MétaCan
Menu
Back to cohort

No Contact Needed: Humans Adapt Their Gait to Suit Legged Robot Companions

2023· article· en· W4389665710 on OpenAlexfundno aff
Paul M. Riek, Amy R. Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGaitRobotPhysical medicine and rehabilitationEffect of gait parameters on energetic costComputer sciencePower walkingGait analysisArtificial intelligenceSimulationPreferred walking speedPsychologyMedicine

Abstract

fetched live from OpenAlex

Legged robots companions may one day assist humans with everyday tasks, but their possible impact on human gait is unknown. While previous studies have shown that humans adjust their gait when walking with other humans, it is uncertain whether walking with legged robots would yield similar results. In this study, we measured the gait of healthy participants (N = 14) while they walked alone and with a small quadruped robot. Spatiotemporal and stability gait parameters were calculated to determine whether the presence of the robot affected participants' gait. We found that walking with robots primarily affected measures in the walking direction. Participants walked slower and with altered anterior-posterior stability with the robot, but we did not find significant differences in the mediolateral direction in terms of step width or stability. However, we also observed that variability in the mediolateral distance between the robot and our participants also influenced participant gait behaviour. Our results demonstrate that robots do influence human gait even without physical contact and through seemingly innocuous actions such as walking near them.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.007

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 head0.022
GPT teacher head0.224
Teacher spread0.203 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicRobotic Locomotion and ControlFrench-language works237,207