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Record W2075647054 · doi:10.1177/02783640122068209

Torso Force Feedback Realistically Simulates Slope on Treadmill-Style Locomotion Interfaces

2001· article· en· W2075647054 on OpenAlexfundno aff
John M. Hollerbach, Rose Mills, David Tristano, Robert Christensen, William B. Thompson, Yangming Xu

Bibliographic record

VenueThe International Journal of Robotics Research · 2001
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsnot available
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsTorsoTreadmillSimulationTilt (camera)Haptic technologyComputer scienceEngineeringMechanical engineeringPhysical therapy

Abstract

fetched live from OpenAlex

This paper investigates whether torso force feedback on treadmillstyle locomotion interfaces can substitute for treadmill tilt to simulate walking on smooth inclines. The experimental platform is the Sarcos Treadport, whose active mechanical tether can apply horizontal forces to the user to simulate the gravity forces experienced in slope walking. The authors show that users are extremely sensitive to slope while walking, being able to discriminate a 0.5 degree slope change. Comparisons are then made between walking on a tilted treadmill platform versus walking on a level platform but with tether force application. Psychophysical experiments show that users select tether forces that are predicted by the gravity forces, although at a 65% fractional force level. These results demonstrate definitively that torso force feedback can realistically simulate gravity forces during smooth slope walking.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0020.000

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 head0.054
GPT teacher head0.345
Teacher spread0.291 · 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 designBench or experimental
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

Citations47
Published2001
Admission routes1
Has abstractyes

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