Predictive simulations of elevating and lowering strategies in human stumble recovery

Purpose: Older adults and individuals with neuromuscular impairments face a high risk of falls, which can be mitigated by identifying effective stumble recovery strategies for rehabilitation. Studying stumble recovery through empirical methods is challenging due to injury risks and constraints on natural movement, whereas predictive neuromechanical simulations offer a viable alternative. This study aimed to use a musculoskeletal model within a predictive simulation framework to analyze human stumble recovery following anteriorly directed perturbations.Methods: Using a simplified musculoskeletal model and a reflex-based neural controller, two different scenarios for perturbations occurring in the early (20%) and late (60%) swing phases were simulated. The kinematics of the swing leg, including hip, knee, and ankle joint angles were analyzed for similarity to real human stumble recovery. Additionally, recovery strategies were identified by tracking the swing leg’s toe trajectory following perturbation.Results: Early swing perturbations elicited an elevating strategy, increasing hip and knee flexion to clear the obstacle, while late swing perturbations triggered a lowering strategy, rapidly placing the foot to restore stability. Minor deviations from experimental data were observed, particularly in ankle dorsiflexion and swing phase duration.Conclusion: This study highlights the effectiveness of predictive neuromechanical simulations in analyzing stumble recovery. The framework successfully replicated key recovery mechanisms, demonstrating its potential for rehabilitation, assistive device design, and fall prevention strategies aimed at enhancing mobility and reducing injury risk in vulnerable populations.