Accuracy and precision of wearable inertia sensor during a free-weight back squat.
Bibliographic record
Abstract
INTRODUCTION:Previous research investigating the validity and reliability of the PUSH™ (PUSH Inc., Toronto, Canada) wearable inertia sensor during a back squat exercise has produced conflicting results (Balsalobre-Fernández et al., 2016; Banyard et al., 2017). Therefore, the aim of this study was two-fold a) examine the error in all variables reported by the PUSH™ in comparison to a criterion method of two Kistler force platforms and 3D motion capture system during the free-weight back squat b) briefly discuss the suitability of the PUSH™ as a training monitoring tool in highly trained participants.METHODS:Seven Scottish Rugby Union academy players (age 18.8±1.2 years; height 1.84±0.08 m; body mass 96.9±11.7 kg) were recruited. Participants performed a total of 134 free-weight back squat repetitions with a mean weight of 117.5 kg (±14.72) as part of their regular training sessions. All repetitions were simultaneously captured using the PUSH™ and two Kistler force platforms (Kistler Holding AG, Switzerland) synced with a 12 Oqus 300+ camera motion capture system (Qualisys, Sweden) sampling at 500Hz. PUSH™, Qualisys and Kistler data were then imported into Matlab (R2014a, The Mathworks Inc., Natick, MA, USA) for the analysis of six variables: mean and peak concentric velocity (MV and PV), mean and peak concentric vertical ground reaction force (MF and PF), and mean and peak concentric power (MP and PP). The accuracy and precision of the PUSH™ system were assessed in two ways. Bland-Altman plots were created for each variable for visual inspection. Then MANOVA and subsequent univariate tests were used to assess the difference between the systems. If a significant difference existed, then bias-corrected root mean square error (RMSE) was reported for that variable.RESULTS:From the MANOVA there was a significant difference between the systems, F(6,127) = 904.9, p
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".