Kinect gait skeletal joint feature-based person identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gait not only defines the way a person walks, but also provides interesting cues on individuals daily routine, mental state, health condition or even cognitive function. The importance of incorporating cognitive behavior and analysis in biometric systems has been noted recently. In this article, we develop a biometric-security system using gait-based skeletal information from Microsoft Kinect v1 sensor. The gait cycle is calculated by detecting the three consecutive local minima between the distance of left and right ankle joints. We have utilized the distance feature vector for each of the joints with respect to other joints in the gait cycle for extraction. Mean and variance features are extracted from the distance feature vector. The K Nearest Neighbors (KNN) algorithm is used for classification purpose. The classification accuracy of our proposed approach is 93.33%. The effectiveness of the method is evaluated by comparing it with others existing approaches. Experimental results show that proposed approach is having better recognition accuracy compared to other approaches. Incorporating this biometric in situation awareness system that can identify the mental state of a human is the future direction of this research.
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.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it