Haptic-Based Biometrics: A Feasibility Study
Bibliographic record
Abstract
Biometric systems identify users based on behavioral or physiological characteristics. The advantages of such systems over traditional authentication methods such as passwords are well known and hence biometric systems are gradually gaining ground in terms of usage. This paper explores the feasibility of automatically and continuously identifying participants in Haptic systems. Such a biometric system could be used for authentication in any Haptic based application, such as tele-operation or tele-training, not only at the beginning of the session, but continuously and throughout the session as it progresses. In order to test this possibility, we designed a Haptic system in which position, velocity, force and torque data from the tool was continuously measured and stored as users were performing a specific task. Subsequently, several algorithms and methods were developed to extract biometric features from the measured data. Overall, the results suggest reasonable practicality of implementing haptic-based biometric systems, and that it is an avenue worth pursuing; although they also indicate that it might be quite difficult to develop a highly accurate Haptic ID algorithm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".