Evidence map›Paper›PMID 36921692›Full record

ArticleJournal of sport and health science2023

Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics.

Xianghao Zhan, Yiheng Li, Yuzhe Liu, Nicholas J Cecchi, Samuel J Raymond, Zhou Zhou, Hossein Vahid Alizadeh, Jesse Ruan, Saeed Barbat, Stephen Tiernan and 4 more

Abstract read
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In one paragraph

Article in Journal of sport and health science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Quantitative video analysis of head acceleration events: a review.Frontiers in bioengineering and biotechnology · 2025
    Review
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Xianghao ZhanDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Yiheng LiDepartment of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
Yuzhe LiuDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA. Electronic address: yuzheliu@buaa.edu.cn.
Nicholas J CecchiDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Samuel J RaymondDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Zhou ZhouDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Hossein Vahid AlizadehDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Jesse RuanFord Motor Company, 3001 Miller Rd, Dearborn, MI 48120, USA.
Saeed BarbatFord Motor Company, 3001 Miller Rd, Dearborn, MI 48120, USA.
Stephen TiernanTechnological University Dublin, Dublin, D07 EWV4, Ireland.
Olivier GevaertDepartment of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
Michael M ZeinehDepartment of Radiology, Stanford University, Stanford, CA 94305, USA.
Gerald A GrantDepartment of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
David B CamarilloDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA.

Funding

Disseminating a validated mouthguard sensor to investigate the effect of head impacts on brain healthR24NS098518 · NINDS · STANFORD UNIVERSITY · PI CAMARILLO, DAVID, GRANT, GERALD ARTHUR · 2018 to 2021
$1.1M
NINDS NIH HHS R24 NS098518
6 · The paper itself

Abstract

backgroundTraumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo, and the characteristics of different types of impacts are not well studied. We investigated the spectral characteristics of different head impact types with kinematics classification.

methodsData were analyzed from 3262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football, car crash, mixed martial arts). To test the classifier robustness, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards. Finally, with the classifier, type-specific, nearest-neighbor regression models were built for brain strain.

resultsThe classifier reached a median accuracy of 96% over 1000 random partitions of training and test sets. The most important features in the classification included both low- and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low- and high-frequency ranges (e.g., the spectral densities of mixed martial arts impacts were higher in the high-frequency range than in the low-frequency range). The type-specific regression showed a generally higher R

conclusionThe machine-learning-based classifier enables a better understanding of the impact kinematics spectral density in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation.

Indexed as

Brain Injuries, TraumaticMachine LearningBiomechanical PhenomenaHeadHumansMouth ProtectorsClassificationContact sportsHead impactsImpact kinematicsTraumatic brain injury

Identifiers

PMID36921692
PMCPMC10466194

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.