Simulation, capturing and analysis of human motion

Seminar/Project SoSe 16 Dr. Bertram Taetz, Markus Miezal, Dr. Gabriele Bleser

AG wearHEALTH

Team

Psychology Computer science Mathematics

12/8/2015 2

Organisational

• Seminar (theoretical): 4 CP 120 hours work (15 full-time days)

• Project (practical): 8 CP 240 hours work (30 full-time days)

• Final examination:

– Presentation (20 + 5 min.)

– .bib file with relevant references

– Documented source code / data (projects)

• Presentations (tentative):

– Seminar: June/July

– Project: August (semester break)

• On completion: certificate

Seminar Topics

(1) Magnetic disturbances – detection and avoidance

• Problem: magnetic disturbances

Goal: Read survey paper and present and compare current methods. Extension to bachelor/master thesis possible Possible publication Contact: Markus Miezal, miezal@cs.uni-kl.de

• „Dealing with Magnetic Disturbances in Human Motion Capture: A survey of techniques“, G. Ligorio & M. Sabatini, 2016

http://www.home-designing.com/2010/09/11-living-rooms-with-modern-flair

https://forurbanchix.wordpress.com/2013/01/01/panic-mode-im-going-to-be-poor-single-and-homeless/panic-face/

(2) Contact estimation

From: „From posture to motion“ (Young, A.D., 2010)

https://www.ibiblio.org/kuphaldt/electricCircuits/Semi/SEMI_9.html

• „A Survey of Indoor Inertial Positioning Systems for Pedestrians.“ (Harle, R.A., 2013) • „Kinematic Model-Based Pedestrian Dead Reckoning for Heading [...]“ (Lee, M.S., 2015)

Goal(s): • Read and present survey about zero-velocity update and recent

publications! (Seminar) • Implement approaches and compare (Project) • Related Masterthesis possible! Contact: Markus Miezal, miezal@cs.uni-kl.de

(3) Compensating Soft-Tissue Artefacts in IMU Based Motion Capture

Goal: Understand and present the given paper in detail Contact: Bertram Taetz, taetz@cs.uni-kl.de Projects available! (2,3)

D. Meng, T. Shoepe and G. Vejarano „Accuracy Improvement on the Measurments of Human-Joint Angles“ IEEE Journal of Biomedical and Health Informatics (2016)

(4) Physics Based Motion Tracking / Dynamics of Human Motion

• „Dynamical Simulation Priors for Human Motion Tracking“

(M. Vondrak et al. 2012/2013)

Goal: Present the method and discuss strength and limitations. Contact: Bertram Taetz, taetz@cs.uni-kl.de Related Masterthesis possible!

cs.brown.edu/~ls/Publications/cvpr08sigal.pdf

(5) Deep Activity Recognition

Goal: Present the method and discuss strength and limitations. Contact: Bertram Taetz, taetz@cs.uni-kl.de

M. A. Alsheikh et al. „Deep Activity Recognition Models with Triaxial Accelerometers“ (2015)

(6) DeepConvLSTM

F. Javier Ordónez and A. Roggen „Deep Convolutional and LSTM Recurrent Neuronal Networks for Multimodel Wearable Activity Recognition“, Sensors (2016)

Goal: Present the method and discuss strength and limitations. Contact: Bertram Taetz, taetz@cs.uni-kl.de Project available! (5)

(7) Transfer Learning for Activity Recognition

http://image.slidesharecdn.com/transferoflearning-150411101412-conversion-gate01/95/transfer-of-learning-2-638.jpg?cb=1428869517

Goal: Present and compare methods. Contact: Bertram Taetz, taetz@cs.uni-kl.de

D. Cook, K. D. Feuz and N. C. Krishnan „Transfer Learning for Activity Recognition: A Survey“, NIH (2015)

(8) Active Transfer Learning for Activity Recognition

Goal: Present the method and discuss strength and limitations. Contact: Bertram Taetz, taetz@cs.uni-kl.de Project available! (4)

T. Diethe, T. Twomey and P. Flach „Active transfer learning for activity recognition“ (2016)

(9) Unsupervised Model Generation for Motion Monitoring

M. Weber, G. Bleser, G. Hendeby, A. Reiss and D. Stricker „Unsupervised model generation for motion monitoring“, Systems, Man, and Cybernetics (SMC) 2011.

Goal: Present the method and discuss strength and limitations. Contact: Bertram Taetz, taetz@cs.uni-kl.de Project available! (6)

(10) Estimate CoM with Kinect and Wii Balance Board

• https://www.youtube.com/watch?v=AIVAhM8YJPo

• A. González, M.Hayashibe, P. Fraisse „Estimation of Center of Mass with Kinect and Wii Balance Board“, IROS 2012

Goal: Present the method and discuss strength and limitations. Contact: Bertram Taetz, Markus Miezal taetz@cs.uni-kl.de, miezal@cs.uni-kl.de Project available! (7)

Project Topics

(1) Online motion tracker comparision

• Non-linear recursive bayes filters

• Extended Kalman Filter (EKF)

• Iterated EKF

• Unscented KF

EKF

IEKF UKF

Goal: Implement both extensions and compare their tracking quality to the EKF. Contact: Markus Miezal, miezal@cs.uni-kl.de

• „Statistical sensor fusion“ (Gustafsson, F. 2010) • „The unscented Kalman Filter for nonlinear estimation.“ (Wan.E. & Van Der Merwe, R. 2000)

(2) Soft Tissue influence on inertial body tracking

Goal: Measurement of Soft-Tissue effect on inertial body motion tracking. Contact: Bertram Taetz, taetz@cs.uni-kl.de

https://www.xsens.com/products/mvn-biomech/

D. Meng, T. Shoepe and G. Vejarano „Accuracy Improvement on the Measurments of Human-Joint Angles“ IEEE Journal of Biomedical and Health Informatics (2016)

(3) Soft Tissue Study

D. Weenk et al. „A Feasibility Study In Measureing Soft Tissue Artifacts On The Upper Leg Using Inertial and Magnetic Sensors“ (2013)

Goal: Feasibility Study of Measuring Soft-Tissue under different conditions. Contact: Bertram Taetz, taetz@cs.uni-kl.de

(4) Detection of Breathing

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Goal: Application and validation of learning approach for breath detection. Contact: Bertram Taetz, taetz@cs.uni-kl.de

Code: https://github.com/IRC-SPHERE/ActiveTransfer

T. Diethe, T. Twomey and P. Flach „Active transfer learning for activity recognition“ (2016)

(5) Test and Compare Learning approach for Activity recognition

Goal: Compare deep learning approaches with SVM and Random Forest approaches for activity recognition! Contact: Bertram Taetz, taetz@cs.uni-kl.de

http://scikit-learn.org/stable/index.html

https://www.tensorflow.org/

J.R. Kwapisz et al. „Activity Recognition using Cell Phone Accelerometers” KDD-10

(6) Motant

Goal: Compare deep learning approaches with SVM and Random Forest approaches for activity recognition! Contact: Bertram Taetz, taetz@cs.uni-kl.de

M. Weber, G. Bleser, G. Hendeby, A. Reiss and D. Stricker „Unsupervised model generation for motion monitoring“, Systems, Man, and Cybernetics (SMC) 2011.

(7) Estimate CoM with Kinect compare to a Wii Balance Board

• https://www.youtube.com/watch?v=AIVAhM8YJPo

• A. González, M.Hayashibe, P. Fraisse „Estimation of Center of Mass with Kinect and Wii Balance Board“, IROS 2012

Goal: Use Data from a Kinect, streamed to our framework , and compute a CoM projected on the ground. Compare the output to the CoP from a Balance board.

Contact: Markus Miezal, miezal@cs.uni-kl.de , Bertram Taetz taetz@cs.uni-kl.de

TODO

• Write an email to bleser@cs.uni-kl.de with the following information:

– Name

– Course of studies

– Semester

– Seminar/project

– Your 3 favorite topics

– Grade needed on certificate?

• Deadline: 08.05.2016

• We will distribute the topics until 11.08.2016

• Topic-specific questions: taetz@cs.uni-kl.de, miezal@cs.uni-kl.de

Study participation

Please write an email to study@wearhealth.de