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2023 | OriginalPaper | Chapter

Comparison of Methods for Predicting Muscle Activations and Knee Joint Contact Forces During Squatting Using OpenSim

Authors : Rohan Kothurkar, Ramesh Lekurwale, Mayuri Gad

Published in: Proceedings of International Conference on Intelligent Manufacturing and Automation

Publisher: Springer Nature Singapore

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Abstract

Musculoskeletal modeling and simulation software available give a non-invasive prediction of joint contact forces. However, the results are very sensitive to optimization techniques and model choice. In this study, muscle activations and knee joint contact forces (KJCF) were predicted for squatting using two different optimization techniques along with two musculoskeletal models. OpenSim 4.2 was used to simulate squatting using motion capture data and ground reaction forces. Electromyography (EMG) signals were filtered, rectified, normalized, and compared with predicted muscle activations. Good agreement was observed between predicted muscle activations using OpenSim and the EMG with few exceptions using both models. KJCF showed a significantly large difference using both models and optimization techniques. The study demonstrates the sensitivity of results to optimization techniques and model choice. The study also shows the incapability of musculoskeletal models to predict KJCF during squatting.

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Literature
1.
Fregly BJ, Besier TF, Lloyd DG, Delp SL, Banks SA, Pandy MG, D’Lima DD (2012) Grand challenge competition to predict in vivo knee loads. J Orthop Res 30:503–513CrossRef
2.
Nagura T, Dyrby CO, Alexander EJ, Andriacchi TP (2002) Mechanical loads at the knee joint during deep flexion. J Orthop Res 20:881–886
3.
Nagura T, Matsumoto H, Kiriyama Y, Chaudhari A, Andriacchi TP (2006) Tibiofemoral joint contact force in deep knee flexion and its consideration in knee osteoarthritis and joint replacement. J Appl Biomech 22:305–313
4.
Taylor WR, Schütz P, Bergmann G, List R, Postolka B, Hitz M, Dymke J, Dammb P, Duda G, Gerber H, Verena S, Nasab SHH, Trepczynski A, Kutzner I (2017) A comprehensive assessment of the musculoskeletal system: the CAMS-Knee data set. J Biomech 65:32–39CrossRef
5.
D’Lima DD, Patil S, Steklov N, Chien S (2007) In vivo knee moments and shear after total knee arthroplasty. J Biomech 40:S11–S17
6.
Kothurkar R, Lekurwale R (2022) Techniques to determine knee joint contact forces during squatting: a systematic review. Proc Inst Mech Eng H 236:775–784CrossRef
7.
Delp SL, Anderson FC, Arnold AS, Loan P, Habib A, John CT, Guendelman E, Thelen DG (2007) OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng 54:1940–1950CrossRef
8.
Roelker SA, Caruthers EJ, Hall RK, Pelz NC, Chaudhari AMW, Siston RA (2020) Effects of optimization technique on simulated muscle activations and forces effects of optimization technique on simulated muscle activations and forces. J Appl Biomech 36:259–278CrossRef
9.
Wagner DW, Stepanyan V, Shippen JM, Demers MS, Gibbons RS, Andrews BJ, Creasey GH, Beaupre GS (2013) Consistency among musculoskeletal models: caveat utilitor. Ann Biomed Eng 41:1787–1799CrossRef
10.
Knarr BA, Higginson JS (2015) Practical approach to subject-specific estimation of knee joint contact force. J Biomech 48:2897–2902CrossRef
11.
Navacchia A, Rullkoetter PJ, Schütz P, List RB, Fitzpatrick CK, Shelburne KB (2016) Subject-specific modeling of muscle force and knee contact in total knee arthroplasty. J Orthop Res 34:1576–1587CrossRef
12.
Schellenberg F, Taylor WR, Trepczynski A, List R, Kutzner I, Schütz P, Duda GN, Lorenzetti S (2018) Evaluation of the accuracy of musculoskeletal simulation during squats by means of instrumented knee prostheses. Med Eng Phys 5:1–5
13.
Imani Nejad Z, Khalili K, Hosseini Nasab SH, Schütz P, Damm P, Trepczynski A, Taylor WR, Smith CR (2020) The capacity of generic musculoskeletal simulations to predict knee joint loading using the CAMS-knee datasets. Ann Biomed Eng 48:430–1440
14.
Lin YC, Dorn TW, Schache AG, Pandy MG (2012) Comparison of different methods for estimating muscle forces in human movement. Proc Inst Mech Eng Part H J Eng Med 226:103–112
15.
Roelker SA, Caruthers EJ, Hall RK, Pelz NC, Chaudhari AMW, Siston RA (2020) Effects of optimization technique on simulated muscle activations and forces effects of optimization technique on simulated muscle activations and forces
16.
Wesseling M, Derikx LC, De Groote F, Bartels W, Meyer C, Verdonschot N, Jonkers I (2015) Muscle optimization techniques impact the magnitude of calculated hip joint contact forces. J Orthop Res 33:430–438CrossRef
17.
Rajagopal A, Dembia CL, DeMers MS, Delp DD, Hicks JL, Delp SL (2016) Full-body musculoskeletal model for muscle-driven simulation of human gait. IEEE Trans Biomed Eng 63:2068–2079CrossRef
18.
Catelli DS, Wesseling M, Jonkers I, Lamontagne M (2019) A musculoskeletal model customized for squatting task. Comput Methods Biomech Biomed Engin 22:21–24CrossRef
19.
Mokka—Motion Kinematic & Kinetic Analyzer (n.d.)
20.
Hicks JL, Uchida TK, Seth A, Rajagopal A, Delp SL (2015) Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement. J Biomech Eng 137:020905CrossRef
21.
Mantoan A, Pizzolato C, Sartori M, Sawacha Z, Cobelli C, Reggiani M (2015) MOtoNMS: a MATLAB toolbox to process motion data for neuromusculoskeletal modeling and simulation. Source Code Biol Med 10
22.
van Veen B, Montefiori E, Modenese L, Mazzà C, Viceconti M (2019) Muscle recruitment strategies can reduce joint loading during level walking. J Biomech 97:109368CrossRef
23.
Bedo BLS, Catelli DS, Lamontagne M, Santiago PRP (2020) A custom musculoskeletal model for estimation of medial and lateral tibiofemoral contact forces during tasks with high knee and hip flexions. Comput Methods Biomech Biomed Eng 23:658–663CrossRef
24.
Pizzolato C, Lloyd DG, Sartori M, Ceseracciu E, Besier TF, Fregly BJ, Reggiani M (2015) CEINMS: a toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks. J Biomech 48:3929–3936CrossRef
25.
Esrafilian A, Stenroth L, Mononen ME, Tanska P, Avela J, Korhonen RK (2020) EMG-assisted muscle force driven finite element model of the knee joint with fibril-reinforced poroelastic cartilages and menisci. Sci Rep 10:1–16CrossRef
26.
Taylor WR, Ehrig RM, Duda GN, Schell H, Seebeck P, Heller MO (2005) On the influence of soft tissue coverage in the determination of bone kinematics using skin markers. J Orthop Res 23:726–734CrossRef
Metadata
Title
Comparison of Methods for Predicting Muscle Activations and Knee Joint Contact Forces During Squatting Using OpenSim
Authors
Rohan Kothurkar
Ramesh Lekurwale
Mayuri Gad
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Proceedings of International Conference on Intelligent Manufacturing and Automation

Print ISBN: 978-981-19-7970-5

Electronic ISBN: 978-981-19-7971-2

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-7971-2_51

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