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Record W7042760896

Predictive balance control during backward walking and effects of a haptic input based intervention on predictive balance control during walking

2022· dissertation· en· W7042760896 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldMaterials Science
TopicX-ray Diffraction in Crystallography
Canadian institutionsnot available
Fundersnot available
KeywordsBalance (ability)GaitPower walkingPreferred walking speedDynamic balanceReliability (semiconductor)Model predictive control
DOInot available

Abstract

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Background\nFalls are a leading cause of injuries and hospitalizations in individuals globally and in Canada.\nEven though falls can occur during any activity, a majority of falls occur during walking.\nUnderstanding and improving balance control during walking can help reduce falls. One\nway of improving balance control may be to add haptic input during walking and including\nbackward and tandem walking in gait training programs.\nPurpose\nThe overall purpose of this study was to examine the balance control and sensorimotor integration\nduring backward walking as well as study the effects of an intervention consisting of backward\nand tandem walking on balance control in healthy adults.\nMethods and results\nStudy one: Test-retest reliability, standard error of measurement, and minimal detectable\nchange were computed for spatiotemporal and balance control measures for forward, backward,\nand tandem walking for fifteen healthy adults. The results demonstrated moderate to excellent\nreliability for all spatiotemporal and balance measures but low to poor reliability for variability\nmeasures for forward, backward, and tandem walking.\nStudy two: Differences in spatiotemporal and balance control measures between forward\nand backward walking and the correlation of backward walking velocity with biomechanical\nbalance control measures during forward and tandem walking were examined in fifty-five\nhealthy adults. Backward walking was significantly different in terms of spatiotemporal and\nbalance control measures compared to forward walking. Participants walked significantly\nslower and with a significant reduction in relative double support time during backward walking\ncompared to forward walking. Step length and anteroposterior margin of stability were significantly\nreduced, and step width and mediolateral margin of stability were significantly increased\nduring backward walking compared to forward walking. Backward walking was also significantly\nmore variable compared to forward walking. Step length, step width, and anteroposterior and\nmediolateral margins of stability were significantly more variable during backward walking\ncompared to forward walking. Velocity during backward walking showed a significant positive\ncorrelation with anteroposterior margin of stability and velocity during forward walking and a\nsignificant negative correlation with step length variability during forward walking.\nStudy three: The effects of vision and haptic input added with haptic anchors during backward\nwalking was examined in 55 healthy adults. It was observed that walking backward with\neyes closed significantly changed spatiotemporal and balance control measures compared\nto walking with eyes open. Participants walked slower, with an increased amount of double\nsupport time, reduced step length, and increased step width when walking backward with\neyes closed compared to walking with eyes open. Variability of step width and margin of\nstability in the anteroposterior and mediolateral directions were also significantly higher when\nwalking backward with eyes closed. Margin of stability in the mediolateral direction was\nsignificantly lower when walking backward with the haptic anchors compared to walking\nwithout haptic anchors. An interaction between vision and haptic input revealed that step\nlength was significantly lower when walking backward using the haptic anchors compared\nto walking without haptic anchors in the eyes open condition.\nStudy four: This study examined the effects of a six-week (three days/week) intervention on\nbalance control during forward, backward, and tandem walking in a total of forty-five healthy\nadults. Fifteen participants completed the intervention using haptic anchors, another fifteen\ncompleted the same intervention without the haptic anchors, and a control group of fifteen\nparticipants did not complete the intervention. The intervention consisted of performing ten\ntrials each of backward and tandem walking with eyes closed over a distance of ten meters\nin random order at the participants’ preferred speed. During forward walking, change in step\nlength variability was significantly higher in the eyes closed condition compared to the eyes\nopen condition. During backward walking, velocity, %DS, and step length change scores\nwere significantly higher in the eyes closed condition compared to eyes open and the change\nscore for AP MOS was significantly higher in the eyes closed condition compared to the eyes\nopen condition only for the group that trained without the haptic anchors. During tandem\nwalking, change score for ML MOS was significantly lower in the eyes closed condition\ncompared to the eyes open condition. No significant effects of the intervention were observed\non any measures for forward, backward, and tandem walking except the AP MOS change\nscores in the group that performed the intervention without using the haptic anchors.\nConclusion\nThis thesis provided novel evidence on the reliability of spatiotemporal and balance control\nmeasures across three different walking styles. The findings provide support in favour of\nusing MOS measures as well as backward walking to assess mobility and integrity of the\nbalance control system. The insignificant effects of the haptic input based intervention warrants\nfurther research on the long-term use of haptic anchors to improve balance control.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.002
GPT teacher head0.167
Teacher spread0.165 · 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 teacher head, not a consensus.

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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