Multichannel Wearable Sensor Based on Porous Structure for Simultaneous Acquisition of Different Mechanical Signals
Bibliographic record
Abstract
The intricate mechanical signals inherent in human motion pose significant challenges for the accurate and real-time recognition of postures, necessitating the development of sensors capable of detecting multiple mechanical stimuli concurrently. Herein, a resistance and capacitance multichannel sensor with porous structure is reported. Benefitting from the pores introduced by the prefoaming and steeping process, the resistance channel exhibits heightened sensitivity to strain (gauge factor, GF, of 2.112 for 0–80% strain and 14.227 for 80–150% strain) while remaining nearly insensitive to pressure. Conversely, the capacitance channel demonstrates high sensitivity to pressure (sensitivity, S, of 41.46 Pa –1 for 0–5.5 kPa pressure) and significantly lower sensitivity to strain. The sensor’s superior anti-cross-talk capability allows for the precise differentiation of motion characteristics augmented by machine learning algorithms, enabling the accurate identification of various joint and finger types and states with high recognition rates of 99.89% and 98.56%, respectively. Additionally, a Tai Chi posture recognition system has been developed, leveraging a lightweight hybrid convolutional neural network–long-term memory (CNN-LSTM) model, achieving a remarkable classification accuracy of nearly 99.85% for four distinct continuous Kungfu forms.
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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) | 0.000 | 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 | 0.001 |
| 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".