Development and Verification of a Low-Cost Prosthetic Knee Motion Sensor
Bibliographic record
Abstract
Limb amputation affects many individuals across the world, with the majority of amputations occurring in the lower limb. Healthy individuals with intact limbs have biological sensors embedded in their anatomy to interact with the environment and to facilitate stable walking. Lower limb prosthetic users lose these embedded sensors, leading to decreased balance and an increased risk of falling, abnormal gait, and decreased quality of life. Tactile and kinesthetic sensory feedback techniques are being investigated for upper limb prosthetic users and may soon translate to lower limb users. A barrier to implementing these techniques is the lack of adequate instrumentation of lower limb prostheses. The objective of this research was to design and develop a low-cost wireless system, using inertial measurement units, which can detect when a single axis prosthetic knee is in motion. This sensor could be used to communicate the movement of a prosthetic device to actuators responsible for providing feedback to the user. Our results indicate that the device is capable of tracking the onset and termination of movement at normal walking speeds.
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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 | 0.001 | 0.001 |
| 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".