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Prof. Dr. Didier Stricker

Dr. Alain Pagani

Dr. Gerd Reis

Eric Thil

Keonna Cunningham

Dr. Oliver Wasenmüller

Dr. Gabriele Bleser
Dr. Bruno Mirbach

Dr. Jason Raphael Rambach

Dr. Bertram Taetz
Dr. Muhammad Zeshan Afzal

Sk Aziz Ali

Mhd Rashed Al Koutayni
Murad Almadani
Alaa Alshubbak
Yuriy Anisimov

Jilliam Maria Diaz Barros

Ramy Battrawy
Hammad Butt

Mahdi Chamseddine
Steve Dias da Cruz

Fangwen Shu

Torben Fetzer

Ahmet Firintepe
Sophie Folawiyo

David Michael Fürst
Kamalveerkaur Garewal

Christiano Couto Gava
Leif Eric Goebel

Tewodros Amberbir Habtegebrial
Simon Häring
Khurram Hashmi

Jigyasa Singh Katrolia

Andreas Kölsch
Onorina Kovalenko

Stephan Krauß
Paul Lesur

Muhammad Jameel Nawaz Malik
Michael Lorenz
Markus Miezal

Mina Ameli

Nareg Minaskan Karabid
Mohammad Minouei

Pramod Murthy

Mathias Musahl

Peter Neigel

Manthan Pancholi
Qinzhuan Qian

Engr. Kumail Raza
Dr. Nadia Robertini
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar

Alexander Schäfer
Pascal Schneider

René Schuster

Mohamed Selim
Lukas Stefan Staecker

Dennis Stumpf

Yongzhi Su

Xiaoying Tan
Yaxu Xie

Dr. Vladislav Golyanik

Dr. Aditya Tewari

André Luiz Brandão
Ambulatory inertial spinal tracking using constraints
Ambulatory inertial spinal tracking using constraints
Markus Miezal, Bertram Taetz, Norbert Schmitz, Gabriele Bleser
Proceedings of the 9th International Conference on Body Area Networks International Conference on Body Area Networks (Bodynets-09), October 29 - November 1, London, United Kingdom
- Abstract:
- Wearable inertial sensor networks represent a well-known and meanwhile cheap solution for in-field motion capturing. However, the majority of existing approaches and products rely on simple stick figure models to approximate the hu- man skeleton with only a few rigid segments and connecting joints. Especially the spine is often extremely simplified with one or at most two segments. This simplification results in significant kinematic estimation errors. This paper presents a novel inertial tracking approach, where a recursive filter with integrated constraints enables detailed and efficient es- timation of the spine kinematics in real time. The advan- tages of the proposed approach are confirmed in experiments using ground truth data from an optical reference system.