1. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 1 of 41 Musculoskeletal Modeling of Smilodon Fatalis for Virtual Functional Performance Testing Kiran Konakanchi Advisor: Dr. Venkat Krovi Mechanical & Aerospace Engineering State University of New York at Buffalo

2. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 2 of 41 Goal : Study the functional and behavioral performance of extinct & extant animals  Our Idea & Introduction  Goal & Issues  Literature Review & Available Tools  Musculoskeletal Modeling in AnyBody  Case Studies  Conclusion & Future Work Agenda

3. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 3 of 41 Musculoskeletal Biomechanical Model Engineering Analysis + + Possible Solution In our thousands of years of evolution, there are many unanswered questions Why ? Smilodon Tiger Why do present members of feline family not have saber teeth? Did the Smilodon use its saber teeth during hunting? Idea Introduction Issues Background Modeling Case studies Conclusion

4. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 4 of 41 Introduction Virtual Prototyping (VP) Allows the designer to realistically, accurately and quantitatively test multiple models within virtual environment “Virtual prototype is a computer simulation of a physical product that can be presented, analyzed, and tested from concerned product life-cycle aspects such as design /engineering, manufacturing, service, and recycling as if on a real physical model. The construction and testing of a virtual prototype is called Virtual Prototyping.” Introduction Issues Background Modeling Case studies Conclusion

5. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 5 of 41 Introduction Biomechanical modeling Modeling procedure based on the principles of biomechanics Black Box InputOutput Conceptual Model Analytical Model CAD Model Musculoskeletal Model Four types of models Complexity of Modeling Introduction Issues Background Modeling Case studies Conclusion

6. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 6 of 41 Project Goal Examine various aspects of systematic musculoskeletal model building with the help of detailed examples Explore various issues pertaining to the modeling and analysis of such systems and provide possible solutions Case Studies  Muscle force calculation  Bite force analysis  Determination of optimal muscle location points. Introduction Issues Background Modeling Case studies Conclusion

7. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 7 of 41 Issues Redundancy Addition Method : Adding Constraints Dynamically Determinate One Sided Constrained Method (DDOSC): Problem is divided into series of dynamically determinate problems Optimization Techniques Reduction Method: Grouping Muscles until N m = N d The number of actuators (muscles N m ) is greater than the number of degrees (N d ) of freedom of the system m F1 F2 mg Introduction Issues Background Modeling Case studies Conclusion

8. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 8 of 41 Issues Geometric Complexity  Modeling Phase  Analysis Phase Coarse Model Fine Model Individual Muscle Model Velocity or Force MUSCLE Neural In Group Muscle Model Single Muscle Multiple Muscles Introduction Issues Background Modeling Case studies Conclusion

9. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 9 of 41 Background Why Bite force? Bite force: The amount of force that can be exerted by the jaw adductor musculature and realized at the tooth row as a function of jaw geometry. Meers et al.(2003) established prey/predatory relationships among Triceratops horridus, Tyrannosaurus Rex and other dinosaurs. Verwaijan et al.(2002) found that head and body size have considerable impact on bite force magnitude. Introduction Issues Background Modeling Case studies Conclusion

10. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 10 of 41 Available Tools SIMM  Popularly used musculoskeletal modeling software  Require Bone, Joint, Muscle data in ‘C’ language format  Requires substantial programming knowledge SimMechanics  Good for Mechanical systems  Can define rigid bodies (bones), joints and drivers  Difficult to define mathematical muscle models LifeMod  Can interact with environment  Our data format does not suit the software requirements Introduction Issues Background Modeling Case studies Conclusion

11. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 11 of 41 Available Tools Visual Nastran  Good for mechanical systems simulation  Perform motion and stress analysis  Issue of muscle recruitment pattern  Can not solve the problem of redundancy Vertebrate Analyzer  Visualize and experiment with accurate biomechanically constrained models  Capability to attach muscles, ligaments, tendons etc.  Proposed future work could be an ideal package for functional performance testing Development of a Computational Toolkit for Biomechanical Analysis and Simulation : The Vertebrate Analyzer Introduction Issues Background Modeling Case studies Conclusion

12. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 12 of 41 Musculoskeletal Modeling in AnyBody What is AnyBody software? What will it do? AnyBody is a musculoskeletal modeling software used for developing detailed multi body biomechanical systems Applications  Therapy/Medical rehabilitation  Ergonomic Design in the fields of automotives, sports etc.  Functional performance studies  Training tools for surgeons when combined with virtual environment Introduction Issues Background Modeling Case studies Conclusion

13. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 13 of 41 Software Interface Introduction Issues Background Modeling Case studies Conclusion

14. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 14 of 41 Modeling Procedure 5 stage process Introduction Issues Background Modeling Case studies Conclusion

15. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 15 of 41 Different types of Studies / Analysis Analysis Kinematic analysis  Main emphasis is on system motion  No forces in the system are calculated.  Obtain position, velocity and acceleration information Muscle calibration analysis  Adjusts the lengths of tendons  Optimal length is in the middle of simulation  Not required for simple muscle models. Set up Initial Conditions Introduction Issues Background Modeling Case studies Conclusion

16. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 16 of 41 Inverse Dynamic Analysis (IDA) IDA can be thought as the heart of the software system. Body attempts to use its muscles in such a fashion that minimum fatigue condition is obtained. The main requirement for IDA is that, it should be able to cope with  Statically indeterminate problems  Limits on forces in the problem Analysis Equilibrium equations Optimality criteria involving muscle forces Unique solution + Introduction Issues Background Modeling Case studies Conclusion

17. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 17 of 41 Weber’s hypothesis: Muscle recruitment is performed in a way such that muscular effort is minimized during routine activities. The muscle recruitment is performed according to the following optimal criteria Where, e1: RecruitmentLpPenalty e2: RecruitmentQPenalty Analysis Minimize (Maximum muscle activity ) + e1*(sum of activities) + e2*(sum of squared activities) Subject to Equilibrium equations are fulfilled Muscles are not allowed to push. Introduction Issues Background Modeling Case studies Conclusion

18. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 18 of 41 Project Implementation Introduction Issues Background Modeling Case studies Conclusion

19. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 19 of 41 CAD Model from Point data MIMICS (Materialise’s Interactive Medical Image Control System) is used to develop a CAD model from CT/MRI data Introduction Issues Background Modeling Case studies Conclusion

20. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 20 of 41 Windowing : Adjusting the grey scale values Thresholding : Selection between soft and hard bone Region growing : Reduce noise and separate structures Editing : Remove artifacts CAD Model from Point data Skull Mandibl e Introduction Issues Background Modeling Case studies Conclusion

21. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 21 of 41 MATLAB Interface  To provide user friendly easy to use interface  Eliminates the necessity to learn the programming language  Use other MATLAB features and functions for easy analysis of results Introduction Issues Background Modeling Case studies Conclusion

22. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 22 of 41 Global Reference Frame Introduction Issues Background Modeling Case studies Conclusion

23. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 23 of 41 Segment(s) addition TigerSabertooth tiger Introduction Issues Background Modeling Case studies Conclusion

24. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 24 of 41 Joints Introduction Issues Background Modeling Case studies Conclusion

25. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 25 of 41 Drivers Introduction Issues Background Modeling Case studies Conclusion

26. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 26 of 41 Muscle Models Introduction Issues Background Modeling Case studies Conclusion

27. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 27 of 41 Muscle Route types Tiger Sabertooth tiger Introduction Issues Background Modeling Case studies Conclusion

28. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 28 of 41 Process flow of case studies Introduction Issues Background Modeling Case studies Conclusion

29. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 29 of 41 Revisiting our project flow chart Muscle forces obtained at this point Introduction Issues Background Modeling Case studies Conclusion

30. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 30 of 41 Muscle force calculation Min/Max criteria is used for muscle force calculation Using bound formulation we can easily solve the min/max problems By choosing artificial variable β and artificial function B(β) such that B(β) = β The min/max criteria can be reformulated as Introduction Issues Background Modeling Case studies Conclusion

31. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 31 of 41 Depending on 3 factors  Specimen (Tiger or Sabertooth tiger)  Muscle route algorithm (Via Point muscle or Shortest Path muscle)  Type of muscle (Simple or Hill muscle) CaseFeline memberRouting algorithmMuscle Type Case1TigerVPMuscleSimple Case2TigerVPMuscleComplex Case3TigerSPMuscleSimple Case4TigerSPMuscleComplex Case5Saber TigerVPMuscleSimple Case6Saber TigerVPMuslceComplex Case7Saber TigerSPMuslceSimple Case8Saber TigerSPMuscleComplex Case studies Introduction Issues Background Modeling Case studies Conclusion

32. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 32 of 41 Case 1: Tiger – VPMuscle - Simple Introduction Issues Background Modeling Case studies Conclusion

33. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 33 of 41 Case 8: Sabertooth tiger – SPMuscle - Complex Introduction Issues Background Modeling Case studies Conclusion

34. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 34 of 41 Bite force calculation Bite force is calculated from the following equilibrium equation. Where, is the line vector matrix that depends on the muscles line of action (calculated from plucker coordinates by knowing the positions of origin and insertion of muscle) is the column vector representing the muscle force (obtained above) is the external force or Bite force Introduction Issues Background Modeling Case studies Conclusion

35. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 35 of 41 Optimality Criteria  Vector representing design variables i.e. muscle origin and insertion coordinates Minimize: –Bite force ( ) Subject to: Where, represent the lower and upper limits respectively and Introduction Issues Background Modeling Case studies Conclusion

36. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 36 of 41 Parametric sweep studies Surface plot Line plot Tiger-SPMuscle-Simple Variation of Temporalis originVariation of Temporalis insertion Variation of Masseter originVariation of Masseter insertion Variation of Pterygoid origin Variation of pterygoid insertion Introduction Issues Background Modeling Case studies Conclusion

37. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 37 of 41 Bite Force graphs Case 1:Tiger – SPMuscle - Simple Introduction Issues Background Modeling Case studies Conclusion

38. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 38 of 41 Bite Force graphs Case 1:Sabertooth tiger – SPMuscle - Simple Introduction Issues Background Modeling Case studies Conclusion

39. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 39 of 41 Developed a user friendly computational framework for testing hypothesis and various if-then scenarios. Conclusion Identified the critical issues pertaining to musculoskeletal modeling like redundancy, geometric complexity, muscle recruitment pattern etc. Validated some of the available software packages with regards to available data. Conducted a range of virtual experiments on members of feline family (tiger & sabertooth tiger) with our proposed methodology that can help in the study of functional performance. Finally, we presented the biologist with a novel validated toolbox. Introduction Issues Background Modeling Case studies Conclusion

40. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 40 of 41 Future work Optimization routine Brute force method has been employed for parametric sweep. More sophisticated algorithm like will be used in future. Limitations of the software: MATLAB interface limitations will be resolved in future versions. Issues pertaining to modeling: We approximated the muscle origin and insertion as points. The future work will include the solution strategy to this issue like proving a curtain of muscles instead of single muscle etc. Task space redundancy: The bite force can be calculated at the tip pf one tooth. Task space redundancy need to be resolved to simultaneously calculate the bite force at the tips of two teeth. Screw theory (delSignore [36]) in order to solve the problem of task space redundancy.

41. Kiran Konakanchi August 19, 2005 Automation, Robotics and Mechatronics Lab, SUNY at Buffalo Slide 41 of 41 Thank You! Questions?