Musculoskeletal loading plays an important role in pre-clinical evaluations of hip implants, in particular, bone ingrowth and bone remodelling. Joint force estimation using musculoskeletal models evolved as a viable alternative to in vivo measurement owing to the development of computational resources. This study investigated the efficiencies of four eminent open-source musculoskeletal models in order to determine the model that predicts the most accurate values of hip joint reaction and muscle forces during daily activities. Seven daily living activities of slow walking, normal walking, fast walking, sitting down, standing up, stair down and stair up were simulated in OpenSim using inverse dynamics method. Model predictions of joint kinematics, kinetics and muscle activation patterns were compared with published results. The estimated values of hip joint reaction force were found to corroborate well with in vivo measurements for each activity. Although the estimated values of hip joint reaction force were within a satisfactory range, overestimation of hip joint reaction force (75% BW of measured value) was observed during the late stance phase of walking cycles for all the models. In case of stair up, stair down, standing up and sitting down activities, the error in estimated values of hip joint reaction force were within ~20% BW of the measured value. Based on the results of our study, the London Lower Extremity Model predicted the most accurate value of hip joint reaction force and therefore can be used for applied musculoskeletal loading conditions for numerical investigations on hip implants.
BitsakosCKernerJFisherIet al. The effect of muscle loading on the simulation of bone remodelling in the proximal femur. J Biomech2005; 38(1): 133–139.
2.
GeraldesDMModeneseLPhillipsAT.Consideration of multiple load cases is critical in modelling orthotropic bone adaptation in the femur. Biomech Model Mechan2016; 15(5): 1029–1042.
3.
MukherjeeKGuptaS.The effects of musculoskeletal loading regimes on numerical evaluations of acetabular component. Proc IMechE, Part H: J Engineering in Medicine2016; 230(10): 918–929.
4.
HustonRPasserelloC.On the dynamics of a human body model. J Biomech1971; 4(5): 369–378.
5.
SeiregAArvikarR.A mathematical model for evaluation of forces in lower extremities of the musculoskeletal system. J Biomech1973; 6(3): 313–322.
6.
SeiregAArvikarR.The prediction of muscular load sharing and joint forces in the lower extremities during walking. J Biomech1975; 8(2): 89–102.
7.
CrowninshieldRJohnstonRAndrewsJet al. A biomechanical investigation of the human hip. J Biomech1978; 11(1–2): 75–85.
8.
DostalWFAndrewsJG.A three-dimensional biomechanical model of hip musculature. J Biomech1981; 14(11): 803–812.
9.
BrandRACrowninshieldRDWittstockCet al. A model of lower extremity muscular anatomy. J Biomech Eng1982; 104(4): 304–310.
10.
WickiewiczTLRoyRRPowellPLet al. Muscle architecture of the human lower limb. Clin Orthop Relat Res1983; 179: 275–283.
11.
HorsmanMKKoopmanHVan der HelmFet al. Morphological muscle and joint parameters for musculoskeletal modelling of the lower extremity. Clin Biomech2007; 22(2): 239–247.
12.
WardSREngCMSmallwoodLHet al. Are current measurements of lower extremity muscle architecture accurate?Clin Orthop Relat Res2009; 467(4): 1074–1082.
13.
BergmannGGraichenFRohlmannA.Hip joint loading during walking and running, measured in two patients. J Biomech1993; 26(8): 969–990.
BergmannGDeuretzbacherGHellerMet al. Hip contact forces and gait patterns from routine activities. J Biomech2001; 34(7): 859–871.
16.
FreglyBJBesierTFLloydDGet al. Grand challenge competition to predict in vivo knee loads. J Orthop Res2012; 30(4): 503–513.
17.
HellerMBergmannGDeuretzbacherGet al. Musculo-skeletal loading conditions at the hip during walking and stair climbing. J Biomech2001; 34(7): 883–893.
18.
StansfieldBNicolAPaulJet al. Direct comparison of calculated hip joint contact forces with those measured using instrumented implants: an evaluation of a three-dimensional mathematical model of the lower limb. J Biomech2003; 36(7): 929–936.
19.
SpinkAKoopmanHKlein HorsmanMet al. Hip compression force estimation with a comprehensive musculoskeletal model. In: Proceedings of measuring behavior: 6th international conference on methods and techniques in behavioral research, Maastricht, 26–29 August 2008, pp.16–17. Wageningen: Noldus Information Technology.
20.
MartelliSTaddeiFCappelloAet al. Effect of sub-optimal neuromotor control on the hip joint load during level walking. J Biomech2011; 44(9): 1716–1721.
21.
DelpSLAndersonFCArnoldASet al. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE T Biomed Eng2007; 54(11): 1940–1950.
22.
VicecontiMTestiDTaddeiFet al. Biomechanics modelling of the musculoskeletal apparatus: status and key issues. Proc IEEE2006; 94(4): 725–739.
23.
PandyMG.Computer modelling and simulation of human movement. Annu Rev Biomed Eng2001; 3(1): 245–273.
24.
ErdemirAMcLeanSHerzogWet al. Model-based estimation of muscle forces exerted during movements. Clin Biomech2007; 22(2): 131–154.
25.
DelpSLLoanJPHoyMGet al. An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures. IEEE T Biomed Eng1990; 37(8): 757–767.
26.
AndersonFCPandyMG.Individual muscle contributions to support in normal walking. Gait Posture2003; 17(2): 159–169.
27.
AndersonFCPandyMG.Dynamic optimization of human walking. J Biomech Eng2001; 123(5): 381–390.
28.
PandyMGAndriacchiTP.Muscle and joint function in human locomotion. Annu Rev Biomed Eng2010; 12: 401–433.
29.
ArnoldEMWardSRLieberRLet al. A model of the lower limb for analysis of human movement. Ann Biomed Eng2010; 38(2): 269–279.
30.
ModeneseLPhillipsABullA.An open source lower limb model: hip joint validation. J Biomech2011; 44(12): 2185–2193.
31.
HorsmanK.The Twente lower extremity model: consistent dynamic simulation of the human locomotor apparatus. PhD Thesis, University of Twente, Enschede, 2007.
32.
ModeneseLPhillipsAT.Prediction of hip contact forces and muscle activations during walking at different speeds. Multibody Sys Dyn2012; 28(1): 157–168.
33.
ModeneseLGopalakrishnanAPhillipsA.Application of a falsification strategy to a musculoskeletal model of the lower limb and accuracy of the predicted hip contact force vector. J Biomech2013; 46(6): 1193–1200.
34.
ValenteGMartelliSTaddeiFet al. Muscle discretization affects the loading transferred to bones in lower-limb musculoskeletal models. Proc IMechE, Part H: J Engineering in Medicine2012; 226(2): 161–169.
35.
van der KrogtMMDelpSLSchwartzMH.How robust is human gait to muscle weakness?Gait Posture2012; 36(1): 113–119.
36.
RaikovaRTPrilutskyBI.Sensitivity of predicted muscle forces to parameters of the optimization-based human leg model revealed by analytical and numerical analyses. J Biomech2001; 34(10): 1243–1255.
37.
ArnoldASBlemkerSSDelpSL.Evaluation of a deformable musculoskeletal model for estimating muscle–tendon lengths during crouch gait. Ann Biomed Eng2001; 29(3): 263–274.
38.
LenaertsGDe GrooteFDemeulenaereBet al. Subject-specific hip geometry affects predicted hip joint contact forces during gait. J Biomech2008; 41(6): 1243–1252.
39.
GarnerBAPandyMG.The obstacle-set method for representing muscle paths in musculoskeletal models. Comput Methods Biome2000; 3(1): 1–30.
40.
ArnoldASSalinasSAsakawaDJet al. Accuracy of muscle moment arms estimated from MRI-based musculoskeletal models of the lower extremity. Comput Aided Surg2000; 5(2): 108–119.
41.
ShelburneKBPandyMG.A musculoskeletal model of the knee for evaluating ligament forces during isometric contractions. J Biomech1997; 30(2): 163–176.
42.
JorgensenMMarrasWGranataKet al. MRI-derived moment-arms of the female and male spine loading muscles. Clin Biomech2001; 16(3): 182–193.
43.
McNeillTWarwickDAnderssonGet al. Trunk strengths in attempted flexion, extension, and lateral bending in healthy subjects and patients with low-back disorders. Spine1979; 5(6): 529–538.
44.
AndersonFCPandyMG.A dynamic optimization solution for vertical jumping in three dimensions. Comput Methods Biome1999; 2(3): 201–231.
45.
LathropRLChaudhariAMSistonRA.Comparative assessment of bone pose estimation using point cluster technique and OpenSim. J Biomech Eng2011; 133(11): 114503.
46.
RasmussenJDamsgaardMVoigtM.Muscle recruitment by the min/max criterion – a comparative numerical study. J Biomech2001; 34(3): 409–415.
47.
ThelenDGAndersonFC.Using computed muscle control to generate forward dynamic simulations of human walking from experimental data. J Biomech2006; 39(6): 1107–1115.
48.
CappelliniGIvanenkoYPPoppeleREet al. Motor patterns in human walking and running. J Neurophysiol2006; 95(6): 3426–3437.
49.
LiuMQAndersonFCSchwartzMHet al. Muscle contributions to support and progression over a range of walking speeds. J Biomech2008; 41(15): 3243–3252.
50.
NavacchiaAMyersCARullkoetterPJet al. Prediction of in vivo knee joint loads using a global probabilistic analysis. J Biomech Eng2016; 138(3): 031002.
51.
HamnerSRDelpSL.Muscle contributions to fore-aft and vertical body mass center accelerations over a range of running speeds. J Biomech2013; 46(4): 780–787.
52.
HarrisMDMacWilliamsBAForemanKBet al. Higher medially-directed joint reaction forces are a characteristic of dysplastic hips: a comparative study using subject-specific musculoskeletal models. J Biomech2017; 54: 80–87.
53.
KadabaMPRamakrishnanHWoottenM.Measurement of lower extremity kinematics during level walking. J Orthop Res1990; 8(3): 383–392.
54.
ArnoldEMDelpSL.Fibre operating lengths of human lower limb muscles during walking. P Roy Soc Lond B Bio2011; 366(1570): 1530–1539.
55.
HamnerSRSethADelpSL.Muscle contributions to propulsion and support during running. J Biomech2010; 43(14): 2709–2716.
56.
ValenteG.Subject-specific musculoskeletal models of the lower limbs for the prediction of skeletal loads during motion. PhD Thesis, University of Bologna, Bologna, 2013.
57.
RenLJonesRKHowardD.Whole body inverse dynamics over a complete gait cycle based only on measured kinematics. J Biomech2008; 41(12): 2750–2759.
58.
PedersenDBrandRChengCet al. Direct comparison of muscle force predictions using linear and nonlinear programming. J Biomech Eng1987; 109(3): 192–199.
59.
WesselingMDerikxLCDe GrooteFet al. Muscle optimization techniques impact the magnitude of calculated hip joint contact forces. J Orthop Res2015; 33(3): 430–438.
60.
MellonSJGrammatopoulosGAndersenMSet al. Individual motion patterns during gait and sit-to-stand contribute to edge-loading risk in metal-on-metal hip resurfacing. Proc IMechE, Part H: J Engineering in Medicine2013; 227(7): 799–800.
61.
DeMersMSPalSDelpSL.Changes in tibiofemoral forces due to variations in muscle activity during walking. J Orthop Res2014; 32(6): 769–776.
62.
LambertoGMartelliSCappozzoAet al. To what extent is joint and muscle mechanics predicted by musculoskeletal models sensitive to soft tissue artefacts?J Biomech2017; 62: 68–76.
63.
AckermannMVan den BogertAJ.Optimality principles for model-based prediction of human gait. J Biomech2010; 43(6): 1055–1060.
64.
MillerRHUmbergerBRHamillJet al. Evaluation of the minimum energy hypothesis and other potential optimality criteria for human running. P Roy Soc Lond B Bio2012; 279(1733): 1498–1505.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
