Chunhong Zeng, Kang Lu, Zhiqin He*, Qinmu Wu
CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1441-1456, 2024, DOI:10.32604/cmc.2024.051551
Abstract 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, More >
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