This study introduces a novel continual learning algorithm that incrementally improves the performance of deep-learning-based walking speed estimators during level-ground walking with a powered knee-ankle prosthesis. While user-dependent (DEP) estimators generally outperform user-independent (IND) estimators, they require the pre-collection of DEP training data. In contrast, our real-time algorith...Show More
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 letter proposes a mode-unified intent recognition strategy that continuously estimates terrain slopes across five...Show More
Recently, extreme heat waves have swept the world, exacerbating the pressing issue of increased drought occurrences. Accurate water level prediction is crucial in combating droughts. However, current water level prediction models lack interchangeability between different bodies of water and ultimately fail to consider the relevance between different hydrological systems, rendering them inaccurate ...Show More
