Continuous Volitional Control of a Bionic Leg Supports Diverse Walking Patterns in both AMI and Bone-Anchored Prosthesis User
Bibliographic record
Abstract
Myoelectric control paradigms have the potential to enable continuous volitional control of bionic limbs in various movement conditions. Although individuals with agonist-antagonist muscle interface (AMI) amputation were proven to display a greater degree of continuous volitional control in bionic ankle-foot systems with respect to conventional socket-suspended prosthetic users, it remains unclear how myoelectric interfaces could translate to non-AMI prosthetic users with bone-anchored prostheses (BAP). This study proposes a human-machine interface (HMI) based on a neuromechanical model to enable volitional, continuous control of a bionic leg in AMI and BAP users, walking across various speeds and ground inclinations. Differently from state of the art solutions, the proposed method is based on a digital twin of the intact contralateral leg, synthesizing the user's phantom limb musculoskeletal function as controlled by muscle activations measured from the stump. When embedded in a real-time framework, it enabled the participants to achieve volitional modulation of peak muscle activation timing and amplitude during overground walking at three speeds (between 1.6 and 3.96 km/h). Moreover, case studies are provided during calf-raises (30, 45, and 60 bpm) and ramp ascent walking (3 and 5 \% incline). Before prosthesis control tests, the participants underwent a 2-day gait training session; results showed that all three subjects learned how to alter initial muscle activation patterns so that an average of 87\% of peak activation timing fell within target ranges. The proposed neuromechanical modeling technology opens new avenues toward generalizable HMI for the volitional control of active prostheses beyond set conditions and subjects.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".