Examining the Ability of Two Actigraph Models to Detect and Discriminate Between Low Frequency Movements
Bibliographic record
Abstract
Recently, there has been a notable shift to increasingly more sedentary lifestyles, yet it remains unclear whether inexpensive accelerometers, such as those contained in Actigraph devices, can provide consistent measurements of low magnitude accelerations. This study sought to explore the ability of two Actigraph models to differentiate low frequency oscillations, in reference to higher-end accelerometers with the idle sleep mode disabled (ISM) (Part 1) and enabled (Part 2) in a controlled environment. Eight GT9X, fifteen wGT3X-BT and two higher-end accelerometers (Triaxial ICP) were mounted to a 6-degree of freedom robot, which introduced frequencies ranging from 0.5-2.0 Hz (Part 1) and 0.5-4.0 Hz (Part 2). To compare the models, the minimum, maximum and range of outputs were calculated for each of the frequencies. Part 1 revealed that the Actigraph monitors were able to detect low frequency oscillations; the captured output was similar across the different Actigraph models but was significantly greater than the higher-end devices. Part 2 demonstrated that amplitudes greater than the described 40 m g (0.392 m/s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) threshold were required for the Actigraph monitors to wake up. This study demonstrates that the GT9X and wGT3X-BT Actigraph accelerometers can detect low magnitude movements when the ISM is disabled.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".