Reinforcement Learning Identifies Age-Related Balance Strategy Shifts
Bibliographic record
Abstract
Falls are one of the leading causes of non-disease death and injury in the elderly, partly due to the loss of muscle mass in a musculoskeletal disorder named sarcopenia. Studying the impact of this muscle weakness on standing balance through direct human experimentation poses ethical dilemmas, involves high costs, and fails to fully capture the internal dynamics of the muscle. To address these limitations, we employ neuromusculoskeletal modeling to explore the impact of sarcopenia on balance. In this study, we introduce a novel full-body MSK model comprising both the torso and lower limbs, with 290 muscle actuators controlling 23 degrees of freedom and supporting varying levels of sarcopenia. Using reinforcement learning coupled with curriculum learning and muscle synergy representations, we trained an agent to perform standing balance on a backward-sliding plate and compared its behavior to human experiments. Our results demonstrate that, without pre-recorded experimental data, both healthy and sarcopenic agents can reproduce ankle and hip balancing strategies consistent with experimental findings. Furthermore, we show that as the degree of sarcopenia increases, the agent adapts its balancing strategy based on the platform's acceleration. The full code is open-sourced and can be found in this repository: https://github.com/cherylwang20/StandingBalance.
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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 | 0.003 |
| 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".