Motion Detection and Analysis Using Multimaterial Fiber Sensors
Bibliographic record
Abstract
This work presents a system for measuring and analyzing motion, by a portable electronic device and a flexible fiber sensor. The fiber is composed of multi-walled carbon nanotubes (MWCNTs) for its conductivity and polydimethylsiloxane elastomer (PDMS) for its elasticity. A new sensor interface circuit was designed in this study to interface with the fiber and measure its impedance. The measured impedance data are sampled and transmitted via Bluetooth to a laptop. The characteristics of the fiber and a wireless measurement system allow an easy integration into a smart garment to monitor various vital signs and motion markers (e.g angle, step). The system was assessed on a robotic arm before being put in realistic situations through various exercises (flexion/extension knee movements, standing multi-joint movements and walk/run on treadmill) on 5 participants for its ability to measure angle, number of movements, rate and speed. In addition, fibers measurement endurance capacities over months were observed. An assessment of the fiber impedance measurement circuit was performed (minimum resolution of$25~\Omega $, relative error of 2.82% on the estimated value of resistance). Tests carried out over a period of several months show that the fiber maintained good measurement performance when tested on a robotic arm, given an average correlation of 0.85 between angle and fiber impedance. The relative error (RE) made on the number of detected movements was 6.57% in average. In realistic workout situations, these values respectively reached between 0.58 and 73.95% for flexion/extension knee movements. A correlation factor of 0.76 was obtained when the participants were walking on a treadmill at a given speed. Otherwise, RE on number of movements was 8.33% for treadmill exercise, 12.84% on standing exercise. For these exercises and for the movement rate, the average correlation calculated with the reference was between 0.75 and 0.33. Finally, RE on estimated speed was 23.3% in average for the treadmill exercise. The system (sensor interface circuit and Fiber) allows to properly monitor human motion in various activities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".