A Deterministic Model of Human Motion Based on Algebraic Techniques and a Sensor Network to Simulate Shoulder Kinematics

Authors

  • Kimberly D. Kendricks College of Sciences, University of Nevada-Las Vegas, Las Vegas, NV 89154, USA
  • Anthony Taylor Department of Mathematics and Computer Science, Central State University, Wilberforce, OH 45384, USA
  • Anum Barki Department of Engineering Physics, Air Force Institute of Technology, Wright Patterson Air Force Base, Ohio 45433, USA
  • Ronald F. Tuttle Department of Engineering Physics, Air Force Institute of Technology, Wright Patterson Air Force Base, Ohio 45433, USA
  • Sean S. Kohles Division of Biomaterials & Biomechanics, Department of Restorative Dentistry, Oregon Health & Science University, Portland, Oregon 97201, USA

DOI:

https://doi.org/10.12970/2311-1755.2015.03.01.1

Keywords:

 Gait model, inverse kinematics, shoulder biomechanics, motion analysis.

Abstract

Limiting the quantitative characterization of ambulatory mobility to only the two-dimensional sagittal plane through the investigation of key kinematic parameters, may still inform scientists and bioengineers of critical elements of joint locomotion. This paper presents the initial validation of a deterministic biomechanical gait model that was derived from an inverse kinematic analysis of three-dimensional upper extremity movement. Algebraic methods were applied to generate shoulder flexion and extension angles during a single gait cycle during normal walking. The direct kinematic measurements from a motion capture system were analyzed and compared to the predicted measurements from the algebraic model for eight healthy, human subjects. The predicted results over all subjects varied from the actual joint angle measurements with a nominal amount of mean error (23%), while correlations were quite strong (mean R2 = 0.97). These findings indicate the potential value of deterministic modeling with algebraic techniques as an alternative to existing methods.

References

Cappozzo A, Catani F, Leardini A, Benedetti MG, Della Croce U. Position and orientation in space of bones during movement: experimental artefacts. Clin Biomech 1996; 11 (2): 90-100. http://dx.doi.org/10.1016/0268-0033(95)00046-1

Lucchetti L, Cappozzo A, Cappello A, Della Croce U. Skin movement artifact assessment and compensation in the estimation of knee-joint kinematics. J Biomech 1998; 31(11): 977-84. http://dx.doi.org/10.1016/S0021-9290(98)00083-9

Della Croce U, Leardini A, Chiari L, Cappozzo A. Human movement analysis using stereophotogrammetry. Part 4: assessment of anatomical landmark misplacement and its effects on joint kinematics. Gait Posture 2005; 21(2): 226-37. http://dx.doi.org/10.1016/j.gaitpost.2004.05.003

DeGroote F, DeLaet T, Jonkers I, DeSchutter J. Kalman smoothing improves the estimation of joint kinematics and kinetics in marker-based human gait analysis. J Biomech 2008; 41: 3390-8. http://dx.doi.org/10.1016/j.jbiomech.2008.09.035

Reinhold MM, Wilk K, Macrina L, et al. Changes in shoulder and elbow passive range of motion after pitching in professional baseball players. Am J Sports Med 2008; 36(3): 523-7. http://dx.doi.org/10.1177/0363546507308935

Boninger ML, Cooper RA, Shimada SD, Rudy TE. Shoulder and elbow motion during two speeds of wheelchair propulsion: a description using a local coordinate system. Spinal Cord 1998; 36: 418-26. http://dx.doi.org/10.1038/sj.sc.3100588

Kaliki R, Davoodi R, Loeb G. Prediction of elbow trajectory from shoulder angles using neural networks. Int J Comput Intell Appl 2008; 7(3): 333-349. http://dx.doi.org/10.1142/S1469026808002296

Kim W, Veloso AP, Araújo D, Kohles SS. Novel computational approaches characterizing knee physiotherapy. J Comput Design Eng 2014; 1(1): 55-66. http://dx.doi.org/10.7315/JCDE.2014.006

Duysen J, Tax AA, Trippel M, Dietz V. Phase-dependent reversal of reflexly induced movements during human gait. Exp Brain Res 1992; 90: 404-414.

Nieuwenhuijzen PHJA. Stiffness control of the leg in perturbed gait and posture. PhD Dissertation. Verdivas Communicatieproducties, Radboud University, Nijmegen. 2004.

Burleight AL, Horak FB, Molouin F. Modification of postural responses and step initiation: evidence for goal-directed postural interactions. J Neurophysiol 1994; 72(6): 2892-2902.

Carpenter MG, Allum JHJ, Honegger F. Influence of acute unilateral vestibular loss on postural responses in multiple directions. Gait Posture 1999; 9(4): 277-286.

Woollacott M, Shumway-Cook A. Attention and the control of posture and gait: a review of an emerging area of research. Gait Posture 2002; 16: 1-14. http://dx.doi.org/10.1016/S0966-6362(01)00156-4

Stagni R, Leardini A, Cappozzo A, Benedetti MG, Cappello A. Effects of hip joint centre mislocation on gait analysis results. J Biomech 2000; 33: 1479-1487. http://dx.doi.org/10.1016/S0021-9290(00)00093-2

Chiari L, Della Croche U, Leardini A, Cappozzo A. Human movement analysis using stereophotogrammetry. Part 2: instrumental errors. Gait Posture 2005; 21: 197-211. http://dx.doi.org/10.1016/j.gaitpost.2004.04.004

Leardini A, Chiari L, Della Croce U, Cappozzo A. Human movement using stereophotogrammetry. Part 3. Soft tissue artifact assessment and compensation. Gait Posture 2005; 21: 212-235. http://dx.doi.org/10.1016/j.gaitpost.2004.05.002

Rao G, Amarantini D, Berton E, Favier D. Influence of body segment’s parameters estimation models on inverse dynamics solutions during gait. J Biomech. 2005; 39: 1531-36. http://dx.doi.org/10.1016/j.jbiomech.2005.04.014

Ren L, Jones RK, Howard D. Whole body inverse dynamics over a complete gait cycle based only on measured kinematics. J Biomech 2008; 41: 2750-9. http://dx.doi.org/10.1016/j.jbiomech.2008.06.001

Ayoub MM. Simulation for sagittal plane lifting activities. Occup Ergonomics 2003; 3: 141-51.

Meredith M, Maddock S. Approximating character biomechanics with real-time weighted inverse kinematics. Comput Animation Virt Worlds 2007; 18: 349-59. http://dx.doi.org/10.1002/cav.191

Mihelj M. Inverse kinematics of human arm based on multisensory data integration. J Intell Robotic Syst 2006; 47: 139-53. http://dx.doi.org/10.1007/s10846-006-9079-8

Todorov E. Probabilistic inference of multijoint movements, skeletal parameters and marker attachments from diverse motion capture data. IEEE Trans Biomed Eng 2007; 54(11): 1927-39. http://dx.doi.org/10.1109/TBME.2007.903521

Wang X. A behavior-based inverse kinematics algorithm to predict arm prehension postures for computer-aided ergonomic evaluation. J Biomech 1999; 32: 453-60. http://dx.doi.org/10.1016/S0021-9290(99)00023-8

Kendricks DK, Fullenkamp MA, McGrellis R, Juhl J, Tuttle FR. An inverse kinematic mathematical model using Groebner basis theory for arm swing movement in gait. In: Proc 2010 Military Sensing Symposia on Battlespace Acoustic and Magnetic Sensors. Balitmore, MD. 2010.

Kendricks K. Solving the inverse kinematic robotics problem: A comparison study of the Denavit-Hartenberg matrix and Groebner basis theory. PhD Dissertation, Auburn University Libraries, Auburn, AL. 2007.

Chou LS, Song SM, Draganich LF. Predicting the kinematics and kinetics of gait based on the optimum trajectory of the swing limb. J Biomech 1995; 28(4): 377-85. http://dx.doi.org/10.1016/0021-9290(94)00083-G

Kodek T, Munih M. An analysis of static and dynamic joint torques in elbow flexion-extension movements. Simul Modeling Pract Theory 2003; 11(3/4): 297-312. http://dx.doi.org/10.1016/S1569-190X(03)00063-7

Wu G, Van der Helm FCT, Veeger HEJ, et al. ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion-Part II: shoulder, elbow, wrist and hand. J Biomech 2005; 38: 981-92. http://dx.doi.org/10.1016/j.jbiomech.2004.05.042

Adams W, Loustaunau P. An Introduction to Groebner Bases. Providence, RI: American Mathematical Society, 1994.

Stifter S. Algebraic methods for computing inverse kinematics. J Intell Robotic Syst. 1994; 11(1-2): 79-89. http://dx.doi.org/10.1007/BF01258295

Li Y, Wang W, Crompton RH, Gunther MM. Free vertical moments and transverse forces in human walking and their role in relation to arm-swing. J Exp Biol 2001(Pt 1); 204: 47-58.

Wagenaar RC, van Emmerik R. Resonant frequencies of arms and legs identify different walking patterns. J Biomech 2000; 33(7): 853-861. http://dx.doi.org/10.1016/S0021-9290(00)00020-8

Collins S, Adamczyk PG, Kuo A. Dynamic arm swinging in human walking. Proc Biol Sci 2009; 276(1673): 3679-88. http://dx.doi.org/10.1098/rspb.2009.0664

Sigal L, Black MJ. Measure locally, reason globally: occlusion-sensitive articulated pose estimation. Conf Comput Vis Pattern Recognit Workshops 2006; 2041-8.

Lu TW, O'Connor JJ. Bone position estimation from skin marker co-ordinates using global optimisation with joint constraints. J Biomech 1999; 32(2): 129-34. http://dx.doi.org/10.1016/S0021-9290(98)00158-4

Leardini A, O'Connor JJ, Catani F, Giannini S. A geometric model of the human ankle joint. J Biomech 1999; 32(6): 585-91. http://dx.doi.org/10.1016/S0021-9290(99)00022-6

Kingma I, de Looze M, van Dieën JH, Toussaint HM, Adams MA, Baten CT. When is lifting movement too asymmetric to identify low back loading by 2D analysis? Ergonomics 1998; 41: 1453-61. http://dx.doi.org/10.1080/001401398186207

Kohles SS, Gregorczyk KN, Phillips TC, Brody LT, Orwin JF, Vanderby R Jr. Concentric and eccentric shoulder rehabilitation biomechanics. Proc Inst Mech Eng H. 2007; 221(3): 237-49. http://dx.doi.org/10.1243/09544119JEIM140

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Published

2015-08-03

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