Quantifying Brain Activities and Lower-Limb Movements during Dual Task Activity Assisted with Auditory Biofeedback: A Pilot Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".