Comparison of Short Fast Fourier Transform and Continuous Wavelet Transform in Study of Stride Interval
Bibliographic record
Abstract
Neurodegenerative diseases (NDD) are a heterogeneous group of complex diseases characterized by neuronal loss and progressive degeneration of different areas of the nervous system.Gait analysis presents an early recognition system for NDD which is important to increase the patient's awareness of their health conditions.However, it is very difficult to identify and formulate suitable digital biomarkers from the data collected from gait experiments such as stride interval and swing.The objective of this paper is to compare the result of Short -Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) on the collected stride interval of healthy young people and healthy old people.In this paper, STFT and CWT are performed on the collected stride interval and from the result of the STFT and CWT, further features are extracted like instantaneous RMS and maximum RMS value.STFT is performed on the collected stride interval from a window length of 64 to 512 while CWT is performed on the collected stride interval from the scale of 128 to 2048.The processing time of the STFT and CWT with varied window lengths and scales respectively are collected.Besides, the actual maximum time from the time -frequency plot derived from STFT and CWT is also collected.Both STFT and CWT show that the young group has a higher maximum RMS, an indication of higher stride interval than the old group and higher variance, an indication of higher gait complexity.The suitable window lengths for STFT in analyzing the stride interval are 64 and 128 while the scale for CWT should be set to the lowest scale.Overall, STFT with a window length of 64 and 128 is better in analyzing the stride interval due to low processing time at the expense of slightly less accurate time -frequency representation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".