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Record W4402452678 · doi:10.11159/icbes24.141

Quantifying Brain Activities and Lower-Limb Movements during Dual Task Activity Assisted with Auditory Biofeedback: A Pilot Study

2024· article· en· W4402452678 on OpenAlexvenueno aff
Swapno Aditya, Winson C.C. Lee, Adam R. Clarke, Lucy Armitage, Victoria Traynor, Evangelos Pappas, Thanaporn Kanchanawong

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2024
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsnot available
FundersUniversity of Wollongong
KeywordsBiofeedbackTask (project management)Dual (grammatical number)Computer scienceBrain activity and meditationPhysical medicine and rehabilitationHuman–computer interactionPsychologyNeuroscienceMedicineElectroencephalographyEngineering

Abstract

fetched live from OpenAlex

Conducting multiple tasks at the same time is a staple habit in our daily life.But during multitasking, there are notable reductions in performance and movement mechanics.Studying the effects of 'dual task' protocols will provide an understanding about its impact on movement and cognitive processing.During dual tasking scenarios, cognitive processing is strained, resulting in additional cognitive resource allocation to maintain performance.Biofeedback methods have been used to improve movement mechanics in upper and lower body (particularly during walking) and have yielded promising results.However, questions arise about whether a cognitively challenging setting such as a 'Dual Task' protocol, with a biofeedback method, impairs tasks further because the brain must process additional external information while already under stress.The aim of this pilot study was to explore the role of rhythmic cues as a biofeedback method in changing lower limb movement patterns in a cognitively challenging arrow matching dual task protocol.Five young adults participated in a foot tapping + flanker task dual task experiment, where rhythmic metronome beats were provided as biofeedback to match foot taps during a dual task protocol (the rhythm of a metronome was set from the natural foot tapping frequency of the participants which was determined before the experiments commenced).Electroencephalography, kinematic data of the ankle joints, and foot tap forces were collected and analysed.Findings suggested rhythmic biofeedback from the metronome negatively impacted on foot tapping variability during dual task protocol.Additional observations indicated that biofeedback diverted attention towards having a more stable foot tapping performance, but at the cost of performance in the arrow matching task (decreased accuracy).Further studies will assist in identifying the usefulness of biofeedback methods for improving multitasking capabilities, including performing both tasks efficiently.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.246
Teacher spread0.225 · 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

Citations1
Published2024
Admission routes1
Has abstractno

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