“Creating impactful characters: correcting human impact accelerations using high rate IMUs in dynamic activities” by Kuo, Liang, Fan, Blouin and Pai
Conference:
Type(s):
Title:
- Creating impactful characters: correcting human impact accelerations using high rate IMUs in dynamic activities
Session/Category Title: Human Capture and Modeling
Presenter(s)/Author(s):
Abstract:
Human motion capture using video-based or sensor-based methods gives animators the capability to directly translate complex human motions to create lifelike character animations. Advances in motion capture algorithms have improved their accuracy for estimating human generalized motion coordinates (joint angles and body positions). However, the traditional motion capture pipeline is not well suited to measure short duration, high acceleration impacts, such as running and jumping footstrikes. While high acceleration impacts have minimal influence on generalized coordinates, they play a big role in exciting soft tissue dynamics.Here we present a method for correcting motion capture trajectories using a sparse set of inertial measurement units (IMUs) collecting at high sampling rates to produce more accurate impact accelerations without sacrificing accuracy of the generalized coordinates representing gross motions. We demonstrate the efficacy of our method by correcting human motion captured experimentally using commercial motion capture systems with high rate IMUs sampling at 400Hz during basketball jump shots and running. With our method, we automatically corrected 185 jumping impacts and 1266 running impacts from 5 subjects. Post correction, we found an average increase of 84.6% and 91.1% in pelvis vertical acceleration and ankle dorsiflexion velocity respectively for basketball jump shots, and an average increase of 110% and 237% in pelvis vertical acceleration and ankle plantarflexion velocity respectively for running. In both activities, pelvis vertical position and ankle angle had small corrections on average below 2.0cm and 0.20rad respectively. Finally, when driving a human rig with soft tissue dynamics using corrected motions, we found a 143.4% and 11.2% increase in soft tissue oscillation amplitudes in basketball jump shots and running respectively. Our methodology can be generalized to correct impact accelerations for other body segments, and provide new tools to create realistic soft tissue animations during dynamic activities for more lifelike characters and better motion reconstruction for biomechanical analyses.
References:
1. Milton Abramowitz and Irene Stegun. 1965. Handbook of mathematical functions: with formulas, graphs, and mathematical tables. Number 55. Dover Publishing, Washington, D.C. Google ScholarDigital Library
2. Nathaniel A. Bates, Kevin R. Ford, Gregory D. Myer, and Timothy E. Hewett. 2013. Impact differences in ground reaction force and center of mass between the first and second landing phases of a drop vertical jump and their implications for injury risk assessment. Journal of Biomechanics 46, 7 (2013), 1237–1241.Google ScholarCross Ref
3. Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh. 2017. Realtime multi-person 2d pose estimation using part affinity fields.. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7291–7299. arXiv:1611.08050Google ScholarCross Ref
4. Pietro Cerveri, Antonio Pedotti, and Giancarlo Ferrigno. 2003. Robust recovery of human motion from video using Kalman filters and virtual humans. Human movement science 22, 3 (2003), 377–404.Google Scholar
5. Pietro Cerveri, Antonio Pedotti, and Giancarlo Ferrigno. 2005. Kinematical models to reduce the effect of skin artifacts on marker-based human motion estimation. Journal of Biomechanics 38, 11 (2005), 2228–2236.Google ScholarCross Ref
6. Ross A. Clark, Yong-hao Pua, Karine Fortin, Callan Ritchie, Kate E. Webster, Linda Denehy, and Adam L. Bryant. 2012. Gait & Posture Validity of the Microsoft Kinect for assessment of postural control. Gait & Posture 36, 3 (2012), 372–377.Google ScholarCross Ref
7. William B. Edwards, David Taylor, Thomas J. Rudolphi, Jason C. Gillette, and Timothy R. Derrick. 2010. Clinical Biomechanics Effects of running speed on a probabilistic stress fracture model. Clinical Biomechanics 25, 4 (2010), 372–377.Google ScholarCross Ref
8. Kevin R. Ford, Gregory D. Myer, and Timothy E. Hewett. 2000. Valgus Knee Motion during Landing in High School Female and Male Basketball Players. Medicine & Science in Sports & Exercise 35, 10 (2000), 1745–1750.Google ScholarCross Ref
9. Giannis Giakas and Vasilios Baltzopoulos. 1997. A Comparison of Automatic Filtering Techniques Applied to Biomechanical Walking Data. Journal of Biomechanics 30, 8 (1997), 847–850.Google ScholarCross Ref
10. Michael Gleicher. 1999. Animation From Observation : Motion Capture and Motion Editing. ACM SIGGRAPH Computer Graphics 33, 4 (1999), 51–54. Google ScholarDigital Library
11. Samuel R. Hamner, Ajay Seth, and Scott L. Delp. 2010. Muscle contributions to propulsion and support during running. Journal of Biomchanics 43, 14 (2010), 2709–2716.Google ScholarCross Ref
12. Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu. 2014. Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence 36, 7 (2014), 1325–1339. Google ScholarDigital Library
13. Doug L. James and Dinesh K. Pai. 2002. DyRT: dynamic response textures for real time deformation simulation with graphics hardware. ACM Transactions on Graphics (TOG) 21, 3 (2002), 582–585. Google ScholarDigital Library
14. Gerard Pons-moll Javier, Romero Naureen, and Mahmood Michael. 2015. Dyna : A Model of Dynamic Human Shape in Motion. ACM Transactions on Graphics (TOG) 34, 4 (2015). Google ScholarDigital Library
15. Angjoo Kanazawa, Michael J. Black, David W. Jacobs, and Jitendra Malik. 2018. End-to-end Recovery of Human Shape and Pose. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7122–7131. arXiv:1712.06584Google ScholarCross Ref
16. Calvin Kuo, Jake Sganga, Michael Fanton, and David B. Camarillo. 2018a. Head impact kinematics estimation with network of inertial measurement units. Journal of Biomechanical Engineering In Revisions (2018).Google Scholar
17. Calvin Kuo, Lyndia C. Wu, Jesus Loza, Daniel Senif, Scott Anderson, and David B. Camarillo. 2018b. Comparison of video-based and sensor-based head impact exposure. PLoS ONE 13, 6 (2018), e0199238.Google ScholarCross Ref
18. Daniel L. Miranda, Paul D. Fadale, Michael J. Hulstyn, Robert M. Shalvoy, Jason T. Machan, and Braden C. Fleming. 2013a. Knee biomechanics during a jump-cut maneuver: effects of gender & ACL surgery. Medicine and science in sports and exercise 45, 5 (2013), 942–951.Google Scholar
19. Daniel L. Miranda, Michael J. Rainbow, Joseph J. Crisco, and Braden C. Fleming. 2013b. Kinematic differences between optical motion capture and biplanar videoradiography during a jump – cut maneuver. Journal of Biomechanics 46, 3 (2013), 567–573.Google ScholarCross Ref
20. Thomas B. Moeslund and Erik Granum. 2001. A Survey of Computer Vision-Based Human Motion Capture. Computer Vision and Image Understanding 81, 3 (2001), 231–268. Google ScholarDigital Library
21. Thomas B. Moeslund, Adrian Hilton, and Volker Kru. 2006. A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104, 2–3 (2006), 90–126. Google ScholarDigital Library
22. Dinesh K. Pai, Austin Rothwell, Pearson Wyder-Hodge, Alistair Wick, Ye Fan, Egor Larionov, Darcy Harrison, Debanga Raj Neog, and Cole Shing. 2018. The Human Touch : Measuring Contact with Real Human Soft Tissues. ACM Transactions on Graphics (TOG) 37, 4 (2018). Google ScholarDigital Library
23. Sang Il Park. 2006. Capturing and Animating Skin Deformation in Human Motion. ACM Transactions on Graphics (TOG) 25, 3 (2006), 881–889. Google ScholarDigital Library
24. Sang Il Park and Jessica K. Hodgins. 2008. Data-driven Modeling of Skin and Muscle Deformation. ACM Transactions on Graphics (TOG). 27, 3 (2008). Google ScholarDigital Library
25. Katherine Pullen and Christoph Bregler. 2002. Motion Capture Assisted Animation : Texturing and Synthesis. ACM Transactions on Graphics (TOG) 21, 3 (2002), 501–508. Google ScholarDigital Library
26. Raziel Riemer, Elizabeth T. Hsiao-wecksler, and Xudong Zhang. 2008. Uncertainties in inverse dynamics solutions : A comprehensive analysis and an application to gait. Gait & posture 27, 4 (2008), 578–588.Google Scholar
27. Daniel Roetenberg, Henk Luinge, and Per Slycke. 2009. Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors. Technical Report January 2009. 1–7 pages.Google Scholar
28. Thomas Seel, Jörg Raisch, and Thomas Schauer. 2014. IMU-Based Joint Angle Measurement for Gait Analysis. Sensors 14, 4 (2014), 6891–6909.Google ScholarCross Ref
29. Gunter P. Siegmund, Kevin M. Guskiewicz, Stephen W. Marshall, Alyssa L. DeMarco, and Stephanie J. Bonin. 2016. Laboratory Validation of Two Wearable Sensor Systems for Measuring Head Impact Severity in Football Players. Annals of Biomedical Engineering 44, 4 (2016), 1257–1274.Google ScholarCross Ref
30. Chantal Simons and Elizabeth J. Bradshaw. 2016. Do accelerometers mounted on the back provide a good estimate of impact loads in jumping and landing tasks? estimate of impact loads in jumping and landing tasks? Sports Biomechanics 15, 1 (2016), 76–88.Google ScholarCross Ref
31. Daniel Vlasic, Rolf Adelsberger, Giovanni Vannucci, John Barnwell, Markus Gross, Wojciech Matusik, and Jovan Popovic. 2007. Practical Motion Capture in Everyday Surroundings. ACM transactions on graphics (TOG) 26, 3 (2007). Google ScholarDigital Library
32. Shih-En Wei, Varun Ramakrishna, Takeo Kanada, and Yaser Sheikh. 2016. Pose Machines :. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. arXiv:arXiv:1602.00134v4Google Scholar
33. Christopher R. Winby, David G. Lloyd, Thor F. Besier, and T. Brett Kirk. 2009. Muscle and external load contribution to knee joint contact loads during normal gait. Journal of Biomechanics 42, 14 (2009), 2294–2300.Google ScholarCross Ref
34. Lyndia C. Wu, Calvin Kuo, Jesus Loza, Mehmet Kurt, Kaveh Laksari, Livia Z. Yanez, Daniel Senif, Scott C. Anderson, Logan E. Miller, Jillian E. Urban, Joel D. Stitzel, and David B. Camarillo. 2018. Detection of American Football Head Impacts Using Biomechanical Features and Support Vector Machine Classification. Scientific Reports 8, 1 (2018), 1–14.Google Scholar
35. Lyndia C. Wu, Kaveh Laksari, Calvin Kuo, Jason F. Luck, Svein Kleiven, Cameron R. Bass, and David B. Camarillo. 2016a. Bandwidth and sample rate requirements for wearable head impact sensors. Journal of Biomechanics 39, 0 (2016), 2918–2924.Google ScholarCross Ref
36. Lyndia C. Wu, Vaibhav Nangia, Kevin Bui, Bradley Hammoor, Mehmet Kurt, Fidel Hernandez, Calvin Kuo, and David B. Camarillo. 2016b. In Vivo Evaluation of Wearable Head Impact Sensors. Annals of Biomedical Engineering 44, 4 (2016), 1234–1245. arXiv:1503.03948Google ScholarCross Ref
37. Songning Zhang, Timothy R. Derrick, William Evans, Yeon-joo Yu, Songning Zhang, Timothy R. Derrick, William Evans, and Yeon-joo Yu. 2008. Shock and impact reduction in moderate and strenuous landing activities. Sports Biomechanics 7, 2 (2008), 296–309.Google ScholarCross Ref
