Graphical Abstract Figure
Graphical Abstract Figure
Close modal

Abstract

Euler–Lagrange's formulation is known for its systematic and simplified approach to deriving dynamics of complex systems. In order to apply the existing formulation to human gait dynamics, the base reference frame must be assumed as an inertial reference frame. Conventionally, the ankle joints or the hip joints are regarded as base reference frames during the stance and swing phases of human walking. As these joints are non-inertial in nature during actual locomotion, this assumption could result in inaccurate calculation of lower-limb joint torques and forces. Therefore, in this paper, an existing Euler–Lagrange-based formulation originally developed for fixed-base robotic manipulators is considered and modified to accommodate the movement of the base reference frame with respect to an inertial frame of reference defined outside the human body. The applicability of the modified formulation is studied, implemented, and validated using three standard and publicly available gait datasets covering the phases of walking and running. The joint torques obtained using the proposed dynamic model are compared with reference torques by calculating the mean absolute error values and visually through Bland–Altman plots. The obtained joint torque values and plots indicate a close agreement with published torques, thereby validating the accuracy of the proposed dynamic model. The robust formulation implementation makes it a valuable resource for researchers in this field, offering a reliable framework for gait analysis and the design of lower-limb prosthetics or exoskeletons.

References

1.
Marshall
,
E. A.
,
1983
, “
A Dynamical Model for the Stride in Human Walking
,”
Math. Modell.
,
4
(
5
), pp.
391
415
.
2.
Tutsoy
,
O.
,
Erol Barkana
,
D.
, and
Colak
,
S.
,
2017
, “
Learning to Balance an NAO Robot Using Reinforcement Learning With Symbolic Inverse Kinematic
,”
Trans. Inst. Meas. Control
,
39
(
11
), pp.
1735
1748
.
3.
Zhang
,
L.
,
Soselia
,
D.
,
Wang
,
R.
, and
Gutierrez-Farewik
,
E. M.
,
2022
, “
Lower-Limb Joint Torque Prediction Using LSTM Neural Networks and Transfer Learning
,”
IEEE Trans. Neural Syst. Rehabil. Eng.
,
30
, pp.
600
609
.
4.
Omer
,
A.
,
Hashimoto
,
K.
,
Lim
,
H. O.
, and
Takanishi
,
A.
,
2014
, “
Study of Bipedal Robot Walking Motion in Low Gravity: Investigation and Analysis
,”
Int. J. Adv. Rob. Syst.
,
11
(
9
), p.
139
.
5.
Hong
,
H.
,
Kim
,
S.
,
Kim
,
C.
,
Lee
,
S.
, and
Park
,
S.
,
2013
, “
Spring-Like Gait Mechanics Observed During Walking in Both Young and Older Adults
,”
J. Biomech.
,
46
(
1
), pp.
77
82
.
6.
Gora
,
S.
,
Gupta
,
S. S.
, and
Dutta
,
A.
,
2023
, “
Energy-Based Footstep Planning of Biped on Uneven Deformable Terrain Using Nonlinear Inverted Pendulum
,”
ASME J. Mech. Rob.
,
15
(
5
), p.
054502
.
7.
Ren
,
L.
,
Jones
,
R. K.
, and
Howard
,
D.
,
2008
, “
Whole Body Inverse Dynamics Over a Complete Gait Cycle Based Only on Measured Kinematics
,”
J. Biomech.
,
41
(
12
), pp.
2750
2759
.
8.
Alkjaer
,
T.
,
Simonsen
,
E. B.
, and
Dyhre-Poulsen
,
P.
,
2001
, “
Comparison of Inverse Dynamics Calculated by Two- and Three-Dimensional Models During Walking
,”
Gait Posture
,
13
(
2
), pp.
73
77
.
9.
Maillardet
,
F. J.
,
1977
, “
The Swing Phase of Locomotion
,”
Eng. Med.
,
6
(
3
), pp.
101
106
.
10.
Vimieiro
,
C.
,
Andrada
,
E.
,
Witte
,
H.
, and
Pinotti
,
M.
,
2015
, “
A Computational Model for Dynamic Analysis of the Human Gait
,”
Comput. Meth. Biomech. Biomed. Eng.
,
18
(
7
), pp.
799
804
.
11.
Fu
,
K. S.
,
Gonzalez
,
R. C.
,
Lee
,
C. G.
, and
Freeman
,
H.
,
1987
,
Robotics: Control, Sensing, Vision, and Intelligence
, Vol.
1
,
McGraw-Hill
,
New York
.
12.
Winter
,
D. A.
,
2009
,
Biomechanics and Motor Control of Human Movement
,
John Wiley & Sons
,
Hoboken, NJ
.
13.
Al-Shuka
,
H. F.
,
Corves
,
B. J.
, and
Zhu
,
W. H.
,
2014
, “
Dynamic Modeling of Biped Robot Using Lagrangian and Recursive Newton-Euler Formulations
,”
Int. J. Comput. Appl.
,
101
(
3
), pp.
1
8
.
14.
Xiang
,
Y.
,
Arora
,
J. S.
,
Rahmatalla
,
S.
, and
Abdel-Malek
,
K.
,
2009
, “
Optimization-Based Dynamic Human Walking Prediction: One Step Formulation
,”
Int. J. Numer. Methods Eng.
,
79
(
6
), pp.
667
695
.
15.
Tu
,
Y.
,
Zhu
,
A.
,
Song
,
J.
,
Shen
,
H.
,
Shen
,
Z.
,
Zhang
,
X.
, and
Cao
,
G.
,
2020
, “
An Adaptive Sliding Mode Variable Admittance Control Method for Lower Limb Rehabilitation Exoskeleton Robot
,”
Appl. Sci.
,
10
(
7
), p.
2536
.
16.
Velandia
,
C. C.
,
Tibaduiza
,
D. A.
, and
Vejar
,
M. A.
,
2017
, “
Proposal of Novel Model for a 2 DOF Exoskeleton for Lower-Limb Rehabilitation
,”
Robotics
,
6
(
3
), p.
20
.
17.
Tutsoy
,
O.
, and
Barkana
,
D. E.
,
2021
, “
Model Free Adaptive Control of the Under-Actuated Robot Manipulator With the Chaotic Dynamics
,”
ISA Trans.
,
118
, pp.
106
115
.
18.
Featherstone
,
R.
,
2014
,
“Rigid Body Dynamics Algorithms”
,
Springer
,
New York
.
19.
Sentis
,
L.
, and
Khatib
,
O.
,
2005
, “
Control of Free-Floating Humanoid Robots Through Task Prioritization
,”
Proceedings of the 2005 IEEE International Conference on Robotics and Automation
,
Barcelona, Spain
,
April
,
IEEE
, pp.
1718
1723
.
20.
McGrath
,
M.
,
Howard
,
D.
, and
Baker
,
R.
,
2017
, “
A Lagrange-Based Generalised Formulation for the Equations of Motion of Simple Walking Models
,”
J. Biomech.
,
55
, pp.
139
143
.
21.
Wronka
,
C. M.
, and
Dunnigan
,
M. W.
,
2011
, “
Derivation and Analysis of a Dynamic Model of a Robotic Manipulator on a Moving Base
,”
Rob. Auton. Syst.
,
59
(
10
), pp.
758
769
.
22.
Apkarian
,
J.
,
Naumann
,
S.
, and
Cairns
,
B.
,
1989
, “
A Three-Dimensional Kinematic and Dynamic Model of the Lower Limb
,”
J. Biomech.
,
22
(
2
), pp.
143
155
.
23.
Qi
,
Y.
,
Soh
,
C. B.
,
Gunawan
,
E.
,
Low
,
K. S.
, and
Maskooki
,
A.
,
2013
, “
A Novel Approach to Joint Flexion/Extension Angles Measurement Based on Wearable UWB Radios
,”
IEEE J. Biomed. Health Inform.
,
18
(
1
), pp.
300
308
.
24.
Drillis
,
R.
,
Contini
,
R.
, and
Bluestein
,
M.
,
1964
, “
Body Segment Parameters
,”
Artif. Limbs
,
8
(
1
), pp.
44
66
.
25.
Vaughan
,
C. L.
,
Davis
,
B. L.
, and
O’connor
,
J. C.
,
1992
,
Dynamics of Human Gait
,
Human Kinetics Publishers
,
South Africa
.
26.
Contini
,
R.
,
1972
, “
Body Segment Parameters, Part II
,”
Artif. Limbs
,
16
(
1
), pp.
1
19
.
27.
Hora
,
M.
,
Soumar
,
L.
,
Pontzer
,
H.
, and
Sládek
,
V.
,
2017
, “
Body Size and Lower Limb Posture During Walking in Humans
,”
PLoS One
,
12
(
2
), p.
e0172112
.
28.
Whittle
,
M. W.
,
2014
,
Gait Analysis: An Introduction
,
Butterworth-Heinemann Elsevier
,
Philadelphia, PA
.
29.
Wells
,
R. P.
,
1981
, “
The Projection of the Ground Reaction Force as a Predictor of Internal Joint Moments
,”
Bull. Prosthet. Res.
,
10
, pp.
15
19
.
30.
Van den Bogert
,
A. J.
, and
De Koning
,
J. J.
,
1996
, “
On Optimal Filtering for Inverse Dynamics Analysis
,”
Proceedings of the IXth Biennial Conference of the Canadian Society for Biomechanics
,
Vancouver, Canada
,
Aug. 21–24
, pp.
214
215
.
31.
Giavarina
,
D.
,
2015
, “
Understanding Bland Altman Analysis
,”
Biochem. Med.
,
25
(
2
), pp.
141
151
.
32.
Rao
,
G.
,
Amarantini
,
D.
,
Berton
,
E.
, and
Favier
,
D.
,
2006
, “
Influence of Body Segments' Parameters Estimation Models on Inverse Dynamics Solutions During Gait
,”
J. Biomech.
,
39
(
8
), pp.
1531
1536
.
You do not currently have access to this content.