A Stretchable Strain Sensor System for Wireless Measurement of Musculoskeletal Soft Tissue Strains
Bibliographic record
Abstract
Abstract Measurement of in vivo strain patterns of musculoskeletal soft tissues (MSTs) during functional activities reveals their biomechanical function, supports the identification and understanding of pathologies, and quantifies tissue adaptation during healing. These scientific and clinical insights have motivated the development and application of various strain sensors to quantify MST strains in either intraoperative or dynamic in vivo conditions. In this study, a strain sensor system is developed based on stretchable electronics and radio frequency identification technologies. In this system, a flexible inductor‐capacitor‐resistor sensor is fabricated such that it can be wirelessly excited by a custom‐designed readout box through electronic resonance. The resonant frequency of the sensor changes when the capacitor is stretched, which is then also recorded by the readout box at a sampling rate of 1024 Hz. Suturing the stretchable capacitor onto the MST allows it to be stretched in line with musculoskeletal deformations, hence providing an indirect method to assess strain patterns in vivo. Application of the system ex vivo indicates that the signal remains linear between 0 and 25% strain and is electronically stable in a simulated in vivo environment for one week and over 100 000 cycles of fatigue loadings. The strain sensor exhibits excellent resolution (0.1% strain, ≈9 µm) during wireless strain measurement. Finally, sensor implantation and strain measurement onto the medial gastrocnemius tendon of a sheep indicate that the sensor is able to record repetitive strain patterns in vivo during dynamic movements. This study indicates the potential scientific and clinical applicability in vivo.
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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 | 0.000 |
| 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".