Assistive Control of Knee Exoskeletons for Human Walking on Sandy Terrain

Human walkers encounter diverse terrains such as sand and solid ground, which result in distinct gait locomotion and energy costs. This study aims to design and validate a knee stiffness-based model predictive control approach for knee exoskeletons to assist walking on sand. A comparative analysis of human gait, kinematics, and kinetics on sand versus solid ground is conducted. A machine learning-based estimation scheme is developed to predict ground reaction forces in real time. These predictions, combined with human joint torque estimates, are used to design a knee exoskeleton controller that employs a model predictive stiffness control strategy. The experiments demonstrate significant differences in lower limb kinematics and kinetics, as well as a 15% reduction in major muscle activation and a 3.7% (albeit insignificant) reduction in metabolic cost during exoskeleton-assisted walking on sand. These results confirm that the proposed control framework effectively improves walking efficiency on sandy terrain by reducing both muscular effort and metabolic demand.