A Bionic Foot Controlled by a Synergy-Driven Neuromechanical Model Enables Walking at Various Speeds in Socket-Suspended and Bone-Anchored Prosthesis Users
Bibliographic record
Abstract
Human locomotion adapts to different conditions, resulting in changes in gait parameters like speed, stride time, and length. Bionic limbs strive to mimic natural walking patterns, with speed adaptation being a key feature. Research shows that myoelectric bionic legs allow individuals with agonist-antagonist myoneural interface (AMI) amputations to control speed-adaptive walking. However, those with non-AMI amputations show difficulty generating consistent electromyography (EMG) signals. Therefore, we aim to create a human-machine interface that provides speed-adaptive biomimetic behavior without relying on EMGs. Steady-state locomotion can be modeled as the sequential recruitment of muscle groups during the gait cycle. To replicate this motor control, we created a control framework for a bionic foot using a neuromechanical model driven by synthetic muscle activations, replacing EMG recordings. We tested the controller on two individuals with transtibial amputations-one with a socket-suspended prosthesis and the other with a bone-anchored prosthesis. Muscle activation peaks fell within target ranges, leading to peak plantar-flexion torques at 49% of the gait cycle. The averaged model torques aligned with those from inverse dynamics on the intact side (RMSE $=$ 0.52 $ ~\pm ~$ 0.3 (Nm/Kg), r $=$ 0.52 $~\pm ~$ 0.4). The results show that the control system effectively modulates joint torques in timing and amplitude for two subjects across three walking speeds (0.55 to 1.1 m/s). Designed for steady-state walking, it can modulate torque during speed transitions. This first investigation aims to prove the feasibility of a personalized biomimetic control framework for bionic limbs without relying on EMGs, supporting walking at various 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".