Detecting Tap Events from a Foot-Tapping Test with an Accelerometer: Validation of Teager-Kaiser Energy Operator and Discrete Wavelet Transform
Bibliographic record
Abstract
Foot-tapping is used to evaluate functional and cognitive ability of people with and without neurological disorders. Accurate tap event detection during this test is key. This study validated the accuracy of two algorithms: Ⅰ) Teager-Kaiser energy operator in conjunction with the Maximal Overlap Discrete Wavelet Transform (TKEO-MODWT) and II) Discrete Wavelet Transform (DWT), for detecting tap events from accelerometer data during a unilateral foot-tapping test. These algorithms have been validated for tap detection during finger-tapping tests in healthy younger adults and people with Parkinson’s disease. However, their accuracy for detecting taps during a foot-tapping test has not been explored. Thirty-three participants (15 older adults aged 65 and above, and 18 young-midlife adults 64 years old and under) performed a dominant and nondominant unilateral foot-tapping test. Data were recorded using an accelerometer and a force plate. The sensitivity, precision, mean absolute error in tap detection, and timing detected by the two algorithms from the accelerometer data were compared against true taps recorded using a force plate. Both algorithms (TKEO-MODWT and DWT) were robust for detecting foot-tapping taps. Algorithm Ⅰ (TKEO-MODWT) consistently outperformed algorithm Ⅱ (DWT), achieving higher sensitivity and precision. Additionally, algorithm Ⅰ (TKEO- MODWT) resulted in lower mean absolute error between detected taps and the true taps for both feet in both groups. TKEO-MODWT provides an accurate tool for objectively assessing unilateral foot-tapping in young-midlife adults and older adults using only an accelerometer, and could add to the clinical assessment of functional and cognitive assessment of older adults.
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.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".