Dr. Muhammad Zeshan Afzal

Prof. Dr. Didier Stricker

Dr. Alain Pagani

Keonna Cunningham

Dr. Oliver Wasenmüller

Dr. Gabriele Bleser

Dr. Bruno Mirbach

Dr. Jason Raphael Rambach

Dr. Bertram Taetz

Mhd Rashed Al Koutayni

Jilliam Maria Diaz Barros

Iuliia Brishtel

Mahdi Chamseddine

Steve Dias da Cruz

Ahmet Firintepe

Sophie Folawiyo

David Michael Fürst

Kamalveerkaur Garewal

Christiano Couto Gava

Leif Eric Goebel

Tewodros Amberbir Habtegebrial

Jigyasa Singh Katrolia

Andreas Kölsch

Onorina Kovalenko

Muhammad Jameel Nawaz Malik

Nareg Minaskan Karabid

Mohammad Minouei

Manthan Pancholi

Engr. Kumail Raza

Dr. Nadia Robertini

María Alejandra Sánchez Marín

Dr. Kripasindhu Sarkar

Alexander Schäfer

Pascal Schneider

Lukas Stefan Staecker

Dr. Vladislav Golyanik

Dr. Aditya Tewari

André Luiz Brandão

Magnetometer Robust Deep Human Pose Regression With Uncertainty Prediction Using Sparse Body Worn Magnetic Inertial Measurement Units

Magnetometer Robust Deep Human Pose Regression With Uncertainty Prediction Using Sparse Body Worn Magnetic Inertial Measurement Units
Hammad Tanveer Butt, Bertram Taetz, Mathias Musahl, Maria Alejandra Sanchez Marin, Pramod Murthy, Didier Stricker
IEEE Access (IEEE) 9 Seiten 36657-36673 IEEE 2/2021 .

Abstract:: We propose a deep learning based framework that learns data-driven temporal priors to perform 3D human pose estimation from six body worn Magnetic Inertial Measurement units sensors. Our work estimates 3D human pose with associated uncertainty from sparse body worn sensors. We derive and implement a 3D angle representation that eliminates yaw angle (or magnetometer dependence) and show that 3D human pose is still obtained from this reduced representation, but with enhanced uncertainty. We do not use kinematic acceleration as input and show that it improves the generalization to real sensor data from different subjects as well as accuracy. Our framework is based on Bi-directional recurrent autoencoder. A sliding window is used at inference time, instead of full sequence (offline mode). The major contribution of our research is that 3D human pose is predicted from sparse sensors with a well calibrated uncertainty which is correlated with ambiguity and actual errors. We have demonstrated our results on two real sensor datasets; DIP-IMU and Total capture and have come up with state-of-art accuracy. Our work confirms that the main limitation of sparse sensor based 3D human pose prediction is the lack of temporal priors. Therefore fine-tuning on a small synthetic training set of target domain, improves the accuracy.

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