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Sixu Zhou

2 accepted papers

2025

Mode-Unified Intent Estimation of a Robotic Prosthesis Using Deep-Learning

RA-L 2025

Traditional robotic knee-ankle prostheses categorize ambulation modes such as level walking, ramps, and stairs. However, human movement scales continuously across various states rather than discretely, making traditional mode classifiers inadequate for accurate intent recognition. This paper propose

Cited by 4SourceScholar
2025

Transfer Learning for Walking Speed Estimation Across Novel Prosthetic Devices and Populations

IROS 2025

Accurate walking speed estimation in lower-limb prostheses is crucial for delivering biomechanically appropriate assistance across varying speeds. However, training robust models requires extensive domain-specific, user-dependent (DEP) data, which is impractical for every new prosthesis user. This s

Cited by 0SourceScholar