The parametric optimization techniques have been widely employed to predict human gait trajectories; however, their applications to reveal the other aspects of gait are questionable. The aim of this study is to investigate whether or not the gait prediction model is able to justify the movement trajectories for the higher average velocities. A planar, seven-segment model with sixteen muscle groups was used to represent human neuro-musculoskeletal dynamics. At first, the joint angles, ground reaction forces (GRFs) and muscle activations were predicted and validated for normal average velocity (1.55 m/s) in the single support phase (SSP) by minimizing energy expenditure, which is subject to the non-linear constraints of the gait. The unconstrained system dynamics of extended inverse dynamics (USDEID) approach was used to estimate muscle activations. Then by scaling time and applying the same procedure, the movement trajectories were predicted for higher average velocities (from 2.07 m/s to 4.07 m/s) and compared to the pattern of movement with fast walking speed. The comparison indicated a high level of compatibility between the experimental and predicted results, except for the vertical position of the center of gravity (COG). It was concluded that the gait prediction model can be effectively used to predict gait trajectories for higher average velocities.
WinterDA. Biomechanics and motor control of human gait: Normal, elderly and pathological. 1991.
2.
KimJHAbdel-MalekKXiangYYangJJAroraJS. Concurrent motion planning and reaction load distribution for redundant dynamic systems under external holonomic constraints. International Journal for Numerical Methods in Engineering2011; 88(1): 47-65.
3.
GollidayCHemamiH. An approach to analyzing biped locomotion dynamics and designing robot locomotion controls. IEEE Transactions on Automatic Control1977; 22(6): 963-72.
4.
KuoAD. The six determinants of gait and the inverted pendulum analogy: A dynamic walking perspective. Human Movement Science2007; 26(4): 617-56.
5.
AndersonFCPandyMG. Dynamic optimization of human walking. Journal of Biomechanical Engineering2001; 123(5): 381-90.
6.
LangenderferJLaScalzaSMellACarpenterJEKuhnJEHughesRE. An EMG-driven model of the upper extremity and estimation of long head biceps force. Computers in Biology and Medicine2005; 35(1): 25-39.
7.
AllouchSBoudaoudSYounèsRBen-MansourKMarinF. Proposition, identification, and experimental evaluation of an inverse dynamic neuromusculoskeletal model for the human finger. Computers in Biology and Medicine2015; 63: 64-73.
8.
MaYXieSZhangY. A patient-specific EMG-driven neuromuscular model for the potential use of human-inspired gait rehabilitation robots. Computers in Biology and Medicine2016; 70: 88-98.
9.
SaidouniTBessonnetG. Generating globally optimised sagittal gait cycles of a biped robot. Robotica2003; 21(02): 199-210.
10.
ShourijehMSMcPheeJ. Forward dynamic optimization of human gait simulations: A global parameterization approach. Journal of Computational and Nonlinear Dynamics2014; 9(3): 031018.
11.
MartinAESchmiedelerJP. Predicting human walking gaits with a simple planar model. Journal of Biomechanics2014; 47(6): 1416-21.
12.
ChevallereauCBessonnetGAbbaGAoustinY. Bipedal robots: Modeling, design and walking synthesis. John Wiley and Sons; 2013.
13.
van den AckermannMBogertAJ. Optimality principles for model-based prediction of human gait. Journal of Biomechanics2010; 43(6): 1055-60.
14.
RenLJonesRKHowardD. Predictive modelling of human walking over a complete gait cycle. Journal of Biomechanics2007; 40(7): 1567-74.
15.
XiangYAroraJSAbdel-MalekK. Optimization-based prediction of asymmetric human gait. Journal of Biomechanics2011; 44(4): 683-93.
16.
KirtleyCWhittleMWJeffersonR. Influence of walking speed on gait parameters. Journal of Biomedical Engineering1985; 7(4): 282-8.
17.
LythgoNWilsonCGaleaM. Basic gait and symmetry measures for primary school-aged children and young adults. II: Walking at slow, free and fast speed. Gait and Posture2011; 33(1): 29-35.
18.
ObergTKarszniaAObergK. Joint angle parameters in gait: Reference data for normal subjects, 10–79 years of age. Journal of rehabilitation Research and Development1994; 31(3): 199-213.
19.
ZhaoLZhangLWangLWangJ, editors. Three-dimensional motion of the pelvis during human walking. Mechatronics and Automation, 2005 IEEE International Conference 2005; IEEE.
20.
ZajacFEWintersJM. Modeling musculoskeletal movement systems: Joint and body segmental dynamics, musculoskeletal actuation, and neuromuscular control. Multiple Muscle Systems. Springer; 1990; 121-48.
21.
PandyMG. Computer modeling and simulation of human movement. Annual Review of Biomedical Engineering2001; 3(1): 245-73.
22.
MarshallRWoodGJenningsL. Performance objectives in human movement: A review and application to the stance phase of normal walking. Human Movement Science1989; 8(6): 571-94.
23.
AckermannM. Dynamics and energetics of walking with prostheses. University of Stuttgart; 2007.
24.
RahmatiMRostamiMBeigzadehB. A low-cost optimization framework to solve muscle redundancy problem. Nonlinear Dynamics2017; 1-15.
25.
YenVNagurkaM. Suboptimal trajectory planning of a five-link human locomotion model. Biomechanics of Normal and Prosthetic Gait1987; 4: 17-22.
26.
KoopmanBGrootenboerHJDe JonghHJ. An inverse dynamics model for the analysis, reconstruction and prediction of bipedal walking. Journal of Biomechanics1995; 28(11): 1369-76.
27.
SchiehlenW. Multibody system dynamics: Roots and perspectives. Multibody System Dynamics1997; 1(2): 149-88.
28.
VilimekM. Musculotendon forces derived by different muscle models. Acta of Bioengineering and Biomechanics2007; 9(2): 41.
29.
MenegaldoLLde Toledo FleuryAWeberHI. Moment arms and musculotendon lengths estimation for a three-dimensional lower-limb model. Journal of Biomechanics2004; 37(9): 1447-53.
30.
GerritsenKGvan den BogertAJHulligerMZernickeRF. Intrinsic muscle properties facilitate locomotor control – a computer simulation study. Motor Control1998; 2(3): 206-20.
31.
HillA. The heat of shortening and the dynamic constants of muscle. Proceedings of the Royal Society of London B: Biological Sciences1938; 126(843): 136-95.
32.
WintersJM. Hill-based muscle models: A systems engineering perspective. Multiple Muscle Systems. Springer; 1990; 69-93.
33.
HeintzSGutierrez-FarewikEM. Static optimization of muscle forces during gait in comparison to EMG-to-force processing approach. Gait and Posture2007; 26(2): 279-88.
34.
RaikovaRTAladjovHT. Hierarchical genetic algorithm versus static optimization – investigation of elbow flexion and extension movements. Journal of Biomechanics2002; 35(8): 1123-35.
35.
PeltaDCruzCVerdegayJL. Simple control rules in a cooperative system for dynamic optimisation problems. International Journal of General Systems2009; 38(7): 701-17.
36.
PeasgoodMKubicaEMcPheeJ. Stabilization of a dynamic walking gait simulation. Journal of Computational And Nonlinear Dynamics2007; 2(1): 65-72.
37.
BessonnetGSeguinPSardainP. A parametric optimization approach to walking pattern synthesis. The International Journal of Robotics Research2005; 24(7): 523-36.
38.
CrowninshieldRDBrandRA. A physiologically based criterion of muscle force prediction in locomotion. Journal of Biomechanics1981; 14(11): 793-801.
39.
NeptuneRRSasakiK. Ankle plantar flexor force production is an important determinant of the preferred walk-to-run transition speed. Journal of Experimental Biology2005; 208(5): 799-808.
40.
LulićTSušićAKodvanjJ. Biomechanical analysis of walking: Effects of gait velocity and arm swing amplitude. Periodicum Biologorum2010; 112(1): 13-7.
41.
van SoestAJBobbertMF. The contribution of muscle properties in the control of explosive movements. Biological Cybernetics1993; 69(3): 195-204.
42.
GordonAHuxleyAFJulianF. The variation in isometric tension with sarcomere length in vertebrate muscle fibres. The Journal of Physiology1966; 184(1): 170-92.
43.
ColeGKvan den BogertAJHerzogWGerritsenKG. Modelling of force production in skeletal muscle undergoing stretch. Journal of Biomechanics1996; 29(8): 1091-104.
44.
ThelenDG. Adjustment of muscle mechanics model parameters to simulate dynamic contractions in older adults. Transactions-American Society of Mechanical Engineers Journal of Biomechanical Engineering2003; 125(1): 70-7.
45.
ProskeUMorganD. Tendon stiffness: Methods of measurement and significance for the control of movement. A review. Journal of Biomechanics1987; 20(1): 75-82.
