Library · Simulation & modelling
What Biomechanical Modeling got wrong, from 29 theses
Summary written by a model from the records below: Biomechanical modeling efforts frequently fail when models oversimplify musculoskeletal dynamics, anatomical flexibility, or locomotion state representations. Kinematic estimators and data-driven models also struggle to generalize when trained on constrained single-axis tasks or when relying on heuristic rules instead of dynamic optimization. These records come from graduate theses, 2021 to 2025, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 10 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Oversimplified muscle dynamics and contraction formulations cause simulation instability and prediction errors
Omitting muscle force-length-velocity dynamics or shortening fiber lengths to simulate contractures leads to dynamic instability and excessive actuator forces. Complex activation dynamics also prohibit analytical inversion, while both microscopic Huxley formulations and Hill-type models struggle to predict eccentric contractions, metabolic costs, and activation patterns across varying elevations and mechanical conditions.
Tried and failed
reducing muscle fiber length to simulate contracture applied to musculoskeletal gait simulation. Reason: simulated gait became unstable and fell before producing toe gait kinematic patterns
Investigation of the role of spinal circuits in human motor control, from healthy to rehabilitation conditions · EPFL · PhD
Tried and failed
Active muscle volume modeling applied to locomotion metabolic energy expenditure prediction. Outcome: did not generalise. Reason: Failed to predict metabolic cost and muscle activation differences across varying mechanical footwear conditions
Long-term Effects of Altered Foot-Ankle Mechanics on Locomotion Neuromechanics and Energetics · Georgia Tech · PhD
Tried and failed
musculoskeletal simulation omitting muscle force-length-velocity dynamics applied to biomechanical gait simulation. Outcome: unstable. Reason: neglecting dynamic muscle properties caused reserve actuator forces to exceed acceptable validity thresholds
Predicting lower limb kinematics and kinetics from internal measurement units using deep learning · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected computing control inputs via continuous analytical inverse dynamics because redundant, non-linear activation dynamics and ODE-based muscle contraction models prohibit direct mathematical inversion.
Neuro-musculoskeletal Models: A Tool to Study the Contribution of Muscle Dynamics to Biological Motor Control · Tübingen · PhD
Considered and rejected
Considered and rejected: Rejected using microscopic/biophysical Huxley-type muscle models with explicit damping because they cannot predict mechanical response during eccentric contractions.
Tunable Mechanical Damping in Fast Perturbed Locomotion: Biomechanical Simulations and Biorobotic Applications · Tübingen · PhD
Tried and failed
non-linear Hill muscle model parameterisation applied to cutting speed biomechanics modeling. Outcome: worse than baseline. Reason: produced worse RMSE compared to a simpler linear model
The biomechanics and energetics of foraging in leaf-cutter ants · Imperial · PhD
Tried and failed
Optimization-based musculoskeletal simulation with Hill-type models applied to shoulder muscle activation during overhead tasks. Outcome: did not generalise. Reason: Poor pattern similarity predicting posterior deltoid activation across varied task elevations.
Evaluating Model-Estimated Shoulder Muscle Activity During Overhead Work with Varied Task Demands and Exoskeleton Use · Virginia Tech · Master's
Simple heuristic rules and kinematic proxies underperform relative to direct force sensing and probabilistic optimization
Heuristic thresholding, naive peak detection, and motor averaging models fall short when resolving movement deflections or detecting gait events under compliance and motion artifacts. Similarly, estimating ground reactions or repetitive behaviors from kinematic proxies or behavioral endpoints yields inferior accuracy compared to direct force measurements and full physical representations.
Tried and failed
kinematic inertial sensor estimation applied to ground reaction force estimation. Outcome: worse than baseline. Reason: high stride-by-stride kinematic variability and non-sagittal compensatory movements degraded accuracy relative to direct force sensing
Experimental and Analytical Methods for Understanding User Gait Biomechanics in Response to Soft Ankle Exosuits · Harvard · PhD
Tried and failed
conditioning models on observed behavioral responses applied to neural motion prediction models. Outcome: worse than baseline. Reason: empirical response endpoints provided worse cross-validated likelihood than true physical object endpoints
Mechanisms of Multi-Object Working Memory and Motion Prediction in the Primate Brain · MIT · PhD
Lost to a baseline
Heuristic rules-based algorithm achieved lower overall gait event identification accuracy across locomotion modes (94.87%) compared to the unsupervised BP-AR-HMM (99.6%).
Machine Learning and Wearable Sensors for the Estimation of Biomechanical Variables Outside the Laboratory · University of Oregon · PhD
Tried and failed
multimodal classification omitting primary kinematic modality applied to repetitive behavior classification. Outcome: worse than baseline. Reason: physiological signals lacked sufficient discriminative power without dominant motion features
Explainable and Robust Data-Driven Machine Learning Methods for Digital Healthcare Monitoring · Virginia Tech · Doctorate
Tried and failed
motor averaging model applied to obstacle avoidance reaching under target uncertainty. Outcome: worse than baseline. Reason: failed to predict movement direction deflections compared to performance optimization models
Understanding How Environmental Dynamics and Uncertainty Affect Adaptive Changes in Motor Planning · Harvard · PhD
Considered and rejected
Considered and rejected: Rejected naive peak detection for stride segmentation under exoskeleton pulsing due to motion artifacts and tissue compliance, adopting genetic algorithm optimization.
Effects of Mechanical Interventions on Human Locomotion · MIT · PhD
Rigid control constraints and reduced-order states degrade locomotion synthesis and user adaptation
Enforcing rigid trajectory tracking or fixed passive mechanics restricts natural biomechanical variability and fails to accommodate high-speed walking demands. In simulation, relying on standing-wave patterns, low-dimensional reduced-order states, or flat reinforcement learning architectures leads to suboptimal body configurations and failure to synthesize whole-body motion.
Tried and failed
passive mechanical design for functional locomotion applied to transfemoral prosthetic gait adaptation. Outcome: did not generalise. Reason: fixed mechanical properties could not accommodate high-speed walking demands for all users
The biomechanical effect of prosthetic design for transfemoral amputees. A combined experimental and computational study · Imperial · PhD
Tried and failed
rigid high-gain trajectory tracking without compliance applied to human gait assistance control. Outcome: worse than baseline. Reason: Restricted natural biomechanical variability and movement patterns, causing severe user discomfort.
Partial Assistance with Lower-Limb Exoskeletons to Enhance Gait and Balance in Daily Living Activities · EPFL · PhD
Tried and failed
standing-wave undulatory gait generation applied to limbless locomotion in cluttered environments. Outcome: worse than baseline. Reason: minimal angular range of beneficial obstacle interaction compared to traveling-wave gaits
Geometric modeling of biological and robotic locomotion in highly damped environments · Georgia Tech · PhD
Tried and failed
model-free reinforcement learning with reduced-order state applied to bipedal locomotion control. Outcome: did not converge. Reason: simplified low-dimensional state representation lacked sufficient dynamics information for whole-body motion synthesis
Towards versatile, high-performing and interactive humanoids · UT Austin · PhD
Tried and failed
trajectory optimization with non-zero initial boundary curvature applied to undulatory locomotion gait generation. Outcome: worse than baseline. Reason: enforced non-zero boundary curvature produced energetically suboptimal intermediate S-shaped body configurations
Gait optimality for undulatory locomotion with applications to C. elegans phenotyping · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected non-hierarchical/flat reinforcement learning (E2E and PMTG) for gait discovery due to suboptimal convergence (standing still or 3-legged locomotion).
Agile Legged Robots through Reinforcement Learning and Optimal Control · University of Washington · PhD
Rigid-body segment assumptions fail to capture multi-segment anatomical flexibility and tissue deformations
Treating structures like the foot or spine as single rigid bodies ignores independent column kinematics, relative metatarsal motions, and complex non-rigid tissue deformations. In addition, subject-specific kinematic calibrations and wearable inertial models introduce systematic angular biases and substantially underestimate joint shear forces relative to optical motion capture.
Considered and rejected
Considered and rejected: Rejected using human-subject motion analysis/inverse dynamics to derive foot stiffness due to high individual gait asymmetry confounding, non-rigid body segment violations, and instantaneous center of rotation misalignment.
Evaluating use of a robotic prosthetic foot emulator to test-drive prosthetic feet in people with lower limb amputation: mechanical validation and qualitative interviews · University of Washington · PhD
Considered and rejected
Considered and rejected: Rejected treating tarsals and metatarsals as single rigid segments (as in standard KU-Leuven model) because it overlooks independent medial/lateral column kinematics and relative metatarsal motions.
Strain estimations of the plantar fascia and other ligaments of the foot: Implications for plantar fasciitis · Iowa State · PhD
Lost to a baseline
IMU-based musculoskeletal models underestimated L5/S1 anteroposterior and mediolateral shear forces by 31.1% and 52.6% compared to optical motion capture baseline.
The Effects of Variation in Physical Activity and Spinal Loading on Trunk Muscle Endurance and Lumbar Spine Function · Harvard · PhD
Considered and rejected
Considered and rejected: Standard parametric motion models (e.g., affine transformations) for VNC image registration; rejected because they could not capture complex non-rigid tissue deformations.
Tried and failed
kinematic model calibration using subject data applied to joint angle estimation in motion capture. Outcome: worse than baseline. Reason: introduced significant systematic bias across segments instead of improving ground truth agreement
Integration of Physical and Psychological Human Data into Digital Human Modelling for Manufacturing System Design · Cranfield · PhD
Models trained on constrained or single-axis movements fail to generalize to dynamic multi-axis tasks
Machine learning models trained on symmetric, constrained, or isolated single-degree-of-freedom tasks fail to capture the muscle activation patterns and load variations seen in free multi-axis dynamics. Furthermore, attempts to align pathological gait to normative latent reference profiles or predict shot outcomes from idiosyncratic body kinematics produce poor predictive accuracy.
Tried and failed
latent space alignment to normative reference profile applied to pathological gait recovery assessment. Outcome: did not generalise. Reason: normative latent representation movement negatively correlated with specific localized functional joint kinematics
Gait signatures: data-driven discovery of individual-specific neuromechanical dynamics · Georgia Tech · PhD
Tried and failed
cross-task machine learning model training applied to muscle fascicle length estimation during movement. Outcome: did not generalise. Reason: varying mechanical loads and ranges of motion across different dynamic movement tasks
Machine Learning and Biomechanical Sensing Toward Real-Time In-The-Loop Gait and Joint Health Optimization · Georgia Tech · PhD
Tried and failed
training on constrained tasks to predict unconstrained tasks applied to time-series activity classification. Outcome: did not generalise. Reason: constrained symmetric training data lacked kinematic variability present in free-dynamic movements
Armband EMG-based Lifting Detection and Load Classification Algorithms using Static and Dynamic Lifting Trials · Virginia Tech · Master's
Tried and failed
training on single-axis motions for multi-axis prediction applied to continuous kinematic regression from myoelectric signals. Outcome: did not generalise. Reason: isolated single-degree-of-freedom training data failed to represent muscle activation patterns during combined simultaneous movements
Integrating computer vision with neuromuscular interfacing for the semi-autonomous control of robotic limbs · Imperial · PhD
Considered and rejected
Considered and rejected: Predicting shot outcome from shooter body kinematics was rejected due to idiosyncratic movement across individuals producing weak predictive accuracy.
Basketball Shooting as a Model System for Understanding Skill Learning and Motor Variability · Harvard · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Model mechanical interactions driving biophysical movements in sea-urchin gastrulation and zebrafish epiboly using multi-node lateral vertex simulations. Blocker: Lacks specific biological parameter values and formal model adaptations for sea-urchin and zebrafish morphology.
Cellular Mechanics of Cephalic Furrow Formation in the Drosophila Embryo · Texas Tech · PhD
Left open
Extend the prosthetic foot design framework to model multi-planar motion such as inversion/eversion for uneven terrain and squatting. Blocker: No concrete 3D kinematic/biomechanical models or optimization formulation specified
Development and Validation of a Passive Prosthetic Foot Design Framework based on Lower Leg Dynamics · MIT · PhD
Left open
Improve cross-subject generalization for stance/swing phase duration prediction and frontal plane ankle kinematics/torque. Blocker: Requires multi-sensor HDEMG and biomechanical locomotion data across multiple subjects or a powered lower-limb prosthesis testing apparatus.
A multi-sensor, hybrid model- and signal-based control system for powered lower limb prostheses · Imperial · PhD
Left open
Implement Bayesian deep learning networks on multi-IMU gait data to predict distributions of foot placements with uncertainty estimates. Blocker: Lacks access to the multi-IMU dataset and motion capture ground truth collected for the thesis.
A Deep-learning based Approach for Foot Placement Prediction · Virginia Tech · Master's
Left open
Simulate muscle contributions to balance control across varied step widths during unperturbed steady-state walking using OpenSim musculoskeletal models. Blocker: Requires kinematic, kinetic, and EMG gait data across varied step widths or dynamic predictive simulation setup.
Fall detection and balance control during human movement · UT Austin · PhD
Left open
Train neural networks on simulated or public gait data to predict muscle and joint reaction forces, bypassing musculoskeletal modeling. Blocker: None
Predicting lower limb kinematics and kinetics from internal measurement units using deep learning · Imperial · PhD
Left open
Investigate correlations between center of mass position, limb musculature reconstructions, and locomotion implications across synapsid taxa. Blocker: Lacks specific target taxa, anatomical muscle reconstruction models, and formalized biomechanical framework
Reconstructing Body Size and Center of Mass in Synapsids · Harvard · Master's
Left open
Develop biomechanical demand estimation models using only markerless motion capture kinematic data, eliminating the need for insole pressure measurement kinetics. Blocker: Requires paired markerless motion capture kinematic data and ground-truth biomechanical kinetics/exposures.
Evaluation of Markerless Motion Capture to Assess Physical Exposures During Material Handling Tasks · Virginia Tech · Doctorate
Left open
Compute lower-limb joint quasi-stiffness from perturbation walking kinematics and kinetics data and compare it to steady-state gait. Blocker: Requires experimental biomechanical gait perturbation data (motion capture and force plates) or specialized perturbation simulations.
An assessment of lower-limb joint quasi-stiffness during healthy and transtibial amputee walking · UT Austin · PhD
Left open
Model the effects of aging on lumbar spine tissue loading and biomechanics using modified musculoskeletal and finite element models. Blocker: Lacks specific aged-subject motion capture datasets and age-degraded tissue material properties to parameterize the models
Multi-scale Modeling of the Lumbar Spine During Manual Material Lifting Tasks · Texas Tech · PhD
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Metin Bicer · Imperial · PhD · 2024
Predicting lower limb kinematics and kinetics from internal measurement units using deep learning - Luis G. Rosa · Georgia Tech · PhD · 2025
Machine Learning and Biomechanical Sensing Toward Real-Time In-The-Loop Gait and Joint Health Optimization - Nick Watters · MIT · PhD · 2025
Mechanisms of Multi-Object Working Memory and Motion Prediction in the Primate Brain - Lingyu Li · Virginia Tech · Master's · 2025
Evaluating Model-Estimated Shoulder Muscle Activity During Overhead Work with Varied Task Demands and Exoskeleton Use - Zeynep Özge Orhan · EPFL · PhD · 2025
Partial Assistance with Lower-Limb Exoskeletons to Enhance Gait and Balance in Daily Living Activities - Jazmin Merae Cruz · Texas Tech · PhD · 2025
Multi-scale Modeling of the Lumbar Spine During Manual Material Lifting Tasks - Olivia Walthaus · Imperial · PhD · 2024
The biomechanics and energetics of foraging in leaf-cutter ants - Benjamin Sibson · Harvard · PhD · 2024
The Effects of Variation in Physical Activity and Spinal Loading on Trunk Muscle Endurance and Lumbar Spine Function - Taniel S. Winner · Georgia Tech · PhD · 2024
Gait signatures: data-driven discovery of individual-specific neuromechanical dynamics - Krithika Swaminathan · Harvard · PhD · 2024
Experimental and Analytical Methods for Understanding User Gait Biomechanics in Response to Soft Ankle Exosuits - Alice Julie Bruel · EPFL · PhD · 2024
Investigation of the role of spinal circuits in human motor control, from healthy to rehabilitation conditions - Fabio Izzi · Tübingen · PhD · 2024
Tunable Mechanical Damping in Fast Perturbed Locomotion: Biomechanical Simulations and Biorobotic Applications
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