Depth-aware pose estimation using deep learning for exoskeleton gait analysis.

ABSTRACT: In rehabilitation medicine, real-time analysis of the gait for human wearing lower-limb exoskeleton rehabilitation robot during walking can effectively prevent patients from experiencing excessive and asymmetric gait during rehabilitation training, thereby avoiding falls or even secondary injuries. To address the above situation, we propose a gait detection method based on computer vision for the real-time monitoring of gait during human-machine integrated walking. Specifically, we design a neural network model called GaitPoseNet, which is used for posture recognition in human-machine integrated walking. Using RGB images as input and depth features as output, regression of joint coordinates through depth estimation of implicit supervised networks. In addition, joint guidance strategy (JGS)

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10730887 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature