Technical Reliability Assessment of Three Accelerometer Models in a Mechanical Setup
Bibliographic record
Abstract
PURPOSE: To determine which of the three most commonly used accelerometer models has the best intra- and interinstrument reliability using a mechanical laboratory setup. Secondly, to determine the effects that acceleration and frequency have on these reliability measures. METHODS: Three experiments were performed. In the first, five each of the Actical, Actigraph, and RT3 accelerometers were placed on a hydraulic shaker plate and simultaneously accelerated in the vertical plane at varying accelerations and frequencies. Six different conditions of varying intensity were used to produce a range of accelerometer counts. Reliability was calculated using standard deviation, standard error of the measurement, coefficient of variation, and intraclass correlation coefficients. In the second and third experiments, 39 Actical and 50 Actigraph accelerometers were put through the same six conditions. RESULTS: Experiment 1 showed poor reliability in the RT3 (intra- and interinstrument CV > 40%). Experiments 2 and 3 clearly indicated that the Actical (CVintra = 0.5%, CVinter = 5.4%) was more reliable than the Actigraph (CVintra = 3.2%, CVinter = 8.6%). Variability in the Actical was negatively related to the acceleration of the condition, whereas no relationship was found between acceleration and reliability in the Actigraph. Variability in the Actigraph was negatively related to the frequency of the condition, whereas no relationship was found between frequency and reliability in the Actical. CONCLUSION: Of the three accelerometer models measured in this study, the Actical had the best intra- and interinstrument reliability. However, discrepant trends in the variability of Actical and Actigraph counts across accelerations and frequencies preclude the selection of a superior model. More work is needed to understand why accelerometers designed to measure the same thing behave so differently.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".