Jiajie Shen1, Yan Wang1,*, Dongxu Zhang2
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.047903
Abstract Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable devices. Previous work have achieved impressive performance in classifying steady locomotion states. However, it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion states. Due to the similarities between the information of the transitions and their adjacent steady states. Furthermore, most of these methods rely solely on data and overlook the objective laws between physical activities, resulting in lower accuracy, particularly when encountering complex locomotion modes such as transitions.… More >
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