentrecherche. Zudem wird ihnen beigebracht, wie sie nach der Entwicklung ihre Ideen schützen und gegebenenfalls an den Markt bringen können.Lernziele und Kompetenzen:
Ideation, Design Thinking
Patentrecherche, Marktanalysetechniken
Agile Entwicklungsmethoden (Scrum)
Prototyping
Sicherung geistigen Eigentums
Einführung in Entrepreneurship, Startup Finanzierung
. Prüfungsleistung
Der Umfang der Leistung im Innovation Lab setzt sich aus vier Teilbereichen zusammen (in Klammern ist der Anteil an der Gesamtnote angegeben):
Teampräsentation - 30 min (30%)
Abschlussvortrag - 10 min (10 %)
Hardware/Software Development, Scrum Meetings, Practical work (40%)
Abschlussdokumentation/Report - ca. 3 - 6 Seiten pro Studierendem (20 %)
Nach dem erfolgreichen Absolvieren erhalten die Studierenden 10 ECTS.
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Kolloquium im Bereich Maschinelles Lernen und Datenanalytik [KoMAD] -
- Lecturer:
- Björn Eskofier
- Details:
- Kolloquium, 2 cred.h
- Dates:
- Wed, 12:30 - 14:00, 00.010
- Fields of study:
- WPF INF-MA ab 1
- Contents:
- The colloquium provides a platform for the exchange of the researchers
of the MaD-Lab. It focuses on scientific education via the
discussion of seminal papers and recent developments, improvement of
presentation skills and other aspects like for example grant writing.
The colloquium is open to all interested researchers and students.
The colloquium is organized in the following StudOn group:
http://www.studon.uni-erlangen.de/studon/goto.php?target=crs_387333
Please join the group if you participate in the colloquium.
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Leading by Learning [LBL] -
- Lecturer:
- Janina Beilner
- Details:
- Vorlesung, 2 cred.h, ECTS: 5
- Dates:
- Fri, 10:15 - 11:45, Live-Stream
Online via MS Teams
- Fields of study:
- WPF MT-MA ab 5
WPF MT-BA ab 1
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Legged Locomotion of Robots [LLR] -
- Lecturer:
- Anne Koelewijn
- Details:
- Seminar, 2 cred.h, graded certificate, ECTS: 2,5
- Dates:
- Tue, 10:15 - 11:45, 00.010
- Fields of study:
- WF EEI-BA ab 6
WF EEI-MA ab 1
WPF INF-MA ab 1
WF MT-MA-BDV ab 1
WF MT-MA-GPP ab 1
WF MT-MA-MEL ab 1
- Keywords:
- Robotics, Humanoids, Legged Locomotion, Walking Robots, Control, Dynamic Walking, Stability, Energetics
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Legged Locomotion of Robots Laborprojekt [LLR-L] -
- Lecturer:
- Anne Koelewijn
- Details:
- Praktikum, 2 cred.h, ECTS: 2,5
- Dates:
- to be determined
- Fields of study:
- WF EEI-BA ab 6
WF EEI-MA ab 1
WPF INF-MA ab 1
WF MT-MA-BDV ab 1
WF MT-MA-GPP ab 1
WF MT-MA-MEL ab 1
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Machine Learning for Engineers II: Advanced Methods [MLE2] -
- Lecturers:
- Björn Eskofier, u.a.
- Details:
- Vorlesung, ECTS: 2,5, See VHB for further details
- Dates:
- to be determined
- Fields of study:
- WF MB-BA 3-6
WF MB-MA 1-3
WF ME-BA 3-6
WF ME-MA 1-3
WF WING-BA 3-6
WF WING-MA 1-3
WF IP-BA 3-6
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Machine Learning for Engineers; Introduction to Methods and Tools [MLE1] -
- Lecturers:
- Björn Eskofier, Jörg Franke, Nico Hanenkamp
- Details:
- Vorlesung, ECTS: 5
- Dates:
- Please see VHB and StudOn for further details: https://kurse.vhb.org/VHBPORTAL/kursprogramm/kursprogramm.jsp?kDetail=true&COURSEID=13154,72,1399,1 https://www.studon.fau.de/studon/goto.php?target=crs_3092844
- Fields of study:
- WF WING-BA 3-6
WF ME-BA 3-6
WF MB-MA 1-3
WF MB-BA 3-6
WF IP-BA 3-6
WF ME-MA 1-3
WF WING-MA 1-3
WPF DS-BA 2
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Maschinelles Lernen und Datenanalytik für Industrie 4.0 [MADI40] -
- Lecturers:
- Björn Eskofier, An Nguyen, Franz Köferl, Philipp Schlieper, Christoph Scholl
- Details:
- Seminar, 2 cred.h, graded certificate, ECTS: 5, nur Fachstudium, Registration via mail to an.nguyen@fau.de
- Dates:
- Wed, 16:15 - 18:00, 00.010
Starts on 14.04.2021
- Fields of study:
- WPF MT-MA-BDV ab 1
WPF INF-MA ab 1
WPF CE-MA ab 1
WF EEI-MA ab 1
WF ASC-MA ab 1
WF ICT-MA ab 1
WPF AI-MA ab 1
- Prerequisites / Organisational information:
- Prerequisites
Registration via e-mail to an.nguyen@fau.de Registration period: 15.02-19.04.2021
This course will be held online until the coronavirus pandemic is contained to such an extent that the Bavarian state government can allow face-to-face teaching again. More information will be sent via mail to registered students. Requirements:
Prior knowledge of machine learning via courses like PA, IntroPR, PR, DL, MLTS, CVP or equivalent (ideally first project experiences) is expected!
Motivation to explore scientific findings (e.g. via literature research)
Motivation to code and analyze data
Examination:
50% of grade: Presentation + demo (20 minutes)
50% of grade 4 pages IEEE standard paper (excluding references) + code submission
Attending the presentations of other students
- Contents:
- Contents
Companies in all kinds of industries are producing and collecting rapidly more and more data from various sources. This is enabled by technologies such as the Internet of Things (IoT), Cyber-physical system (CPS) and cloud computing. Hence there is an increasing demand in industry and research for students and graduates with machine learning and data analytics skills in the Industry 4.0 context.
In this Seminar the Industry 4.0 term will include the medical device sector. Aim of this seminar is to give students insights about state-of-the-art machine learning and data analytics methods and applications in the Industry 4.0 and Healthcare context. Students will mainly work independently on specific topics including implementation and analytical components. Several potential topics will be provided but students are also encouraged to propose their own topics (please discuss with teaching staff beforehand). Topics covered will include but are not limited to:
Best practices for presentation and scientific work
Overview of current hot topics in the field of machine learning and data analytics for Industry 4.0 (e.g. deep learning for predictive maintenance and process mining for usage analysis)
Data acquisition (what kind of data can be acquired? Identification of publicly available data sets) and storage (how can data be stored efficiently?)
Machine learning and data analytics methodologies (Support vector machines, Hidden Markov models, Deep learning, Process mining, etc.) for industrial data (sensor data, event logs, ...)
Object detection in industry application
The seminar will include talks by corresponding lecturer and invited experts in the domain. Furthermore, students will present results from literature research and data analytics projects (provided or open source datasets). Learning Objectives and Competencies
Students will develop an understanding of the current hot field of machine learning and data analytics for Industry 4.0 / healthcare
Students will learn to research and present a topic within the context of machine learning and data analytics for Industry 4.0 / healthcare independently
Students will learn to identify opportunities, challenges and limitations of corresponding ML approaches for Industry 4.0 / healthcare
Students will develop the skill to identify and understand relevant literature and to present their finding in a structured manner
Students will learn to present implementation and validation results in form of a demonstration and/or report
- Recommended literature:
- Literature (Selection)
Lei, Yaguo, Naipeng Li, Liang Guo, Ningbo Li, Tao Yan, and Jing Lin. “Machinery Health Prognostics: A Systematic Review from Data Acquisition to RUL Prediction.” Mechanical Systems and Signal Processing 104 (May 2018): 799–834.https://doi.org/10.1016/j.ymssp.2017.11.016.
Rojas, Eric, Jorge Munoz-Gama, Marcos Sepúlveda, and Daniel Capurro. “Process Mining in Healthcare: A Literature Review.” Journal of Biomedical Informatics 61 (June 1, 2016): 224–36. https://doi.org/10.1016/j.jbi.2016.04.007.
Wil M. P. van der Aalst. „Process Mining: Data Science in Action” 2nd edition, Springer 2016. ISBN 978-3-662-49851-4
Wang, Lihui, and Xi Vincent Wang. Cloud-Based Cyber-Physical Systems in Manufacturing. Cham: Springer International Publishing, 2018. https://doi.org/10.1007/978-3-319-67693-7.
- Keywords:
- Machine Learning, Data Analytics, Process Mining, Predictive Maintenance, Industry 4.0, Healthcare
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Project Machine Learning and Data Analytics [ProjMAD] -
- Lecturers:
- Björn Eskofier, Dario Zanca, An Nguyen
- Details:
- Sonstige Lehrveranstaltung, graded certificate, ECTS: 10, Registration via email to dario.zanca@fau.de or an.nguyen@fau.de
- Dates:
- Thu, 16:15 - 18:00, 00.010
- Fields of study:
- WPF INF-MA 1
WPF MT-MA ab 1
WPF AI-MA ab 1
- Prerequisites / Organisational information:
- Master Studium Informatik
Kick-off seminar on first Thursday of each semester (SS2021 - 15.04.2021)
Depending on the topic knowledge from courses like PR, PA, DL, MLTS or CV including good Python programming skills are required.
Motivation to code and experiment
- Contents:
- There will be a kick-off meeting the first Thursday 16:15-18:00 of each semester where topics in the field of machine learning and data analytics will be presented. Most topics will be related to the diverse research fields of the Machine Learning and Data Analytics Lab. Students also have the possibility to discuss their own project ideas with the supervisors. The distribution of topics will be based on prerequisites and first come, first serve in terms of time of registration until all topics are distributed.
- Keywords:
- Master Projekt Project
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Reinforcement Learning [RL] -
- Lecturer:
- Christopher Mutschler
- Details:
- Vorlesung, 2 cred.h, graded certificate, ECTS: 2,5
- Dates:
- Thu, 8:30 - 10:00, Zoom-Meeting
- Fields of study:
- WF ASC-MA ab 1
WF INF-MA ab 1
WF CE-MA-TA-MT ab 1
WF MT-MA ab 1
WF ME-MA ab 1
WF CME-MA ab 1
WF ICT-MA ab 1
- Contents:
- Reinforcement Learning (RL) is an area of Machine Learning that has recently made large advances and has been publicly visible by reaching and surpassing human skill levels in games like Go and Starcraft. These successes show that RL has the potential to transform many areas of research and industry by automatizing the development of processes that once needed to be engineered explicitly.
In contrast to other machine learning paradigms, which require the presence of (labeled or unlabeled) data, RL considers an agent that takes actions in an environment and learns from resulting feedback. The agent tries to maximize a reward signal that it receives for desirable outcomes, while at the same time trying to explore the world in which it operates to find yet unknown, potentially more rewarding action sequences–a dilemma known as the exploration-exploitation tradeoff. Recent advances in machine learning based on deep learning have made RL methods particularly powerful since they allow for agents with particularly well performing models of the world. The lecture will start with introductory lectures to RL where we cover the foundations of RL (i.e., Markov decision processes and dynamic programming techniques) before we go to model-free prediction and control algorithms such as TD-learning, SARSA and Q-learning. We will also get the general idea behind value function approximation techniques such as Deep Q-Networks (DQN) and study advanced policy-gradient and actor-critic methods including TRPO and PPO. We will end with focus sessions on advanced topics such as model-based RL, offline RL, explainable RL, and exploration-exploitation.
- Recommended literature:
- While there is particular literature given in the slides of the videos the following list serves as a general basis to get into the topic but also to go deeper at particular points.
Richard S. Sutton and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA.
Bellman, R.E. 1957. Dynamic Programming. Princeton University Press, Princeton, NJ. Republished 2003: Dover, ISBN 0-486-42809-5.
Csaba Szepesvari and Ronald Brachman and Thomas Dietterich. 2010. Algorithms for Reinforcement Learning. Morgan and Claypool Publishers.
Warren B. Powell. 2011. Approximate Dynamic Programming. Wiley.
Maxim Lapan. 2020. Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition. Packt Publishing.
Dimitri P. Bertsekas. 2017. Dynamic Programming and Optimal Control. Athena Scientific.
Miguel Morales. 2020. grokking Deep Reinforcement Learning. Manning.
Laura Graesser and Keng Wah Loon. 2019. Foundations of Deep Reinforcement Learning: Theory and Practice in Python. Addison-Wesley Data & Analytics.
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Reinforcement Learning Übung [RL-UE] -
- Lecturer:
- Christopher Mutschler
- Details:
- Übung, 2 cred.h, ECTS: 2,5
- Dates:
- Mon, 10:15 - 11:45, Zoom-Meeting
- Fields of study:
- WF ASC-MA ab 1
WF CE-MA-TA-MT ab 1
WF MT-MA ab 1
WF INF-MA ab 1
WF ME-MA ab 1
WF CME-MA ab 1
WF ICT-MA ab 1
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Selected Topics in ASC - Biomedical Signal Analysis [STASC] -
- Lecturer:
- Raj Rangayyan
- Details:
- Vorlesung, 4 cred.h, certificate, ECTS: 5
- Dates:
- Fri, Wed, 16:15 - 18:00, Live-Stream
from 14.4.2021 to 18.6.2021
- Fields of study:
- PF ASC-MA 2
WF CME-MA 1-4
WF ICT-MA ab 1
- Prerequisites / Organisational information:
- StudOn-Link: https://www.studon.fau.de/crs3698067.html
There is a limited number of places for this course. Students need to register via StudOn until April 8, 2021. Places will be distributed on April 9, 2021.
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The why and how of human gait simulations [HGS] -
- Lecturer:
- Anne Koelewijn
- Details:
- Seminar, 2 cred.h, graded certificate, ECTS: 2,5, nur Fachstudium, Organisation and slides via StudOn.
- Dates:
- Tue, 10:15 - 11:45, 00.010
- Fields of study:
- WF MT-MA-BDV ab 1
WF MT-MA-GPP ab 1
WPF INF-MA ab 1
WF EEI-MA ab 1
WF MT-MA-MEL ab 1
- Prerequisites / Organisational information:
- Assignment: Mail to mailto:anne.koelewijn@fau.de
The grade consists of a presentation (100% of the final grade).
- Contents:
- Learning objectives:
Set up a trajectory optimization problem to solve for a gait simulation
Be familiar with different approaches to solving gait simulations
Be able to select an approach to solve a specific simulation problem
Know the state-of-the-art gait simulation methods used at FAU and universities in Germany and abroad
- Keywords:
- trajectory optimization, optimal control, human locomotion, biomechanics
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