Egocentric 3D Skeleton Learning in a Deep Neural Network Encodes Obese-like Motion Representations.

ABSTRACT: Obesity is a growing health concern, mainly caused by poor dietary habits. Yet, accurately tracking the diet and food intake of individuals with obesity is challenging. Although 3D motion capture technology is becoming increasingly important in healthcare, its potential for detecting early signs of obesity has not been fully explored. In this research, we used a deep LSTM network trained with individual identity (identity-trained deep LSTM network) to analyze 3D time-series skeleton data from mouse models with diet-induced obesity. First, we analyzed the data from two different viewpoints: allocentric and egocentric. Second, we trained various deep recurrent networks (e.g., RNN, GRU, LSTM) to predict the identity. Lastly, we tested whether these models effectively encode obese-like motion

SUBMITTER: Kwon J 

PROVIDER: S-EPMC11247279 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature