iP3T: an interpretable multimodal time-series model for enhanced gait phase prediction in wearable exoskeletons

iP3T: an interpretable multimodal time-series model for enhanced gait phase prediction in wearable exoskeletons

IntroductionWearable exoskeletons assist individuals with mobility impairments, enhancing their gait and quality of life. This study presents the iP3T model, designed to optimize gait phase prediction through the fusion of multimodal time-series data.MethodsThe iP3T model integrates data from stretc...

Full description

Bibliographic Details
Main Authors: Hui Chen, Xiangyang Wang, Yang Xiao, Beixian Wu, Zhuo Wang, Yao Liu, Peiyi Wang, Chunjie Chen, Xinyu Wu
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-09-01
Series:Frontiers in Neuroscience
Subjects:
multimodal data fusion
gait phase prediction
wearable exoskeleton
transformer
IMU
sEMG
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2024.1457623/full

Internet

https://www.frontiersin.org/articles/10.3389/fnins.2024.1457623/full

Similar Items