Using Machine Learning for Person Identification through Physical Activities
Bibliographic record
Abstract
In this paper, the concept of utilizing machine learning algorithms for person identification through physical activity is proposed. Many previous machine learning research articles focused on building models to identify physical activities using a sensor fusion input. Nevertheless, there has been no focus on building models that can identify the activity performer as well. This paper will demonstrate that machine learning can be applied not only for the identification of physical activities but also for the identification of the activity performer as well. The paper will present the achieved accuracies for the person identification through physical activities using different machine learning algorithms. Additionally, a novel multi-label shared deep neural network (DNN) is proposed for identifying both the physical activity and the activity performer simultaneously. The proposed design allows for a single training/re-training which is advantageous over having to train two separate DNNs. Moreover, it is 30% smaller compared to a design that consists of two separate DNNs for identifying the physical activity and the activity performer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".