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Motion Tracking Using Inertial Sensors – From Fundamentals to Latest Advances and Challenges

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A pre-conference workshop at the European Control Conference 2018.


MEMS-based Inertial Measurement Units (IMUs) have recently become a game-changing technology in applications ranging from wearables in smart health systems and consumer electronics to aerospace, robotics and autonomous vehicles. This European Control Conference Workshop will present a broad and profound overview of the latest advances, open issues and challenges in the use of IMUs for realtime motion tracking, motion classification and feedback control.

The workshop program is a combination of presentation sessions with a methodological focus on the one hand and application-oriented discussions and hands-on sessions with real sensor data on the other hand. Thereby, active participation and discussion among attendees will be promoted. The workshop will be of interest to anyone in the systems and control community who has recently started to work with IMUs or has used them for several years.

Summary of Topics

The following topics will be addressed by the workshop sessions. Each topic will be presented and discussed in the light of latest research results and practical application examples.

  • Principles, Challenges and Potentials of Inertial Sensors
  • Systematic Overview of Methods for IMU-based Orientation and Position Tracking
  • Recent Advances in IMU-related Parameter Identification Methods
    • Maximum-Likelihood Approaches to Calibration Parameter Estimation
    • Realtime Joint Axis Identification by Nonlinear Least Squares
    • IMU-to-Segment Model Parameter Identification
  • IMU-based Classification and Regression with Uncertainties
    • Bayesian Regression and Classification (e.g. Gaussian Processes)
    • Bayesian Neural Networks
    • Applications to IMU data (e.g. gait phase classification, stride length regression)
  • Improved Position Tracking by Combining IMUs with Ultrawideband
  • Reducing Communication Load in Wireless Inertial Sensor Networks by Event-based Sampling
  • Potential and Benefits of Inertial Motion Tracking in Various Application Scenarios

Hands-on Session and Bring-Your-Own-Slides

In a dedicated hands-on session, several of the methods that are discussed in the above mentioned sessions will be applied to real data provided by the workshop organizers. This will, for example, include live data from the smartphones of participants as well as recorded IMU data from normal gait of healthy subjects and from the mechatronic prosthetic leg of a transfemoral amputee.

Participants are encouraged, but of course not required, to bring their own laptop with Matlab/Scilab and Python installations and investigate the proposed methods and provided data themselves. Useful code files will be provided by the organizers to assure that the focus is on discussion of the methods and results.

Moreover, each participant is cordially invited to bring their own two slides and pitch a recent research result or a challenging application problem such that fruitful discussions are stimulated among all organizers and attendees.

Brief Biography of the Workshop Organizers

Manon Kok

Manon is with the Machine Learning Group of the Computational and Biological Learning Lab at the University of Cambridge. She received her Ph.D. degree in Automatic Control from Linköping University, Sweden. Manon's research interests are in the fields of probabilistic inference for sensor fusion, signal processing and machine learning. One of the applications that she is specifically interested in is position and orientation estimation using inertial sensors and magnetometers.

Bertram Taetz

Bertram is with the wearHEALTH group at University of Kaiserslautern. He received his Ph.D. from the Faculty of Mathematics at Ruhr-University Bochum. Bertram's current research interest is in algorithms to combine physical and statistical models with sensor data, ranging from inertial sensors to RGB and RGB-D videos. He is involved in research and teaching of sensor fusion methods and statistical models with applications to human motion capturing and analysis at the University of Kaiserslautern.

Gabriele Bleser

Gabriele is with the wearHEALTH group at University of Kaiserslautern. The group focuses on the design, development and evaluation of mobile and wearable health systems. She also received her Ph.D. from the Department of Computer Science at the University of Kaiserslautern. Gabriele's current research interests are sensor fusion methods in inertial measurement units for biomechanical analyzes as well as the connection of capturing and feedback in mobile health systems.

Thomas Seel

Thomas is with the Control Systems Group at Technische Universität Berlin. He received his Ph.D. from the Department of Electrical Engineering and Computer Science at TU Berlin. Thomas' main research interests are learning control and sensor fusion methods in inertial sensor networks for biomedical engineering applications ranging from noninvasive blood pressure measurement to feedback-controlled neuroprostheses.

Recent Literature

Bleser, Gabriele, Taetz, Bertram, Miezal, Markus, Christmann, Corinna Anna, Steffen, Daniel, Regenspurger, Katja. Development of an Inertial Motion Capture System for Clinical Application - Potentials and challenges from the technology and application perspectives. Journal of Interactive Media, 16 (2) 2017.

Kok, Manon, Schön, Thomas B.. Magnetometer Calibration Using Inertial Sensors. IEEE Sensors Journal, 16 (14):5679-5689, July 2016.

Kok, Manon, Hol, Jeroen D., Schön, Thomas B.. Indoor positioning using ultrawideband and inertial measurements. IEEE Transactions on Vehicular Technology, 64 (4):1293–1303, 2015.

Kok, Manon, Hol, Jeroen D., Schön, Thomas B.. Using Inertial Sensors for Position and Orientation Estimation. Foundations and Trends on Signal Processing, 11 (1–2):1–153, 2017.

Kok, Manon, Hol, Jeroen D., Schön, Thomas B.. An optimization-based approach to human body motion capture using inertial sensors. In Proceedings of the 19th World Congress of the International Federation of Automatic Control, pages 79–85, Cape Town, South Africa, August 2014.

Miezal, Markus, Taetz, Bertram, Bleser, Gabriele. Real-time inertial lower body kinematics and ground contact estimation at anatomical foot points for agile human locomotion. In International Conference on Robotics and Automation, 2017.

Miezal, Markus, Taetz, Bertram, Bleser, Gabriele. On inertial body tracking in the presence of model calibration errors. MDPI Sensors, 16 (7):1132, 2016.

Taetz, Bertram, Bleser, Gabriele, Miezal, Markus. Towards self-calibrating inertial body motion capture. In International Conference on Information Fusion, pages 1751-1759, 2016.

P. Müller, M. A. Bégin, T. Schauer, T. Seel. Alignment-Free, Self-Calibrating Elbow Angles Measurement using Inertial Sensors. IEEE Journal of Biomedical and Health Informatics, 21 (2):312–319, 2017.
T. Seel, S. Ruppin. Eliminating the Effect of Magnetic Disturbances on the Inclination Estimates of Inertial Sensors. IFAC-PapersOnLine, 50 (1):8798–8803, 2017.
T. Seel, C. Werner, J. Raisch, T. Schauer. Iterative Learning Control of a Drop Foot Neuroprosthesis – Generating Physiological Foot Motion in Paretic Gait by Automatic Feedback Control. Control Engineering Practice, 48 (1):87–97, 2016.
D. Laidig, S. Trimpe, T. Seel. Event-Based Sampling for Reducing Communication Load in Realtime Human Motion Analysis by Wireless Inertial Sensor Networks. Current Directions in Biomedical Engineering, 2 (1):711–714, 2016.
D. Graurock, T. Schauer, T. Seel. User-Adaptive Inertial Sensor Network for Feedback-Controlled Gait Support Systems. In Proc. of the 20th Annual International FES Society Conference, page 1–4, La Grande Motte, France, 2016.

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