Journal

Quantifying Morphology-Related Deviations in Brain Strain Using an Automated Mesh Morphing Method

Deformation pipeline for subject-specific finite element head model.

Abstract

Finite element head models (FEHMs) have been widely used to study the biomechanics in traumatic brain injury (TBI). Most FEHMs are constructed to reflect the average head shape, which inevitably leads to the omission of individual brain morphology. In this study, an automated mesh morphing method based on radial basis function-thin plate spline (RBF-TPS) with automated landmark extraction and projection was developed. Five representative subject-specific head models and the baseline model were subjected to head kinematics from six datasets covering diverse impact scenarios. Results showed that morphology-related deviations increased with loading severity, reaching up to 0.21 for MPS95 and 0.14 s^-1 for MPSR95. Logistic regression indicated that TBI risk thresholds varied by approximately 19.4% for MPS95 and 11.4% for MPSR95 across representative models. These findings indicate that subject-specific morphology affects strain response beyond size scaling alone, underscoring the importance of incorporating individual morphology into brain injury prediction models.

DOI Source Document
Quantifying Morphology-Related Deviations in Brain Strain Using an Automated Mesh Morphing Method
Local and Global Effects of Inertial Force Components Producing Brain Strain During Head Impacts

Inertial force components and corresponding brain strain distributions.

Abstract

Traumatic brain injury (TBI) is a brain dysfunction caused by an external mechanical force and is a leading cause of disability worldwide. In traumatic brain injury, the brain strain is driven by inertial force associated with head acceleration. We identified three distinct mechanisms by which inertial forces induce brain strain: the global rotation effect, the global translation effect, and the local force effect. The global rotation and translation effects arise from whole-brain movement relative to the skull, producing brain strain through shearing, pushing, and pulling, respectively. In contrast, the local force effect refers to the strain produced inside the brain by the local force without whole-brain movement. These effects correspond to different inertial force components: Euler force (angular acceleration), linear force (linear acceleration), and centrifugal force (angular velocity). In this study, we applied impact loading by each inertial force component independently to quantify their contributions and clarify the conditions under which Holbourn’s hypothesis applies. We found that 97% of the total MPS in American football impacts was produced by the Euler force. When head kinematics were extended to extreme scenarios such as aviation or high-impact accidents, both linear and centrifugal forces were also capable of producing significant brain strain. Independent kinematic thresholds were estimated, showing that most injurious head impacts consistently exceed angular acceleration thresholds, while corresponding linear accelerations and angular velocities remain below them.

DOI Source Document
Local and Global Effects of Inertial Force Components Producing Brain Strain During Head Impacts
创伤性脑损伤研究的人类头部有限元模型研究进展

Representative finite element head models for traumatic brain injury research.

Abstract

创伤性脑损伤（traumatic brain injury,TBI）是发病率、患病率最高的神经系统疾病，为全社会带来了巨大的公共卫生负担。深入研究TBI的生物力学原理有助于提升头部防护效果，发展快速评估技术并采取及时干预，从而降低伤情恶化的风险。人类头部有限元模型（finite element head model,FEHM）作为一种数值分析工具，能够模拟头部在受到冲击时的动态响应，包括脑组织的应力应变时空分布、颅内压的变化等，为理解创伤性脑损伤的力学机制提供了重要依据。本文详细总结了国内外主流的人类头部有限元模型的现状与发展，追溯了模型的发展历程，总结了模型的特点并介绍了基于有限元模型的TBI机制研究进展。对相关研究的总结和梳理将有助于开发新型FEHM，并为创伤性脑损伤的风险评估及防护装备的设计提供理论指导和技术支撑。

DOI Source Document
创伤性脑损伤研究的人类头部有限元模型研究进展
AI-based identification of head impact locations, speeds, and force based on head kinematics simulations

Head impact parameter identification from helmeted impact simulations.

Abstract

Objective: With the development of wearable sensors, head kinematics data have become widely available. However, key impact information—such as impact direction, speed, and force—which is crucial for helmet development, is still not directly measured. This study presents a deep learning model designed to accurately predict these head impact parameters from head kinematics during helmeted impacts. Methods: Leveraging a dataset of 16,000 simulated helmeted head impacts using the Riddell helmet finite element model, a Long Short-Term Memory (LSTM) network was implemented to process the head kinematics: linear accelerations and angular velocities. Results: In simulations, the model accurately predicted impact direction, speed, and the force profile with R² exceeding 70% for all tasks. Validation on 79 on-field impacts recorded by instrumented mouthguards and videos demonstrated that the model significantly outperformed traditional methods in identifying impact locations, achieving 79.7% accuracy compared to the previous highest of 49.4%. Conclusion: This work highlights the potential of deep learning to enhance helmet design and head impact sensing by recovering key impact parameters from wearable kinematic data. Future work should evaluate model performance across helmet types and sports, and apply transfer learning for broader applicability.

DOI Source Document
AI-based identification of head impact locations, speeds, and force based on head kinematics simulations
Differences between two maximal principal strain rate calculation schemes in traumatic brain analysis with in-vivo and in-silico datasets

Comparison of two maximal principal strain rate calculation schemes.

Abstract

Brain deformation caused by a head impact leads to traumatic brain injury (TBI). The maximum principal strain (MPS) was used to measure the extent of brain deformation and predict injury, and recent evidence has indicated that incorporating the maximum principal strain rate (MPSR) and the product of MPS and MPSR, denoted as MPS × SR, enhances the accuracy of TBI prediction. However, ambiguities have arisen about the calculation of MPSR. Two schemes have been utilized: one is to use the time derivative of MPS (MPSR1), and another is to use the first eigenvalue of the strain rate tensor (MPSR2). To quantify the discrepancies between these two methodologies, we compared them across eight in-vivo and one in-silico head impact datasets and found that 95MPSR1 was slightly larger than 95MPSR2 and 95MPS × SR1 was 4.85% larger than 95MPS × SR2 on average. Across every element in all head impacts, the average MPSR1 was 12.73% smaller than MPSR2, and MPS × SR1 was 11.95% smaller than MPSR2. Logistic regression models trained to predict TBI showed no significant difference in predictability between the two schemes. The consequence of misuse of MPSR and MPS × SR thresholds was also examined, showing false decision rates around 1%, suggesting that the two methodologies are not significantly different in detecting TBI.

DOI Source Document
Differences between two maximal principal strain rate calculation schemes in traumatic brain analysis with in-vivo and in-silico datasets
Padded Helmet Shell Covers in American Football: A Comprehensive Laboratory Evaluation with Preliminary On-Field Findings

Laboratory impact configurations for padded helmet shell covers.

Abstract

S.I. : Concussions II Padded Helmet Shell Covers in American Football: A Comprehensive Laboratory Evaluation with Preliminary On-Field Findings NICHOLAS J. C ECCHI ,1 ASHLYN A. C ALLAN,1 LANDON P. W ATSON,1 YUZHE LIU,1 XIANGHAO ZHAN,1 RAMANAND V. V EGESNA,2 COLLIN PANG,1 ENORA LE FLAO,1 GERALD A. G RANT,3,4,5 MICHAEL M. Z EINEH,6 and D AVID B. C AMARILLO1,3,7 1Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; 2Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA; 3Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA; 4Department of Neurology, Stanford University, Stanford, CA 94305, USA; 5Department of Neurosurgery, Duke University, Durham, NC 27710, USA; 6Department of Radiology, Stanford University, Stanford, CA 94305, USA; and 7Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA (Received 29 November 2022; accepted 8 February 2023) Associate Editor Stefan M. Du

DOI Source Document
Padded Helmet Shell Covers in American Football: A Comprehensive Laboratory Evaluation with Preliminary On-Field Findings
AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction

Convolutional neural network architecture for head impact kinematics denoising.

Abstract

Wearable devices used for measuring head impact kinematics are inherently noisy due to imperfect coupling with the human body. This study developed one-dimensional convolutional neural network (1D-CNN) models to denoise tri-axial linear acceleration and angular velocity signals recorded from instrumented mouthguards. Using 163 dummy head impacts for training, validation was performed at three levels: kinematics, brain injury criteria, and tissue-level strain and strain rate. Denoising reduced pointwise RMSE by 36% and peak absolute error by 56%, while six brain injury criteria errors decreased by 82% on average. Maximum principal strain and strain-rate errors were reduced by 35% and 69%, respectively. Blind testing on 118 college football impacts and 413 PMHS impacts demonstrated similar improvements. The 1D-CNN approach provides an effective method for reducing measurement noise in mouthguard-derived head kinematics and supports future real-world TBI monitoring.

DOI Source Document
AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction
A wearable hydraulic shock absorber with efficient energy dissipation

Soft Hydraulic Shock design and helmet implementation.

Abstract

Advances in shock absorber technology are often translated to wearable personal protective equipment to protect humans from impact-related injuries. In this study, the authors leveraged the energy dissipation of fluid flow using soft structures to prototype a novel wearable hydraulic shock absorber, the Soft Hydraulic Shock. The device achieved an efficient energy absorption ratio of 100% across a range of impact loading conditions and maintained stable energy dissipation across a wide temperature range. Finite element analyses further explored its behavior under different design parameters and impact loadings. When implemented into a full helmet system, the Soft Hydraulic Shock significantly mitigated brain injury risk, demonstrating the promise of wearable hydraulic shock absorbers for protective equipment.

DOI Source Document
A wearable hydraulic shock absorber with efficient energy dissipation
Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types

Transfer learning workflow for machine-learning head models.

Abstract

Objective: The machine-learning head model (MLHM) to accelerate the calculation of brain strain and strain rate, which are the predictors for traumatic brain injury (TBI), but the model accuracy was found to decrease sharply when the training/test datasets were from different head impacts types (i.e., car crash, college football), which limits the applicability of MLHMs to different types of head impacts and sports. Particularly, small sizes of target dataset for specific impact types with tens of impacts may not be enough to train an accurate impact-type-specific MLHM. Methods: To overcome this, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR). Results: The strategies were tested on American football (338), mixed martial arts (457), reconstructed car crash (48) and reconstructed American football (36) and we found that the MLHMs developed with transfer learning are significantly more accurate in estimating MPS and MPSR than other models, with a mean absolute error (MAE) smaller than 0.03 in predicting MPS and smaller than 7 s⁻¹ in predicting MPSR on all target impact datasets. High performance in concussion detection was observed based on the MPS and MPSR estimated by the transfer-learning-based models. Conclusion: The MLHMs can be applied to various head impact types for rapidly and accurately calculating brain strain and strain rate. Significance: This study enables developing MLHMs for the head impact type with limited availability of data, and will accelerate the applications of MLHMs.

DOI Source Document
Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types
Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks

Brain deformation prediction using adaptive machine learning head models.

Abstract

Machine-learning head models (MLHMs) have shown promise in estimating traumatic brain injury (TBI)-related brain deformation from head kinematics, but overfitting to simulated impacts and performance degradation under distribution shift hinder clinical deployment. This study introduces an adaptive MLHM combining unsupervised domain adaptation and generative adversarial networks to address dataset distributional differences across simulated impacts, college football, mixed martial arts, and boxing datasets. Using domain regularized component analysis (DRCA) and cycle-GAN-based adaptation, the proposed models markedly improved maximum principal strain (MPS) and strain-rate (MPSR) prediction accuracy. The DRCA-based approach achieved the best performance with MPS mean absolute errors of 0.017 (college football) and 0.020 (MMA), and MPSR MAE of 4.09 s⁻¹ and 6.61 s⁻¹, outperforming baseline MLHMs across multiple hold-out test sets. These results demonstrate that unsupervised domain adaptation effectively reduces cross-domain error and moves MLHMs toward clinically reliable TBI detection.

DOI Source Document
Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks