Prof. Dr. Didier Stricker
Dr. Alain Pagani
Dr. Gerd Reis
M.A. Eric Thil
Nicole Stephanie Buhlinger
Keonna Cunningham
Dr. Oliver Wasenmüller
Dr. Gabriele Bleser
Dr. Bertram Taetz
Sk Aziz Ali
Rashed Al Koutayni
Yuriy Anisimov
Jilliam Maria Diaz Barros
Ramy Battrawy
Hammad Butt
Mahdi Chamseddine
Steve Dias da Cruz
Fangwen Shu
Torben Fetzer
Michael Fürst
Christiano Couto Gava
Tewodros Amberbir Habtegebrial
Hartmut Feld
Jigyasa Katrolia
Andreas Kölsch
Onorina Kovalenko
Stephan Krauß
Paul Lesur
Muhammad Jameel Nawaz Malik
Mina Ameli
Nareg Minaskan Karabid
Pramod Murthy
Mathias Musahl
Peter Neigel
Manthan Pancholi
Jason Raphael Rambach
María Alejandra Sánchez Marín
Kripasindhu Sarkar
Alexander Schäfer
René Schuster
Mohamed Selim
Dennis Stumpf
Yongzhi Su
Xiaoying Tan
André Luiz Brandão
Ahmet Firintepe
Vladislav Golyanik
Aditya Tewari
Unsupervised Motion Pattern Learning for Motion Segmentation
Unsupervised Motion Pattern Learning for Motion Segmentation
The 21st International Conference on Pattern Recognition International Conference on Pattern Recognition (ICPR-21), November 11-15, Tsukuba Science City, Japan
- Abstract:
- This paper proposes a novel method for automated generation of motion segmentation models for full body motion monitoring. The method generates, in an un-supervised manner, a motion template for a dynamic warping approach from a short training sequence, i.e., from very few data. Therefore it first automatically detects motif candidates, i.e. the recurring patterns in the training sequence. Then it uses the detected motifs to construct the model. This novel method is able to automatically find motifs in a multivariate time series and generate a model which is capable of segmenting the series in a real-time system. The technology is evaluated in the context of a personalized virtual rehabilitation trainer application during a clinical study. The novel motion capturing dataset is publicly available.