Wearable Fiber Optic Sensors for Biomechanical Sensing via Joint Angle Detection
Bibliographic record
Abstract
Monitoring human biomechanical movement is necessary for the analysis and development of kinesthetic exercise techniques in physical rehabilitation, professional sports, and performance arts. Optical fiber technology offers an attractive solution to motion capture sensing in terms of size, robustness, signal fidelity, and efficiency. We report on the development of PDMS-based fiber optic strain sensors for biomechanical sensing in real-time via the evaluation of skeletal joint angles. The fibers were fabricated using an elastomer and gel combination in a 3:2 ratio. The elasticity and optical loss of this novel fiber material was experimentally characterized for two fiber diameters of 3 mm and 5 mm. The experimental stress-strain behavior was fitted to a 3D hyperelastic Mooney-Rivlin model to obtain C01 and C10 material constants of 0.022 MPa and 0.0308 MPa respectively. Transmission monotonically decreased in response to a stress applied in both the longitudinal (elongation) and lateral (bending) directions. The sensors were demonstrated in a motion sensing implementation by monitoring the joint angle at the elbow in real-time. Measurements indicated a consistent performance of both fiber diameters over the range of motion of the elbow corresponding to flexion and extension. The optical loss increased by 0.1784 dB and 0.1147 dB for each degree of flexion with standard deviation error in measurement of 3.525° and 4.672° for the 3 mm and 5 mm fiber diameters, respectively. The results demonstrate the potential of this system for real-time, wearable biomechanical sensing, motion capture systems, and as a feedback mechanism in prosthetics and robotics.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".