Recognizing and quantifying human movement patterns through haptic-based applications
Bibliographic record
Abstract
Biometrics has been introduced recently to identify people by their behavior and physiological features. It offers a wide application scope to detect fraud attempts in organizations, corporations, educational institutions, electronic resources and even crime scenes. The field of biometrics can be divided into two main classes according to features that humans are born with, such as fingerprints or facial features, or behavioral characteristics of humans, like a handwritten signature or voice (J. Ortega-Garcia et al., 2004). The work presented in this paper pursues the latter class, specifically how a person reacts to using daily devices or tools. The fact that we can exploit people's habits in handling devices to identity individuals was the hypothesis that motivated this work. Among the many examples of the potential use of this class of biometrics is the particular force applied to the keys in a keyboard. There is also the time interval between each keypad when dialing a telephone number. Another example that can be extracted from the latter would be the map described by the fingers in navigating through solving maze operation. Extracting these features by using a haptic-based application and defining the subsequent individual pattern is the objective of this research. A framework that identifies behavioral patterns through physical parameters such as direction, force, pressure and velocity has been built. The set up for the experimental work consisted of a multisensory tool, using the Reachin system (Reachin Technologies, User's Programmers Guide and API).
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".