A Wireless Wristband Accelerometer for Monitoring of Rubber Band Exercises
Bibliographic record
Abstract
Here, we report a highly sensitive capacitive printed accelerometer and its wearable wristband module for wireless activity monitoring application during rubber band exercise. The printed accelerometer is modified to possess a high sensitivity of 0.54 pF/g, which shows promising reliability for human's exercise monitoring. The circuit of the wristband exercise module is integrated into the customized printed circuit board so that the module receives and digitizes analog capacitance data from the accelerometer and wirelessly transmits the data to the receiver module on the user interface. Different force levels of exercises were successfully quantified with a distinctive capacitance variance from 0.031 pF under light exercise condition (16.04 N) to 0.266 pF under strong exercise (23.04 N). In addition, three different representative exercise postures were performed with the wristband module: 1) shoulder flexion; 2) horizontal arm abduction; and 3) knee extension. By sampling exercise data with 10 Hz, the module cannot only read the movements of the different exercise postures successfully, but also provide reliable collection of data that can be used to differentiate the exercise characteristics in each posture. The reported wristband exercise-monitoring module utilizes highly sensitive accelerometer and wirelessly communicating unit to provide point-of-care and high level of freedom to users during the rubber band exercises further suggesting feasibility of its potential usages in the other activity monitoring applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".