Estimation of Knee Joint Angle Using a Fabric-Based Strain Sensor and Machine Learning: A Preliminary Investigation
Bibliographic record
Abstract
Monitoring human knee kinematics has various health applications including in-home rehabilitation and longterm tracking of movements of people with knee disorders. We proposed a wearable system based on a stretchable strain sensor and investigated its feasibility to estimate the knee joint angle in tasks of walking and static knee flexion. A pilot study with six subjects was conducted in which participants were asked to walk and perform flexion exercises at multiple speeds. Two commonly used machine learning algorithms (neural network and random forest) were utilized to estimate the knee joint angle based on the strain sensor data. The performance of the proposed approach was assessed in an intra- and inter-subject evaluation. In the intra-subject evaluation., the average mean absolute error (MAE) in estimating the knee joint angle during the walking task and flexion exercises was 1.94 and 3.02 degrees., respectively., with a similar coefficient of determination R2of 0.97. In the inter-subject evaluation., an average MAE of 4.14 degrees in the walking task and 6.97 degrees in the knee flexion exercises was achieved with a R2of 0.90. Our results suggest the feasibility of our approach., which includes a fabric-based strain sensor and machine learning., to estimate the knee joint angle. In future, this method might be used in various applications including the fields of healthcare., virtual reality 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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".