Assessing Control of Fixed-Support Balance Recovery in Wearable Lower-Limb Exoskeletons Using Multibody Dynamic Modelling
Bibliographic record
Abstract
Despite many lower-limb exoskeletons requiring the use of crutches to maintain upright postures, limited research has assessed control of standing balance recovery in these systems. Using a model-based approach, the current simulation study investigated the performance of impedance controllers designed to assist with standing fixed-support balance recovery. A novel multibody dynamic model of the integrated humanexoskeleton system was designed to move in the sagittal plane. Development of the exoskeleton model (Technaid Exo-H3) was accompanied by parameter identification. The balancing torques produced by the human in the model were derived from offline linear control methods and saturated to approximate the torque-production of a young individual either with or without incomplete spinal cord injury. Without intervention, the injured user was not able to recover upright posture following a forward push of specific magnitude. Thus, three feedback control laws, inspired by robotics research (exoskeletons and humanoids), were implemented in the simulated exoskeleton. Each law assisted with balance recovery via reference tracking within the joint and/or whole-body center of mass space. Following optimization of control parameters, all proposed exoskeleton control laws were successful in assisting the injured user return to an upright posture. Joint space control yielded the best jointlevel reference tracking during recovery, while center of mass control better reduced forward center of mass excursions - albeit at the cost of joint-level tracking accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".