Simulating Ideal Assistive Strategies to Reduce the Metabolic Cost of Walking in the Elderly
Bibliographic record
Abstract
OBJECTIVE: Development of walking assist exoskeletons is a growing area of study, offering a solution to restore, maintain, and enhance mobility. However, applying this technology to the elderly is challenging and there is currently no consensus as to the optimal strategy for assisting elderly gait. The gait patterns of elderly individuals often differ from those of the younger population, primarily in the ankle and hip joints. This study used musculoskeletal simulations to predict how ankle and hip actuators might affect the energy expended by elderly participants during gait. METHODS: OpenSim was used to generate simulations of 10 elderly participants walking at self-selected slow, comfortable, and fast speeds. Ideal flexion/extension assistive actuators were added bilaterally to the ankle or hip joints of the models to predict the maximum metabolic power that could be saved by exoskeletons that apply torques at these joints. RESULTS: Compared to the unassisted scenario, the use of ideal hip actuators resulted in 21±5%, 26±5%, and 30±6% reductions in average metabolic power consumption at slow, comfortable, and fast walking speeds, respectively; use of ideal ankle actuators resulted in 12±3%, 14±2%, and 16±1% metabolic savings, respectively. CONCLUSION: The simulations suggest that providing hip assistance to elderly individuals during walking can result in significantly greater metabolic savings than ankle assistance, assuming kinematics and total joint moments do not change substantially with assistance. SIGNIFICANCE: The achieved research results and analysis provide exoskeleton developers guidance on optimally designing walking assist exoskeletons, thus promoting consensus toward the optimal strategy for assisting elderly individuals.
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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 | 0.000 |
| 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".