Subject-Specific Predictive Musculoskeletal Simulations of Lower-Limb Exoskeleton Assistance: Metabolic and Biomechanical Effects of Joint Assistance Strategies
Authors: Neethan Ratnakumar, Mariya Tohfafarosh, Xianlian Zhou
Organizations: Department of Mechanical Engineering, University of Jaffna, Sri Lanka · Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, 07102, USA
Lower-limb exoskeletons have made considerable progress in reducing energy expenditure during walking. However, designing optimal assistance strategies remains challenging, particularly given inter-individual variability in anthropometry and biomechanics. This study explores energy-optimal lower-limb joint-assistance strategies using predictive simulations with musculoskeletal models. Subject-specific models of six able-bodied subjects, with BMI-based muscle strength scaling, were used in predictive simulations to generate gait at self-selected walking speeds. Ideal actuators were incorporated to simulate various combinations of joint assistance at peak levels of 25 Nm and 50 Nm to examine the effects of assistance on gait and metabolic savings. The effects of each assistance configuration were assessed through cost of transport (COT), joint kinematics, muscle activations, assistive torques, and joint-level power metrics to characterize the biomechanical and energetic impacts of different assistance strategies. At 50 Nm, combined H+K+A (hip-knee-ankle) assistance resulted in the greatest mean COT reduction of 48.50 +/- 4.55%, with individual reductions ranging from 42.22% to 54.65% across the subjects. Among single-joint conditions, assisting the hip was most effective, reducing COT by 31.77 +/- 5.21%; the knee and ankle produced smaller, comparable reductions (19.17% and 17.02%). H+A (hip-ankle) assistance (44.60 +/- 7.08%) emerged as the most effective two-joint assistive configuration. Increasing the torque bound increased positive assistive power primarily at the hip and ankle, while knee assistance showed little sensitivity and delivered positive power near pre-swing. These results support H+A assistance as an efficient two-actuator target, while identifying the knee's atypical pre-swing power strategy as a candidate for targeted experimental validation.
Figures & tables
Figure 1: \par Overview of the OpenSim Moco optimal-control framework used to generate the unassisted and exoskeleton-assisted predictive walking simulations. Exoskeleton assistance is modeled as ideal torque actuation at the joint coordinates, optimized together with other variables such as muscle control and joint angles.
Figure 2: \par The generic and scaled musculoskeletal models. The cross-sectional size of each muscle is scaled proportionally to its maximum isometric force, with floor contact represented by contact spheres.
Subject ID
Age
Height (m)
Weight (kg)
BMI
Sex
Tracking Speed (m/s)
AB06
20
1.80
74.84
23.10
M
1.42
AB07
20
1.65
55.34
20.33
M
0.92
AB14
22
1.52
58.41
25.28
F
1.68
AB15
21
1.78
96.16
30.35
M
1.21
AB18
19
1.80
60.13
18.56
F
1.46
AB24
21
1.73
72.57
24.25
F
1.37
Table 1: \par Subject Demographics and Tracking Data Walking Speeds
Figure 3: \par Hip flexion-extension angles from predictive tracking simulations across seven exoskeleton assistance conditions ( 50Nm\par ) and the unassisted baseline (NoExo). Each subplot shows the hip angle profile obtained by solving the optimal control problem for the specific condition. The solid purple line and shaded area represent the mean ± standard deviation across subject-specific simulations. For each subject-specific model, one representative unassisted gait cycle from that subject was used as the tracking reference. The dashed tracking mean data are averaged from the six subjects, and the experimental angle data from all subjects in Camargo et al. [ camargo2021comprehensive\par\par ] is shown in black.
Figure 4: \par Knee flexion-extension angles resulting from predictive tracking simulations across seven exoskeleton assistance conditions ( 50Nm\par ) and the unassisted baseline (NoExo). Each subplot shows the knee angle profile obtained by solving the optimal control problem for the specific condition. The solid purple line and shaded area represent the mean ± standard deviation across subject-specific simulations. For each subject-specific model, one representative unassisted gait cycle from that subject was used as the tracking reference. The dashed tracking mean data are averaged from the six subjects, and the experimental angle data from all subjects in Camargo et al. [ camargo2021comprehensive\par\par ] is shown in black.
Figure 5: \par Ankle dorsiflexion-plantarflexion angles resulting from predictive tracking simulations across seven exoskeleton assistance conditions ( 50Nm\par ) and the unassisted baseline (NoExo). Each subplot shows the ankle angle profile obtained by solving the optimal control problem for the specific condition. The solid purple line and shaded area represent the mean ± standard deviation across subject-specific simulations. For each subject-specific model, one representative unassisted gait cycle from that subject was used as the tracking reference. The dashed tracking mean data are averaged from the six subjects, and the experimental angle data from all subjects in Camargo et al. [ camargo2021comprehensive\par\par ] is shown in black.
Figure 6: \par Overall simulated hip, knee, and ankle joint angles averaged across all subject-specific models at 50Nm\par assistance condition. The purple solid colored line represents the mean joint angle trajectory, and the shaded area denotes the standard deviation. Experimental data from Camargo et al. [ camargo2021comprehensive\par\par ] is shown in black with a gray shaded region representing ± 1 SD.
Figure 7: \par Predicted hip assistive torque ( 50Nm\par ) profiles across the gait cycle for the four exoskeleton configurations (Hip Only, H+A, H+K, and H+K+A) that incorporate the hip assistance. Dashed colored lines indicate individual subject data, while the purple line (with shaded ± SD) represents the subject-specific model group average. The black line shows the experimental biological hip joint torque from Camargo et al. [ camargo2021comprehensive\par\par ] , and the green line (with shaded ± SD) depicts the mean of the NoExo condition for the subject-specific models.
Figure 8: \par Predicted knee assistive torque ( 50Nm\par ) profiles across the gait cycle for the four exoskeleton configurations (Knee Only, H+K, K+A, and H+K+A) that incorporate the knee assistance. Dashed colored lines indicate individual subject data, while the purple line (with shaded ± SD) represents the subject-specific model group average. The black line shows the experimental biological knee joint torque from Camargo et al. [ camargo2021comprehensive\par\par ] , and the green line (with shaded ± SD) depicts the mean of the unassisted condition for the subject-specific model.
Figure 9: \par Predicted ankle assistive torque ( 50Nm\par ) profiles across the gait cycle for the four exoskeleton configurations (Ankle Only, H+A, K+A, and H+K+A) that incorporate the ankle assistance. Dashed colored lines indicate individual subject data, while the purple line (with shaded ± SD) represents the subject-specific model group average. The black line shows the experimental biological ankle joint torque from Camargo et al. [ camargo2021comprehensive\par\par ] , and the green line (with shaded ± SD) depicts the mean of the unassisted condition for the subject-specific model.
Figure 10: \par Overall assistive torques for the hip, knee, and ankle joints ( 50Nm\par ) obtained across all subjects and exoskeleton assistance conditions. Purple solid lines represent the predicted mean assistive torque profiles across all subjects and conditions, with purple shaded regions denoting ± 1 standard deviation. The black line shows the experimental biological joint torque from Camargo et al. [ camargo2021comprehensive\par\par ] , and the green line (with shaded ± SD) depicts the mean of the NoExo (unassisted) condition for the subject-specific model.
Figure 11: \par Predicted hip joint power under 50Nm\par assistance across the gait cycle for the four exoskeleton configurations (Hip Only, H+A, H+K, and H+K+A) that incorporate the hip. Dashed colored lines indicate individual subject data, while the purple line (with shaded ± SD) represents the subject-specific model group average. The black line shows the experimental biological hip joint power profile from Camargo et al. [ camargo2021comprehensive\par\par ] , and the green line (with shaded ± SD) depicts the mean of the NoExo condition for the subject-specific model.
Figure 12: \par Predicted knee joint power under 50Nm\par assistance across the gait cycle for the four exoskeleton configurations (Knee Only, H+K, K+A, and H+K+A) that incorporate the knee. Dashed colored lines indicate individual subject data, while the purple line (with shaded ± SD) represents the subject-specific model group average. The black line shows the experimental biological knee joint power profile from Camargo et al. [ camargo2021comprehensive\par\par ] , and the green line (with shaded ± SD) depicts the mean of the NoExo condition for the subject-specific model.
Figure 13: \par Predicted ankle joint power under 50Nm\par assistance across the gait cycle for the four exoskeleton configurations (Ankle Only, H+A, K+A, and H+K+A) that incorporate the ankle. Dashed colored lines indicate individual subject data, while the purple line (with shaded ± SD) represents the subject-specific model group average. The black line shows the experimental biological ankle joint power profile from Camargo et al. [ camargo2021comprehensive\par\par ] , and the green line (with shaded ± SD) depicts the mean of the NoExo condition for the subject-specific model.
Figure 14: \par Overall assistive power for the hip, knee, and ankle joints ( 50Nm\par ) obtained across all subjects and exoskeleton assistance conditions. Purple solid lines represent the predicted mean assistive power profiles across all subjects and conditions, with purple shaded regions denoting ± 1 standard deviation. The black line shows experimental the biological joint power from Camargo et al. [ camargo2021comprehensive\par\par ] , and the green line (with shaded ± SD) depicts the mean of the NoExo (unassisted) condition for the subject-specific model.
Figure 15: \par Muscle activation profiles across nine lower-limb muscles over the gait cycle, averaged across subjects, under NoExo and seven exoskeleton assistance conditions (Hip Only, Knee Only, Ankle Only, H+K, H+A, K+A, H+K+A) at 50Nm\par assistive torque. The blue curves in different shades show the single-joint conditions, the red shaded lines show the double-joint conditions, and the purple line shows the three-joint condition. The black line shows experimental biological muscle activations from Camargo et al. [ camargo2021comprehensive\par\par ] (with shaded ± SD), and the green line depicts the mean of the NoExo condition for the subject-specific models. *Note: The surface EMG data for IL and BFSH were not available in the dataset as they are deep muscles that are difficult to measure. In the GMax panel, the black experimental curve corresponds to GMed, as GMax data were not available.
Joint
Torque (Nm)
Condition
MPP (W/kg)
MNP (W/kg)
PPR
Metabolic Reduction (W/kg)
\multirow 8*Hip
\multirow 4*25
Hip Only
0.279
-0.052
0.843
1.032
H+A
0.274
-0.053
0.838
1.442
H+K
0.276
-0.049
0.849
1.345
H+K+A
0.248
-0.062
0.800
1.778
\multirow 4*50
Hip Only
0.375
-0.081
0.822
1.323
H+A
0.371
-0.085
0.814
1.869
Table 2: \par Subjects’ mean power of individual joints under 25Nm\par and 50Nm\par assistive torques and different combinations of joint assistance. The corresponding metabolic reduction is also presented.
Joint
τ (Nm)
MPP (W/kg)
MNP (W/kg)
PPR
τMAX (Nm/kg)
τRMS (Nm/kg)
\multirow 2*Hip
25
0.267
-0.052
0.837
0.372
0.246
50
0.359
-0.079
0.820
0.729
0.394
\multirow 2*Knee
25
0.163
-0.084
0.660
0.310
0.162
50
0.168
-0.082
0.672
0.339
0.167
\multirow 2*Ankle
25
0.124
-0.032
0.795
0.372
0.181
50
0.205
-0.033
0.861
0.745
0.283
Table 3: \par Mean Joint Power Magnitudes under 25 Nm and 50 Nm assistive torques (averaged from all corresponding joint assistance cases). All PPR values were computed from the instantaneous joint power time series using Eq. 2.4 \par Power analysis . For comparison, the unassisted biological knee PPR, computed the same way, was 0.352 for the predicted NoExo condition and 0.162 for the experimental data.
Figure 16: \par Mean COT values across exoskeleton assistance configurations at both 25Nm\par and 50Nm\par assistance levels. Colored dots represent individual subject-specific model results, with group means and standard deviations shown as bars.The cross-hatched bars are for 25Nm\par , and the solid-color bars represent the mean COT for 50Nm\par . The dashed line marks the mean unassisted NoExo COT. The percentage reduction in COT relative to the unassisted NoExo condition is shown above each bar.
Appendix figures & tables14 assets
Supplementary material from the paper’s appendix.
Appendix
Muscle
AB06
AB07
AB14
AB15
AB18
AB24
HAMS
3071
2042
2990
5421
1846
3243
BFSH
914
608
890
1614
550
966
GMAX
2211
1470
2152
3903
1329
2335
IL
2664
1771
2593
4702
1601
2813
RF
1330
884
1294
2347
799
1404
VAS
5687
3781
5536
10038
3419
6006
Appendix
Table 4: \par Maximum isometric muscle forces (N) for the subject-specific models obtained by scaling the generic model values based on subject anthropometry.
Muscle
Max Iso. Force (N)
Opt. Fiber (m)
Slack Length (m)
Pennation (rad)
HAMS
2700
0.1091
0.3262
0.0000
BFSH
804
0.1719
0.0884
0.4014
GMAX
1944
0.1548
0.0723
0.0000
IL
2342
0.0975
0.1560
0.1396
RF
1169
0.1142
0.3104
0.0873
VAS
5000
0.1077
0.1168
0.0524
Appendix
Table 5: \par Key muscle properties for the generic model.
Condition
AB06
AB07
AB14
AB15
AB18
AB24
25 Nm
Hip Only
25.85
27.51
25.72
15.82
30.10
21.62
Knee Only
16.08
17.10
18.13
16.61
20.10
15.18
Ankle Only
12.16
11.19
14.41
8.64
11.51
13.21
H+K
32.88
32.34
33.42
26.16
34.73
26.31
H+A
36.25
35.81
37.35
23.50
38.84
31.94
Appendix
Table 6: \par Percentage reduction in COT relative to the unassisted (NoExo) condition for each subject across assistance configurations at 25, and 50 Nm
Comparison
Test
Statistic
Raw p
Holm-adjusted p
H, K, A
Exact Friedman
Q=9.33
0.0055
0.0166 (significant)
H+K, H+A, K+A
Exact Friedman
Q=9.00
0.0081
0.0166 (significant)
H+K+A versus H+A
Exact paired Wilcoxon signed-rank
W=1.00
0.0625
0.0625 (not significant)
Appendix
Table 7: \par Statistical comparisons of COT reduction among assistance configurations at 50Nm\par .
Figure 17: \par Hip flexion-extension angles resulting from predictive tracking simulations across seven exoskeleton assistance conditions (25 Nm) and the unassisted baseline (NoExo). Each subplot shows the hip angle profile obtained by solving the optimal control problem for the specific condition.
Figure 18: \par Knee flexion-extension angles resulting from predictive tracking simulations across seven exoskeleton assistance conditions (25 Nm) and the unassisted baseline (NoExo). Each subplot shows the knee angle profile obtained by solving the optimal control problem for the specific condition.
Figure 19: \par Ankle dorsiflexion-plantarflexion angles resulting from predictive tracking simulations across seven exoskeleton assistance conditions (25 Nm) and the unassisted baseline (NoExo). Each subplot shows the ankle angle profile obtained by solving the optimal control problem for the specific condition.
Figure 20: \par Predicted hip assistive torque (25 Nm) profiles across the gait cycle for the four exoskeleton configurations (Hip Only, H+A, H+K, and H+K+A) that incorporate the hip.
Figure 21: \par Predicted knee assistive torque (25 Nm) profiles across the gait cycle for the four exoskeleton configurations (Knee Only, H+K, K+A, and H+K+A) that incorporate the knee.
Figure 22: \par Predicted ankle assistive torque (25 Nm) profiles across the gait cycle for the four exoskeleton configurations (Ankle Only, H+A, K+A, and H+K+A) that incorporate the ankle.
Figure 23: \par Predicted hip joint power under 25 Nm assistance across the gait cycle for the four exoskeleton configurations (Hip Only, H+A, H+K, and H+K+A) that incorporate the hip.
Figure 24: \par Predicted knee joint power under 25 Nm assistance across the gait cycle for the four exoskeleton configurations (Knee Only, H+K, K+A, and H+K+A) that incorporate the knee.
Figure 25: \par Predicted ankle joint power under 25 Nm assistance across the gait cycle for the four exoskeleton configurations (Ankle Only, H+A, K+A, and H+K+A) that incorporate the ankle.
Figure 26: \par Muscle activation profiles across nine lower-limb muscles over the gait cycle, averaged across subjects, under NoExo and seven exoskeleton assistance conditions (Hip Only, Knee Only, Ankle Only, H+K, H+A, K+A, H+K+A) at 25Nm\par assistive torque. *Note: The experimental data for iliopsoas and biceps femoris short head was not available.
Biological torque control directly maps an estimated human joint moment to exoskeleton assistance, providing a task-agnostic strategy for supporting diverse locomotor activities. However, it remains unclear whether a fixed state-to-torque mapping provides effective assistance across biomechanically distinct tasks. We examined how assistance delay affected hip exoskeleton performance during level-ground (LG), ramp-ascent (RA), and ramp-descent (RD) walking. Eight participants completed a zero-torque baseline condition and five active assistance conditions with delays ranging from 40 to 320 ms. Across tasks and active delays, assistance reduced net metabolic rate by 5.24%, positive biological hip joint work by 5.86%, and total lower-limb positive joint work by 1.68% (all p < 0.05). Assistance delay affected both joint-work outcomes (both p < 0.001) but not net metabolic rate. Mechanical unloading generally decreased with increasing delay, whereas metabolic benefits remained comparatively stable. Relative to the zero-torque condition, net metabolic rate decreased by 9.75% during LG and 7.20% during RA but increased by 1.23% during RD. We did not detect task-dependent differences in the delay response. Our findings indicate that biological torque mappings should be evaluated based on the target outcome and mechanical role of the assisted joint, and that predominantly positive-power assistance may not generalize to negative-work-dominant locomotion without modification.
Jimin An, Ryan Lee, Jingshu Peng +2
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213 USA
Ground-truth human joint torque estimation relies on motion capture systems, which suffer from limited outdoor usability and significant deployment expenses. Furthermore, direct scaling of ground-truth joint torques to obtain motor torque commands is not necessarily the optimal strategy. To address the aforementioned limitations, inspired by the human motion generation process, this paper proposes a novel assistance torque estimation method based on the dynamic model. From an optimization perspective, the proposed method directly generates motor-assist torque and lowers the cost of data acquisition. Then, a data-driven assistance torque prediction network is trained to enable accurate real-time prediction under complex outdoor environments. Experimental results demonstrate that optimized (estimated) assistance torque exhibits better phase consistency with gait trajectories and better alignment with task characteristics. Relative to the Zero torque condition, the predicted torque can decrease metabolic rate by 11.8%-17.7%, heart rate by 8.9%-14.3%, and peak muscle activation levels by 28.2%-54.0%, respectively. This provides a new perspective for low-cost adaptive exoskeleton assistance.
Xiao-Yin Liu, Guotao Li, Weiqun Wang +1
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. · The School of Artificial Intelligence, University of Chinese Academy Sciences, Beijing, China.
The understanding of natural human adaptation during exoskeleton-assisted locomotion - particularly individual differences in adaptation behaviors and temporal progression - remains limited. In this work, we investigate temporal evolution of biomechanical variables to uncover participant-specific adaptation strategies across different exoskeleton-assisted locomotion scenarios. Nine healthy participants performed treadmill walking under three conditions: without an exoskeleton, with exoskeleton active ankle assistance, and with exoskeleton zero-torque. Lower limb kinematics, inter-joint coordination, and metabolic cost of transport (MCoT) were analyzed at both the group and individual levels. Results indicate that adaptation is gradual and highly individualized, with substantial variability in convergence timing and movement patterns across participants. Kinematic adaptation occurred asynchronously across lower limb, with larger fluctuations during the swing phase. Metabolic responses were heterogeneous and often non-convergent, highlighting the limitations of steady-state assumptions commonly adopted in the literature. These findings emphasize the importance of individual-level, temporal evolution analyses for understanding adaptation dynamics in exoskeleton use.
Peter Seungjune Lee, Katja Mombaur
KIT BioRobotics Lab, Optimization and Biomechanics for Human-Centred Robotics, Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany · Human-Centred Robotics and Machine Intelligence, department of systems design engineering, University of Waterloo, Waterloo, N2L3G1, Canada