Pattern transition recognition based on transfer learning for exoskeleton across different terrains.

ABSTRACT: Human motion intention detection is a growing trend in wearable robots. In the study, a novel transfer learning method based on temporal convolutional network spatial attention (TCN-SA) is applied for pattern transition recognition under triple physical loads on different terrains. The proposed approach is used to recognize eight locomotion modes transition among five dynamic locomotion modes in sequence, such as level ground walking, stair ascending, stair descending, ramp ascending, and ramp descending. To address the problem of pattern transition recognition, transfer learning adapts a model from the source domain to the target domain. Temporal convolutional network (TCN) relies on local relationships in time sequence and gains steady gradient propagation. Furthermore, spatial attention

SUBMITTER: Gao Y 

PROVIDER: S-EPMC12453787 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature