Predicting vertical and shear ground reaction forces during walking and jogging using wearable plantar pressure insoles
Bibliographic record
Abstract
ABSTRACT Background The development of plantar pressure insoles has made them a potential replacement for force plates. These wearable devices can measure multiple steps and might be used outside of the lab environment for rehabilitation and evaluation of sport performance. However, they can only measure the normal force which does not completely represent the vertical ground reaction force (GRF). In addition, they are not able to measure shear forces which play an import role in the dynamic performance of individuals. Indirect approaches might be implemented to improve the accuracy of the force estimated by plantar pressure systems. Research question The aim of this study was to predict the vertical and shear components of ground reaction force from plantar pressure data using recurrent neural networks. Methods GRF and plantar pressure data were collected from sixteen healthy individuals during 10 trials of walking and five trials of jogging using Bertec force plates and FScan plantar pressure insoles. A long short-term memory (LSTM) neural network was built to consider the time dependency of pressure and force data in predictions. The data were split into three subsets of train, to train the LSTM model, evaluate, to optimize the model hyperparameters, and test sets, to assess the accuracy of the model predictions. Results The results of this study showed that our LSTM model could accurately predict the shear and vertical GRF components during walking and jogging. The predictions were more accurate during walking compared to jogging. In addition, the predictions of mediolateral force had higher error and lower correlation compared to vertical and anteroposterior components. Significance The LSTM model developed in this study may be an acceptable option for accurate estimation of GRF during outdoor activities which can have significant impacts in rehabilitation, sport performance, and gaming.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 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".