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Record W2102663373 · doi:10.1109/toh.2009.37

Perceived Vibration Strength in Mobile Devices: The Effect of Weight and Frequency

2009· article· en· W2102663373 on OpenAlexafffund
Hsin-Yun Yao, D. F. Grant, Manuel Cruz

Bibliographic record

VenueIEEE Transactions on Haptics · 2009
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsImmerVision (Canada)Immersion (Canada)McGill UniversityMcGill University Health Centre
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVibrationAccelerationAccelerometerMobile devicePerceptionAdaptation (eye)Function (biology)EngineeringAcousticsComputer scienceSimulationElectronic engineeringPhysics

Abstract

fetched live from OpenAlex

This paper addresses the question of strength perception for vibration signals used in mobile devices. Employing devices similar to standard cell phones and using pulsed vibration signals to combat adaptation effects, experiments were performed to study the effect of weight and underlying on perceived strength. Results shows that for the same measured acceleration on the device, a heavier box is perceived to vibrate with greater strength. Furthermore, signals with higher underlying frequency are perceived to be weaker for the same measured acceleration. While our results are consistent with previous studies, they are obtained for the specific condition of ungrounded, vibrating objects held in the hand. Our results suggest the need for a systematic correction law for use by designers to specify the vibratory characteristics of a device as a function of its weight and of the desired operating frequency.

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)0.016
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.016
GPT teacher head0.265
Teacher spread0.249 · 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

Citations43
Published2009
Admission routes2
Has abstractyes

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