Development and design specifications for an accelerometer-based biomechanical, prosthetic sensorimotor platform
Bibliographic record
Abstract
Abstract Following stroke, injury, or exposure to physically limiting conditions, limbs can become physiologically compromised. In particular, motor and fine-dexterity tasks involving the arm, particularly in locomotion, grasp and release, can be influenced becoming either delayed and having to deal with greater force demands. Current prosthetic systems use electromyography (EMG)-based techniques for creating functional sensorimotor platforms. However, several limitations in practical use and signal detection have been identified in these systems. Accelerometer-based sensorimotor systems have been suggested to overcome these limitations but only proof-of-concept has been demonstrated. Here, we explore design specifications for accelerometers being developed for prosthetic integration. We have developed optimizations for the current model, evaluated system properties to enhance sensitivity and reduce signal noise, and performed a pilot test using simulation to test this model. The data suggest these novel design parameters can enhance signal detection, when compared to conventional accelerometers. Future avenues should focus on validation of this design prototype in a full prosthetic system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".