Kwon Jea, Sa Moonsun, Kim Hyewon, Seong Yejin, Lee C Justin
Center for Cognition and Sociality, Institute for Basic Science (IBS), Daejeon 34126, Korea.
Department of Pre-Medicine, Eulji University School of Medicine, Daejeon 34824, Korea.
Exp Neurobiol. 2024 Jun 30;33(3):119-128. doi: 10.5607/en24008.
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 representations by training a support vector classifier with the latent features from the last layer. Our experimental results indicate that the optimal performance is achieved when utilizing an identity-trained deep LSTM network in conjunction with an egocentric viewpoint. This approach suggests a new way to use deep learning to spot health risks in mouse models of obesity and should be useful for detecting early signs of obesity in humans.
肥胖是一个日益严重的健康问题，主要由不良饮食习惯引起。然而，准确追踪肥胖个体的饮食和食物摄入量具有挑战性。尽管三维运动捕捉技术在医疗保健领域变得越来越重要，但其在检测肥胖早期迹象方面的潜力尚未得到充分探索。在本研究中，我们使用了一个经过个体身份训练的深度长短期记忆网络（identity-trained deep LSTM network）来分析饮食诱导肥胖小鼠模型的三维时间序列骨骼数据。首先，我们从两个不同的视角分析数据：以环境为中心和以自我为中心。其次，我们训练了各种深度循环网络（如RNN、GRU、LSTM）来预测身份。最后，我们通过使用来自最后一层的潜在特征训练支持向量分类器，测试这些模型是否能有效地编码类似肥胖的运动表征。我们的实验结果表明，结合以自我为中心的视角使用经过个体身份训练的深度长短期记忆网络时可实现最佳性能。这种方法为利用深度学习在肥胖小鼠模型中发现健康风险提供了一种新途径，并且应该有助于检测人类肥胖的早期迹象。
