Ground Reaction Forces In Rearfoot And Forefoot Running Assessed By A Continuous Wavelet Transform
Bibliographic record
Abstract
The characteristics of the vertical ground reaction force (GRF) are often used to examine the differences between rearfoot running (RFR) and forefoot running (FFR). Frequency analysis of the vertical GRF and tibial acceleration signals has revealed that FFR generates frequency components representative of the vertical impact peak despite this peak being blunted or absent in the time domain. A disadvantage of traditional frequency analysis (i.e. Fourier transform) is the loss of information regarding the time course of frequency components. PURPOSE: To characterize the frequency characteristics of the GRF generated during the impact phase of RFR and FFR running. METHODS: Twenty RF and 20 FF runners ran over a force platform at 3.5 m/s ±5% performing their habitual footfall pattern. The impact phase of the resultant GRF vector was processed with the continuous wavelet transform using the Mexican Hat as the mother wavelet and scale values 1-200. The impact phase was defined as the percent of stance between initial contact and the instant of zero vertical toe or heel velocity for RFR and FFR, respectively. The frequency range assessed was 8-50 Hz for both footfall patterns. The student’s t-test was used to assess the differences in the sum of the power and maximum power of the wavelet coefficients and the weighted mean pseudo-frequency. RESULTS: On average, the impact phase ended at 21.5±3.3% of stance for RFR and 22.0±5.1% of stance for FFR (P=0.23). The weighted mean pseudo-frequency was 26 Hz for RFR and 22 Hz for FFR (P<0.01). The sum of the power and maximum power of the wavelet coefficients were 32% greater for RFR than FFR (P<0.01). Maximum power occurred at 11.6±1.9% of stance for RFR and 20.1±7.9% of stance for FFR (P<0.01). CONCLUSIONS: The results indicate that the foot-ground collision generates frequencies associated with an impact load, regardless of the portion of the foot that makes initial contact with the ground. The motion of the ankle joint during FFR may delay the time course of maximum signal power of this impact energy by ∼9% compared with RFR. Thus, the vertical impact peak in FFR may be visually obscured in the time domain by the active peak rather than it not occurring at all. Future studies should use caution quantifying the vertical impact peak magnitude and loading rate from the time domain when comparing RFR and FFR.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".