Max-Planck-Institut für Informatik
max planck institut
mpii logo Minerva of the Max Planck Society

MPI-INF D4 Publications :: Thesis :: Brock, Heike

MPI-INF D4 Publications
Show all entries of:this year (2019)last year (2018)two years ago (2017)Open in Notes
Action:login to update  Library locked

Thesis - Master's thesis | @MastersThesis | Masterarbeit

Author(s)*:Brock, Heike
BibTeX citekey*:Brock2010_MasterThesis

Title, School
Title*:Automated Classification of Trampoline Motions Based on Inertial Sensor Input
School:Universität des Saarlandes
Type of Thesis*:Master's thesis

Publishers Name:Universität des Saarlandes
Publishers Address:Saarbrücken

Note, Abstract, Copyright
LaTeX Abstract:The automatic segmentation and classification of an unknown motion data stream accord-

ing to given motion categories constitutes an important research problem with applica-
tions in computer animation, medicine and sports sciences. In this thesis, we consider
the scenario of trampoline motions, where an athlete performs a sequence of predefined
trampoline jumps. Here, each jump follows certain rules and belongs to a specific motion
category such as a pike jump or a somersault. Then, the classification problem consists
in automatically segmenting an unknown trampoline motion sequence into its individ-
ual jumps and to classify these jumps according to the given motion categories. Since
trampoline motions are very fast and spacious while requiring special lighting conditions,
it is problematic to capture trampoline motions with video and optical motion capture
systems. Inertial sensors that measure accelerations and orientations are more suitable
for capturing trampoline motions and therefore have been used for this thesis. However,
inertial sensor output is noisy and abstract requiring suitable feature representations that
display the characteristics of each motion category without being sensitive to noise and
performance variations. A sensor data stream can then be transformed into a feature
sequence for classification. For every motion category, a class representation (or in our
case, a class motion template) is learned from a class of example motions performed by
different actors. The main idea, as employed in this thesis, is to locally compare the fea-
ture sequence of the unknown trampoline motion with all given class motion templates
using a variant of dynamic time warping (DTW) in the comparison. Then, the unknown
motion stream is automatically segmented and locally classified by the class template that
best explains the corresponding segment. Extensive experiments have been conducted
on trampoline jumps from various athletes for evaluating various feature representations,
segmentation and classification.

Keywords:Classification, Motion Analysis, Inertial Sensor Application
Download Access Level:MPG

Referees, Status, Dates
1. Referee:Meinard Müller
2. Referee:Alfred Effenberg
Supervisor:Meinard Müller
Date Kolloquium:- - -

MPG Unit:Max-Planck-Institut für Informatik
MPG Subunit:Computer Graphics Group
MPG Subsubunit:Multimedia Information Retrieval and Music Processing
Appearance:MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat

BibTeX Entry:
AUTHOR = {Brock, Heike},
TITLE = {Automated Classification of Trampoline Motions Based on Inertial Sensor Input},
PUBLISHER = {Universität des Saarlandes},
SCHOOL = {Universit{\"a}t des Saarlandes},
YEAR = {2010},
TYPE = {Master's thesis}
PAGES = {118},
ADDRESS = {Saarbr{\"u}cken},
MONTH = {December},

Entry last modified by Anja Becker, 02/18/2011
Hide details for Edit History (please click the blue arrow to see the details)Edit History (please click the blue arrow to see the details)

01/29/2011 05:06:31 PM
Anja Becker
Heike Brock
Heike Brock
Heike Brock
Heike Brock
Edit Dates
18.02.2011 11:40:39
29.01.2011 17:12:55
29.01.2011 17:10:08
29.01.2011 17:09:20
29.01.2011 17:06:31