Personalized Lower Limb Gait Reconstruction Modeling Based on RFA-ProMP

Le résumé fourni par la source

Personalized gait curves are generated to enhance patient adaptability to gait trajectories used for passive training in the early stage of rehabilitation for hemiplegic patients. The article utilizes the random forest algorithm to construct a gait parameter model, which maps the relationship between parameters such as height, weight, age, gender, and gait speed, achieving prediction of key points on the gait curve. To enhance prediction accuracy, an attention mechanism is introduced into the algorithm to focus more on the main features. Meanwhile, to ensure high similarity between the reconstructed gait curve and the normal one, probabilistic motion primitives (ProMP) are used to learn the probability distribution of normal gait data and construct a gait trajectory model. Finally, using the specified step speed as input, select a reference gait trajectory from the learned trajectory, and reconstruct the curve of the reference trajectory using the gait key points predicted by the parameter model to obtain the final curve. Simulation results demonstrate that the method proposed in this paper achieves 98% and 96% curve correlations when generating personalized lower limb gait curves for different patients, respectively, indicating its suitability for such tasks.

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