Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Module INF-73-52-M-6

Methods for modeling and capturing human motion (M, 4.0 LP)

Module Identification

Module Number Module Name CP (Effort)
INF-73-52-M-6 Methods for modeling and capturing human motion 4.0 CP (120 h)

Basedata

CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. in WiSe
Level [6] Master (General)
Language [EN] English
Module Manager
Lecturers
Area of study [INF-KI] Intelligent Systems
Reference course of study [INF-88.79-SG] M.Sc. Computer Science
Livecycle-State [NORM] Active

Notice

alter Titel: Modellierung und Erfassung menschlicher Bewegung

Courses

Type/SWS Course Number Title Choice in
Module-Part
Presence-Time /
Self-Study
SL SL is
required for exa.
PL CP Sem.
2V+1U INF-73-52-K-6
Methods for modeling and capturing human motion
P 42 h 78 h
U-Schein
- PL1 4.0 WiSe
  • About [INF-73-52-K-6]: Title: "Methods for modeling and capturing human motion"; Presence-Time: 42 h; Self-Study: 78 h
  • About [INF-73-52-K-6]: The study achievement "[U-Schein] proof of successful participation in the exercise classes (ungraded)" must be obtained.

Examination achievement PL1

  • Form of examination: written exam (Klausur) (60-180 Min.)
  • Examination Frequency: each semester
  • Examination number: 67355 ("Methods for human motion and capturing")

Evaluation of grades

The grade of the module examination is also the module grade.


Contents

The focus of this course is on methods and models for IMU (inertial measurement unit) based human motion modelling and capturing, with the goal of biomechanically interpretable parameter (e.g. 3D kinematics) estimation. After motivating this general topic via different application examples and presenting an overview of different capturing modalities, the following topics are addressed:
  • Human body, kinematic models & marker-based optical motion capture
  • 3D rigid body kinematics
  • Inertial measurement units
  • Model-based sensor fusion methods
  • Data-based methods

The taught methods are illustrated with practical examples from current research. The content of the lectures is also presented in the form of lab sessions with implementations in Matlab and Python.

Competencies / intended learning achievements

Upon successful completion of the module, students will be able to
  • relate the different components for the appropriate modeling and detection of human motion,
  • combine knowledge from related disciplines (especially physics, mathematics and biomechanics)
  • apply suitable methods of model-based sensor data fusion and machine learning in a problem-oriented way,
  • apply mathematical principles, models and methods to practical problems in modeling and recording human motion,
  • evaluate the advantages and disadvantages as well as limitations of the presented methods
  • implement suitable algorithms for modeling and recording human motion

Literature

  • Zatsiorsky, Vladimir M. Kinematics/Kinetics of human motion. Human Kinetics, 1998/2002.
  • Gustafsson, Fredrik. Statistical sensor fusion. Studentlitteratur, 2010.
  • Thrun, Sebastian, Wolfram Burgard, and Dieter Fox. Probabilistic robotics. MIT press, 2005.

Requirements for attendance of the module (informal)

None

Requirements for attendance of the module (formal)

None

References to Module / Module Number [INF-73-52-M-6]

Course of Study Section Choice/Obligation
[INF-88.79-SG] M.Sc. Computer Science [Specialisation] Specialization 1 [WP] Compulsory Elective