A Cost-Effective Inertial Measurement System for Tracking Movement and Triggering Kinesthetic Feedback in Lower-Limb Prosthesis Users
Bibliographic record
Abstract
Advances in lower-limb prosthetic technologies have facilitated the restoration of ambulation; however, users of such technologies still experience reduced balance control, also due to the absence of proprioceptive feedback. Recent efforts have demonstrated the ability to restore kinesthetic feedback in upper-limb prosthesis applications; however, technical solutions to trigger the required muscle vibration and provide automated feedback have not been explored for lower-limb prostheses. The study's first objective was therefore to develop a feedback system capable of tracking lower-limb movement and automatically triggering a muscle vibrator to induce the kinesthetic illusion. The second objective was to investigate the developed system's ability to provide kinesthetic feedback in a case participant. A low-cost, wireless feedback system, incorporating two inertial measurement units to trigger a muscle vibrator, was developed and tested in an individual with limb loss above the knee. Our system had a maximum communication delay of 50 ms and showed good tracking of Gaussian and sinusoidal movement profiles for velocities below 180 degrees per second (error < 8 degrees), mimicking stepping and walking, respectively. We demonstrated in the case participant that the developed feedback system can successfully elicit the kinesthetic illusion. Our work contributes to the integration of sensory feedback in lower-limb prostheses, to increase their use and functionality.
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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 | 0.001 |
| 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 | 0.001 |
| 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".