Subject Identification Using Behavioral Cues and Machine Learning
Bibliographic record
Abstract
In recent years, significant advances in biometrics have essentially been driven by machine learning (ML) and deep learning (DL) progress. Numerous human identification applications are currently available using physical traits such as fingerprints, face, and voice. With the development of Internet of Things (IoT) sensors and the availability of a variety of ML algorithms, there has been increased research interest in subject identification (SI) based on behavioral cues. For example, several research works have been published on SI based on gait analysis. Sensors like accelerometers (ACC), gyroscopes, (GYR), and magnetometers (MAG) were used to collect data during limited activities such as walking. We believe that using data for one activity is not sufficient to adequately capture behavioral cues for the purpose of SI. Considering other cues such as gestures or head shaking and using a variety of sensors located on different parts of the human body are essential to developing a scenario that includes expressive human activities. We designed a specific scenario that included several activities, such as walking, giving a talk, chatting while sitting, and climbing stairs, using five inertial measurement units (IMU) located on various parts of the human body. Several ML algorithms, namely Linear Discriminant Analysis (LDA), K-Nearest Neighbours (KNN), Random Forest (RF), and XGBoost (XGB) were used. Our results show that SI yields beyond 99% accuracy for most activities. Furthermore, we succeeded in implementing a real-time IoT system for SI based on our best offline results. We achieved 98.04% accuracy within 0.06 ms of processing time (PT).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
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".