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Record W2414749422 · doi:10.1162/pres_a_00245

Force Discrimination Ability of the Human Hand near Absolute Threshold for the Design of Force Feedback Systems in Teleoperations

2016· article· en· W2414749422 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePRESENCE Virtual and Augmented Reality · 2016
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsTeleoperationHaptic technologyPerceptionJust-noticeable differenceAbsolute thresholdForce transducerStimulus (psychology)Computer scienceSimulationArtificial intelligenceAcousticsPsychologyRobotCognitive psychologyPhysics

Abstract

fetched live from OpenAlex

Force discrimination ability is an important surgical skill for micro or minimally invasive surgeries. This article analyzes the force perception of the human hand for lowintensity stimuli. Psychophysical experiments were conducted to measure just noticeable differences (JNDs) at four reference forces below 1 N. Reference forces are chosen to be in the range of forces in minimally invasive surgery that do not follow Weber's law. The force discrimination ability of two groups of subjects, surgeons and non-surgeons, is compared. Results demonstrate a superior ability of surgeons in discriminating small forces. The relationship between the JND and stimulus near absolute threshold is modeled. The application of the model in the design of force feedback systems for surgical teleoperation is discussed. In a force augmenting teleoperation system, the force feedback signal is amplified based on the characteristics of the human perception system. Therefore, the degraded human sense of touch at low-intensity forces would be compensated.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.121
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.089
GPT teacher head0.317
Teacher spread0.228 · 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