Abstract

Estimating muscle forces is crucial for understanding joint dynamics and improving rehabilitation strategies, particularly for patients with neurological disorders who suffer from impaired muscle function. Muscle forces are directly proportional to muscle activations, which can be obtained using electromyography (EMG). EMG-driven modeling estimates muscle forces and joint moments from muscle activations. While surface muscles' activations can be obtained using surface electrodes, deep muscles require invasive methods and are not readily available for real-time applications. This study aims to extend our previously developed method for a single unmeasured muscle to a comprehensive approach for the simultaneous prediction of multiple unmeasured muscle activations in the upper extremity using muscle synergy extrapolation and EMG-driven modeling. By employing non-negative matrix factorization to decompose known EMG data into synergy components, the activations of unmeasured muscles are reconstructed with high accuracy by minimizing differences between joint moments obtained by EMG-driven modeling and inverse dynamics. This methodology is validated through experimentally collected muscle activations, demonstrating over 90% correlation with EMG signals in various scenarios.

References

1.
Lanzoni
,
D.
,
Andrea
,
V.
,
Daniele
,
R.
, and
Rizzi
,
C.
,
2022
, “
Design of Customized Virtual Reality Serious Games for the Cognitive Rehabilitation of Retrograde Amnesia After Brain Stroke
,”
ASME J. Comput. Inf. Sci. Eng.
,
22
(
3
), p.
031009
.10.1115/1.4053149
2.
Ada
,
L.
,
Dorsch
,
S.
, and
Canning
,
C. G.
,
2006
, “
Strengthening Interventions Increase Strength and Improve Activity After Stroke: A Systematic Review
,”
Australian J. Physiother.
,
52
(
4
), pp.
241
248
.10.1016/S0004-9514(06)70003-4
3.
Buchanan
,
T. S.
,
Lloyd
,
D. G.
,
Manal
,
K.
, and
Besier
,
T. F.
,
2004
, “
Neuromusculoskeletal Modeling: Estimation of Muscle Forces and Joint Moments and Movements From Measurements of Neural Command
,”
J. Appl. Biomech.
,
20
(
4
), pp.
367
395
.10.1123/jab.20.4.367
4.
Thelen
,
D. G.
,
Anderson
,
F. C.
, and
Delp
,
S. L.
,
2003
, “
Generating Dynamic Simulations of Movement Using Computed Muscle Control
,”
J. Biomech.
,
36
(
3
), pp.
321
328
.10.1016/S0021-9290(02)00432-3
5.
Kian
,
A.
,
Pizzolato
,
C.
,
Halaki
,
M.
,
Ginn
,
K.
,
Lloyd
,
D.
,
Reed
,
D.
, and
Ackland
,
D.
,
2019
, “
Static Optimization Underestimates Antagonist Muscle Activity at the Glenohumeral Joint: A Musculoskeletal Modeling Study
,”
J. Biomech.
,
97
, p.
109348
.10.1016/j.jbiomech.2019.109348
6.
Heintz
,
S.
, and
Gutierrez-Farewik
,
E. M.
,
2007
, “
Static Optimization of Muscle Forces During Gait in Comparison to EMG-to-Force Processing Approach
,”
Gait Posture
,
26
(
2
), pp.
279
288
.10.1016/j.gaitpost.2006.09.074
7.
Meyer
,
A. J.
,
Patten
,
C.
, and
Fregly
,
B. J.
,
2017
, “
Lower Extremity EMG-Driven Modeling of Walking With Automated Adjustment of Musculoskeletal Geometry
,”
PLoS One
,
12
(
7
), p.
e0179698
.10.1371/journal.pone.0179698
8.
Tahmid
,
S.
,
Font-Llagunes
,
J. M.
, and
Yang
,
J.
,
2023
, “
Upper Extremity Joint Torque Estimation Through an Electromyography-Driven Model
,”
ASME J. Comput. Inf. Sci. Eng.
,
23
(
3
), p.
030901
.10.1115/1.4056255
9.
Lloyd
,
D. G.
, and
Besier
,
T. F.
,
2003
, “
An EMG-Driven Musculoskeletal Model to Estimate Muscle Forces and Knee Joint Moments In Vivo
,”
J. Biomech.
,
36
(
6
), pp.
765
776
.10.1016/S0021-9290(03)00010-1
10.
Sartori
,
M.
,
Farina
,
D.
, and
Lloyd
,
D. G.
,
2014
, “
Hybrid Neuromusculoskeletal Modeling to Best Track Joint Moments Using a Balance Between Muscle Excitations Derived From Electromyograms and Optimization
,”
J. Biomech.
,
47
(
15
), pp.
3613
3621
.10.1016/j.jbiomech.2014.10.009
11.
Pizzolato
,
C.
,
Lloyd
,
D. G.
,
Sartori
,
M.
,
Ceseracciu
,
E.
,
Besier
,
T. F.
,
Fregly
,
B. J.
, and
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
(
14
), pp.
3929
3936
.10.1016/j.jbiomech.2015.09.021
12.
Seth
,
A.
,
Hicks
,
J. L.
,
Uchida
,
T. K.
,
Habib
,
A.
,
Dembia
,
C. L.
,
Dunne
,
J. J.
,
Ong
,
C. F.
, et al.,
2018
, “
OpenSim: Simulating Musculoskeletal Dynamics and Neuromuscular Control to Study Human and Animal Movement
,”
PLoS Comput. Biol.
,
14
(
7
), p.
e1006223
.10.1371/journal.pcbi.1006223
13.
Saul
,
K. R.
,
Hu
,
X.
,
Goehler
,
C. M.
,
Vidt
,
M. E.
,
Daly
,
M.
,
Velisar
,
A.
, and
Murray
,
W. M.
,
2015
, “
Benchmarking of Dynamic Simulation Predictions in Two Software Platforms Using an Upper Limb Musculoskeletal Model
,”
Comput. Methods Biomech. Biomed. Eng.
,
18
(
13
), pp.
1445
1458
.10.1080/10255842.2014.916698
14.
Zonnino
,
A.
, and
Sergi
,
F.
,
2019
, “
Model-Based Estimation of Individual Muscle Force Based on Measurements of Muscle Activity in Forearm Muscles During Isometric Tasks
,”
IEEE Trans. Biomed. Eng.
,
67
(
1
), pp.
134
145
.10.1109/TBME.2019.2909171
15.
Sartori
,
M.
,
Gizzi
,
L.
,
Lloyd
,
D. G.
, and
Farina
,
D.
,
2013
, “
A Musculoskeletal Model of Human Locomotion Driven by a Low Dimensional Set of Impulsive Excitation Primitives
,”
Front. Comput. Neurosci.
,
7
, p.
79
.10.3389/fncom.2013.00079
16.
Ao
,
D.
,
Shourijeh
,
M. S.
,
Patten
,
C.
, and
Fregly
,
B. J.
,
2020
, “
Evaluation of Synergy Extrapolation for Predicting Unmeasured Muscle Excitations From Measured Muscle Synergies
,”
Front. Comput. Neurosci.
,
14
, p.
588943
.10.3389/fncom.2020.588943
17.
Ao
,
D.
,
Vega
,
M. M.
,
Shourijeh
,
M. S.
,
Patten
,
C.
, and
Fregly
,
B. J.
,
2022
, “
EMG-Driven Musculoskeletal Model Calibration With Estimation of Unmeasured Muscle Excitations Via Synergy Extrapolation
,”
Front. Bioeng. Biotechnol.
,
10
, p.
1533
.10.3389/fbioe.2022.962959
18.
Rabbi
,
M. F.
,
Diamond
,
L. E.
,
Carty
,
C. P.
,
Lloyd
,
D. G.
,
Davico
,
G.
, and
Pizzolato
,
C.
,
2022
, “
A Muscle Synergy-Based Method to Estimate Muscle Activation Patterns of Children With Cerebral Palsy Using Data Collected From Typically Developing Children
,”
Sci. Rep.
,
12
(
1
), p.
10
.10.1038/s41598-022-07541-5
19.
Ajiboye
,
A. B.
, and
Weir
,
R. F.
,
2009
, “
Muscle Synergies as a Predictive Framework for the EMG Patterns of New Hand Postures
,”
J. Neural Eng.
,
6
(
3
), p.
036004
.10.1088/1741-2560/6/3/036004
20.
Meyer
,
A. J.
,
Eskinazi
,
I.
,
Jackson
,
J. N.
,
Rao
,
A. V.
,
Patten
,
C.
, and
Fregly
,
B. J.
,
2016
, “
Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions
,”
Front. Bioeng. Biotechnol.
,
4
, p.
77
.10.3389/fbioe.2016.00077
21.
Bianco
,
N. A.
,
Patten
,
C.
, and
Fregly
,
B. J.
,
2018
, “
Can Measured Synergy Excitations Accurately Construct Unmeasured Muscle Excitations?
,”
ASME J. Biomech. Eng.
,
140
(
1
), p.
011011
.10.1115/1.4038199
22.
Sauder
,
N. R.
,
Meyer
,
A. J.
,
Allen
,
J. L.
,
Ting
,
L. H.
,
Kesar
,
T. M.
, and
Fregly
,
B. J.
,
2019
, “
Computational Design of FastFES Treatment to Improve Propulsive Force Symmetry During Post-Stroke Gait: A Feasibility Study
,”
Front. Neurorobotics
,
13
, p.
80
.10.3389/fnbot.2019.00080
23.
Li
,
G.
,
Ao
,
D.
,
Vega
,
M. M.
,
Shourijeh
,
M. S.
,
Zandiyeh
,
P.
,
Chang
,
S.-H.
,
Lewis
,
V. O.
,
Dunbar
,
N. J.
,
Babazadeh-Naseri
,
A.
,
Baines
,
A. J.
, and
Fregly
,
B. J.
,
2022
, “
A Computational Method for Estimating Trunk Muscle Activations During Gait Using Lower Extremity Muscle Synergies
,”
Front. Bioeng. Biotechnol.
,
10
, p.
964359
.10.3389/fbioe.2022.964359
24.
Rook
,
J. W. A.
,
Sartori
,
M.
, and
Refai
,
M. I.
,
2024
, “
Towards Wearable Electromyography for Personalized Musculoskeletal Trunk Models Using an Inverse Synergy-Based Approach
,”
IEEE Transactions on Medical Robotics and Bionics
, p. 1.10.1109/TMRB.2024.3503900
25.
Tahmid
,
S.
,
Font-Llagunes
,
J. M.
, and
Yang
,
J.
,
2024
, “
Upper Extremity Muscle Activation Pattern Prediction Through Synergy Extrapolation and Electromyography-Driven Modeling
,”
ASME J. Biomech. Eng.
,
146
(
1
), p.
011005
.10.1115/1.4063899
26.
Ting
,
L. H.
, and
Macpherson
,
J. M.
,
2005
, “
A Limited Set of Muscle Synergies for Force Control During a Postural Task
,”
J. Neurophysiol.
,
93
(
1
), pp.
609
613
.10.1152/jn.00681.2004
27.
Delp
,
S. L.
,
Anderson
,
F. C.
,
Arnold
,
A. S.
,
Loan
,
P.
,
Habib
,
A.
,
John
,
C. T.
,
Guendelman
,
E.
, and
Thelen
,
D. G.
,
2007
, “
OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement
,”
IEEE Trans. Biomed. Eng.
,
54
(
11
), pp.
1940
1950
.10.1109/TBME.2007.901024
28.
Ao
,
D.
, and
Fregly
,
B. J.
,
2024
, “
Comparison of Synergy Extrapolation and Static Optimization for Estimating Multiple Unmeasured Muscle Activations During Walking
,”
J. NeuroEng. Rehabil.
,
21
(
1
), p.
194
.10.1186/s12984-024-01490-y
29.
Rabbi
,
M. F.
,
Pizzolato
,
C.
,
Lloyd
,
D. G.
,
Carty
,
C. P.
,
Devaprakash
,
D.
, and
Diamond
,
L. E.
,
2020
, “
Non-Negative Matrix Factorisation is the Most Appropriate Method for Extraction of Muscle Synergies in Walking and Running
,”
Sci. Rep.
,
10
(
1
), p.
8266
.10.1038/s41598-020-65257-w
You do not currently have access to this content.