A multimedia art interaction model for motion recognition based on data-driven model.

ABSTRACT: Ergonomics is a relatively important part of user experience in multimedia art design. The study aims to improve human-computer interaction efficiency using a data-driven neural network model combined with video data and wearable devices to achieve high-precision human motion recognition. Firstly, the human motion skeleton information is extracted based on video information through the OpenPose framework, and the motion characteristics of each joint of the human body are calculated. Then, use the inertia data of the wearable bracelet to calculate the exercise intensity. At last, the two kinds of information are sent to recurrent neural networks (RNN) together to achieve high-precision human motion recognition. The innovative contribution of the article is to establish a multimodal fusion m

SUBMITTER: Wang Z 

PROVIDER: S-EPMC10702967 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature