Piezoresistive Sensors Array for Multijoint Motion Estimation Application
Bibliographic record
Abstract
With the emergence of digital healthcare, comes the need for an unobtrusive method for long-term motion monitoring. In recent years, wearable sensors have been utilized for motion monitoring to replace the conventional camera-based systems. Despite several attempts at measuring joint angles, designs for affordable and low power-consuming systems were lacking. This article explored the usage of ten low-cost, energy-efficient conductive polymer composite-based strain sensors composed of thermoplastic polyurethane elastomer matrix and multiwalled carbon nanotube (CNT) to create a smart clothing system for the measurement of elbow and shoulder joint angles. To overcome the time-varying and nonlinear behavior of the proposed strain sensor, a novel architecture of a convolutional neural network was designed to enhance the mapping of sensor signals to joint angles by extracting inter-sensor spatial and temporal information. Strain sensors with different concentrations were fabricated and characterized. It was found that 4 wt% CNT produced the highest sensitivity due to the highest degree of macrostructural damage. Motion monitoring performance was evaluated on one volunteer performing different actions and overall normalized root mean squared errors for elbow angle and shoulder Euler angles were 6.77%, 7.19%, 6.31%, and 8.22%, respectively.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".