Individual Recognition Based on Gait Using Multi-Distance Signal Level Difference Sample Entropy
Bibliographic record
Abstract
The way a person walks has unique characteristics for each individual and can be used to recognize them.There are various ways to classify characteristics for gait of each individual, one of them is the inertia sensor.The inertia sensor is used to collect data gait signals, which are angular velocity variations caused by human walking movements.Multidistance Signal Level Difference Sample Entropy is proposed in this study as a feature extraction before classifying individual gaits.MSLD is used to measure the co-occurrence of two signal samples at a distance d, and SampEn quantizes signal complexity.The MSLD Entropy produce 60 features in the form of SampEn at distances of 1 until 20 from the threeaxis.The testing procedure is carried out on the MSLD Entropy result signal for each classifier with a feature in the form of SampEn at distances of d=1-20, d=1-15, d=1-10, and d=1-5.Softmax regression as a classifier and feature at distance 1 until 20, the test results produce the greatest accuracy of 98.3%.Because a person's gait can be identified not just from one but three directions, using only one axis results in lesser accuracy than using data from all three axes.
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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 | 0.001 | 0.001 |
| 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".