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Articular Geometry Reconstruction for Knee Joint with a Wearable Compliant Device

Published online by Cambridge University Press:  17 June 2019

Jiajie Guo Open the ORCID record for Jiajie Guo [Opens in a new window] ,
Zihang Wang ,
Jianyong Fu  and
Kok-Meng Lee
Jiajie Guo*
Affiliation:
State Key Lab of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Zihang Wang
Affiliation:
State Key Lab of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Jianyong Fu
Affiliation:
State Key Lab of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Kok-Meng Lee
Affiliation:
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
*
*Corresponding author. E-mail: jiajie.guo@hust.edu.cn
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Summary

Nonlinear articular geometries of biological joints have contributed to highly agile and adaptable human-body motions. However, human–machine interaction could potentially distort natural human motions if the artificial mechanisms overload the articular surfaces and constrain biological joint kinematics. It is desired to better understand the deformable articular geometries of biological joints in vivo during movements for design and control of wearable robotics. An articular geometry reconstruction method is proposed to measure the effective articular profile with a wearable compliant device and illustrated with its application to knee-joint kinematic analysis. Regarding the joint articulation as boundary constraints for the compliant mechanism, the equivalent articular geometry is constructed from the beam deformations driven by knee motions, where the continuous deformations are estimated with strain data from the embedded sensors. Both simulated analysis and experimental validation are presented to justify the proposed method.

Keywords

ExoskeletonsHuman biomechanicsHuman-machine interactionMechatronic systemsNovel applications of roboticsSensor networks
Type
Articles
Information
Robotica , Volume 37 , Special Issue 12: Wearable Robotics: Dynamics, Control and Applications , December 2019 , pp. 2104 - 2118
Copyright
© Cambridge University Press 2019 

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References

Belvedere, C., Siegler, S., Ensini, A., Toy, J., Caravaggi, P., Namani, R., Princi, L. G., Durante, S. and Leardini, A., “Experimental evaluation of current and novel approximations of articular surfaces of the ankle joint,” J. Biomech. 75, 159–163 (2018).10.1016/j.jbiomech.2018.04.024CrossRefGoogle Scholar
Riley, J., Roth, J. D., Howell, S. M. and Hull, M. L., “Internal-external malalignment of the femoral component in kinematically aligned total knee arthroplasty increases tibial force imbalance but does not change laxities of the tibiofemoral joint,” Knee Surg. Sports Traumatol. Arthrosc. 26(6), 1618–1628 (2018).CrossRefGoogle ScholarPubMed
Bernstein, D. T., O’Neill, C. A., Kim, R. S., Jones, H. L., Noble, P. C., Harris, J. D. and McCulloch, P. C., “Osteochondral allograft donor-host matching by the femoral condyle radius of curvature,” Am. J. Sports Med. 45(2), 403–409 (2017).CrossRefGoogle Scholar
Lee, K. M. and Guo, J., “Kinematic and dynamic analysis of an anatomically based knee joint,” J. Biomech. 43(7), 1231–1236 (2010).CrossRefGoogle Scholar
Wang, D., Lee, K.-M., Guo, J. and Yang, C.-J., “Adaptive knee joint exoskeleton based on biological geometries,” IEEE-ASME Trans. Mechatron . 19(4), 1268–1278 (2014).CrossRefGoogle Scholar
Hamrick, M. W., “A chondral modeling theory revisited,” J. Theor. Biol. 201(3), 201–208 (1999).CrossRefGoogle Scholar
Sylvester, A. D., “Femoral condyle curvature is correlated with knee walking kinematics in ungulates,” Anat. Rec. 298(12), 2039–2050 (2015).CrossRefGoogle Scholar
Iwaki, H., Pinskerova, V. and Freeman, M. A. R., “Tibiofemoral movement 1: The shapes and relative movements of the femur and tibia in the unloaded cadaver knee,” J. Bone Joint Surg. 82B(8), 1189–1195 (2000).CrossRefGoogle Scholar
Esat, I. I. and Ozada, N., “Articular human joint modelling,” Robotica 28(2), 321–339 (2010).CrossRefGoogle Scholar
Harkey, M. S., Blackburn, J. T., Davis, H., Sierra-Arevalo, L., Nissman, D. and Pietrosimone, B., “The association between habitual walking speed and medial femoral cartilage deformation following 30 minutes of walking,” Gait Posture 59, 128–133 (2018).10.1016/j.gaitpost.2017.09.039CrossRefGoogle Scholar
Mosher, T. J., Smith, H. E., Collins, C., Liu, Y., Hancy, J., Dardzinski, B. J. and Smith, M. B., “Change in knee cartilage T2 at MR imaging after running: A feasibility study,” Radiology 234(1), 245–249 (2005).CrossRefGoogle Scholar
Donahue, T. L. H., Hull, M. L., Rashid, M. M. and Jacobs, C. R., “A finite element model of the human knee joint for the study of tibio-femoral contact,” J. Biomech. Eng.-T. ASME 124(3), 273–280 (2002).10.1115/1.1470171CrossRefGoogle Scholar
Lutz, M., Arora, R., Krappinger, D., Wambacher, M., Rieger, M. and Pechlaner, S., “Arthritis predicting factors in distal intraarticular radius fractures,” Arch. Orthop. Trauma Surg. 131(8), 1121–1126 (2011).10.1007/s00402-010-1211-3CrossRefGoogle Scholar
Nesch, I., Fogarty, D. P., Tzvetkov, T., Reinhart, B., Walus, A. C., Khelashvili, G., Muehleman, C. and Chapman, D., “The design and application of an in-laboratory diffraction-enhanced x-ray imaging instrument,” Rev. Sci. Instrum. 80(9), 093702 (2009)CrossRefGoogle Scholar
Chapman, T., Sholukha, V., Semal, P., Louryan, S. and Van Sint Jan, S., “Further consideration of the curvature of the neandertal femur,” Am. J. Phys. Anthropol. 165(1), 94–107 (2018).10.1002/ajpa.23334CrossRefGoogle Scholar
Gan, Y. D., Xu, D. C., Lu, S. and Ding, J., “Novel patient-specific navigational template for total knee arthroplasty,” Comput. Aided Surg. 16(6), 288–297 (2011).10.3109/10929088.2011.621214CrossRefGoogle Scholar
Kamimura, H. A. S., Wang, L., Carneiro, A. A. O., Kinnick, R. R., An, K.-N. and Fatemi, M., “Vibroacoustography for the assessment of total hip arthroplasty,” Clinics 68(4), 463–468 (2013).10.6061/clinics/2013(04)05CrossRefGoogle Scholar
Karaulova, I. A., Hall, P. M. and Marshall, A. D., “Tracking people in three dimensions using a hierarchical model of dynamics,” Image Vision Comput . 20(9-10), 691–700 (2002).CrossRefGoogle Scholar
Jung, M. C., Chung, J. Y., Son, K. H., Wang, H., Hwang, J., Kim, J. J., Kim, J. H. and Min, B. H., “Difference in knee rotation between total and unicompartmental knee arthroplasties during stair climbing,” Knee Surg. Sports Traumatol. Arthrosc. 22(8), 1879–1886 (2014).10.1007/s00167-014-3064-8CrossRefGoogle Scholar
Stief, F., Bohm, H., Dussa, C. U., Multerer, C., Schwirtz, A., Imhoff, A. B. and Doderlein, L., “Effect of lower limb malalignment in the frontal plane on transverse plane mechanics during gait in young individuals with varus knee alignment,” Knee 21(3), 688–693 (2014).10.1016/j.knee.2014.03.004CrossRefGoogle Scholar
Franci, R., Parenti-Castelli, V., Belvedere, C. and Leardini, A., “A new one-DOF fully parallel mechanism for modelling passive motion at the human tibiotalar joint,” J. Biomech. 42(10), 1403–1408 (2009).10.1016/j.jbiomech.2009.04.024CrossRefGoogle Scholar
Jeong, S. H., Zhang, S., Hjort, K., Hilborn, J. and Wu, Z. G., “PDMS-based elastomer tuned soft, stretchable, and sticky for epidermal electronics,” Adv. Mater. 28(28), 5830-+ (2016).CrossRefGoogle Scholar
Frutiger, A., Muth, J. T., Vogt, D. M., Menguec, Y., Campo, A., Valentine, A. D., Walsh, C. J. and Lewis, J. A., “Capacitive soft strain sensors via multicore-shell fiber printing,” Adv. Mater. 27(15), 2440–2446 (2015).10.1002/adma.201500072CrossRefGoogle Scholar
Menguc, Y., Park, Y.-L., Pei, H., Vogt, D., Aubin, P. M., Winchell, E., Fluke, L., Stirling, L., Wood, R. J. and Walsh, C. J., “Wearable soft sensing suit for human gait measurement,” Int. J. Robot. Res. 33(14), 1748–1764 (2014).CrossRefGoogle Scholar
Zheng, E., Manca, S., Yan, T., Parri, A., Vitiello, N. and Wang, Q., “Gait phase estimation based on noncontact capacitive sensing and adaptive oscillators,” IEEE Trans. Bio.-Med. Eng. 64(10), 2419–2430 (2017).CrossRefGoogle Scholar
Trkov, M., Yi, J., Liu, T. and Li, K., “Shoe-floor interactions in human walking with slips: Modeling and experiments,” J. Biomech. Eng.-T. ASME 140(3), 031005 (2018).CrossRefGoogle Scholar
Lang, S., Fundamentals of Differential Geometry (Springer-Verlag, New York, USA, 2001).Google Scholar
Standring, S., Gray’s Anatomy: The Anatomical Basis of Clinical Practice (Elsevier, Amsterdam, Netherlands, 2015).Google Scholar
Nayfeh, A. H. and Frank Pai, P., Linear and Nonlinear Structural Mechanics (JohnWiley & Sons, Hoboken, New Jersey, USA, 2008).Google Scholar