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Record W4396975764 · doi:10.1186/s12984-024-01378-x

Exploring the challenges of avoiding collisions with virtual pedestrians using a dual-task paradigm in individuals with chronic moderate to severe traumatic brain injury

2024· article· en· W4396975764 on OpenAlexafffund
Thiago de Aquino Costa Sousa, Isabelle Gagnon, Karen Li, Bradford J. McFadyen, Anouk Lamontagne

Bibliographic record

VenueJournal of NeuroEngineering and Rehabilitation · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité LavalCentre for Interdisciplinary Research in RehabilitationConcordia UniversityMontreal Children's HospitalMcGill UniversityJewish Rehabilitation Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTraumatic brain injuryTask (project management)Physical medicine and rehabilitationNeurologyDual (grammatical number)PsychologyNeuroscienceMedicineCognitive psychologyPsychiatryEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Individuals with a moderate-to-severe traumatic brain injury (m/sTBI), despite experiencing good locomotor recovery six months post-injury, face challenges in adapting their locomotion to the environment. They also present with altered cognitive functions, which may impact dual-task walking abilities. Whether they present collision avoidance strategies with moving pedestrians that are altered under dual-task conditions, however, remains unclear. This study aimed to compare between individuals with m/sTBI and age-matched control individuals: (1), the locomotor and cognitive costs associated with the concurrent performance of circumventing approaching virtual pedestrians (VRPs) while attending to an auditory-based cognitive task and; (2) gaze behaviour associated with the VRP circumvention task in single and dual-task conditions. METHODOLOGY: Twelve individuals with m/sTBI (age = 43.3 ± 9.5 yrs; >6 mo. post injury) and 12 healthy controls (CTLs) (age = 41.8 ± 8.3 yrs) were assessed while walking in a virtual subway station viewed in a head-mounted display. They performed a collision avoidance task with VRPs, as well as auditory-based cognitive tasks (pitch discrimination and auditory Stroop), both under single and dual-task conditions. Dual-task cost (DTC) for onset distance of trajectory deviation, minimum distance from the VRP, maximum lateral deviation, walking speed, gaze fixations and cognitive task accuracy were contrasted between groups using generalized estimating equations. RESULTS: In contrast to CTLs who showed locomotor DTCs only, individuals with m/sTBI displayed both locomotor and cognitive DTCs. While both groups walked slower under dual-task conditions, only individuals with m/sTBI failed to modify their onset distance of trajectory deviation and maintained smaller minimum distances and smaller maximum lateral deviation compared to single-task walking. Both groups showed shorter gaze fixations on the approaching VRP under dual-task conditions, but this reduction was less pronounced in the individuals with m/sTBI. A reduction in cognitive task accuracy under dual-task conditions was found in the m/sTBI group only. CONCLUSION: Individuals with m/sTBI present altered locomotor and gaze behaviours, as well as altered cognitive performances, when executing a collision avoidance task involving moving pedestrians in dual-task conditions. Potential mechanisms explaining those alterations are discussed. Present findings highlight the compromised complex walking abilities in individuals with m/sTBI who otherwise present a good locomotor recovery.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.091
GPT teacher head0.321
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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