Postural Sway Classification using Bispectrum
Bibliographic record
Abstract
To reduce mortality and morbidity rates among elderly individuals, continuous monitoring of their postural sway is necessary. This monitoring is implemented through the use of accelerometer sensors, which provide time series signals. The bispectrum, an example of high order spectral (HOS) analysis, is employed to analyze these time series, leveraging the relationship between the various spectral components of the sensor signal for postural sway classifications. From the bispectrum magnitude, features including mean, standard deviation, and entropy are extracted and utilized to train various traditional classifiers. Abstract features are also generated using pre-trained models (MobileNet, Inception, DenseNet, and ResNet) trained with bispectrum magnitude as input and fine-tuned last five layers and the fully connected layers. The classification performance obtained using traditional features and abstract features are presented and compared with two state-of-the-art methods. In addition, the superior performance of the proposed bispectrum is demonstrated by comparing the results of using abstract features from bispectrum magnitude against the abstract features from the traditionally used spectrogram magnitude. The impact of measurement noise on the accelerometer signals on stability classification is also assessed. Under noisy conditions, abstract features extracted from bispectrum magnitude yield the best results compared to state-of-the-art methods and abstract features from spectrogram magnitude. These findings underscore the efficiency of utilizing features derived from the bispectrum for accurate postural sway classifications, even in the presence of significant additive measurement noise.
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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 | 0.000 |
| 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 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".