深度神经网络中的自我中心的3D骨架学习编码了类似肥胖的运动表现
Jea Kwon1, Moonsun Sa1, Hyewon Kim1,2
1Center for Cognition and Sociality, Institute for Basic Science (IBS), Daejeon 34126, Korea.
Experimental neurobiology
|July 12, 2024
概括
研究人员使用深度学习和3D运动捕捉来分析鼠标的运动. 一个以自我为中心的认同训练的深度LSTM网络有效地识别了类似肥胖的运动模式,有助于早期发现健康风险.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 深度学习应用程序
背景情况:
- 肥胖是一个重要的全球健康问题,由不良饮食驱动.
- 对于肥胖个体来说,准确的饮食跟踪仍然是一个挑战.
- 对于早期发现肥胖症的3D运动捕捉的潜力尚未得到充分探索.
研究的目的:
- 探索深度学习和3D运动捕捉的使用,以检测与肥胖相关的运动模式.
- 为了评估不同的深度循环网络来分析骨架数据.
- 确定编码肥胖类运动表示的最佳方法.
主要方法:
- 利用在个人身份上训练的深度LSTM网络 (身份训练深度LSTM网络).
- 从饮食诱导的肥胖小鼠模型中分析了3D时间序列骨数据.
- 对比了非中心的和自我中心的观点和各种反复的网络 (RNN,GRU,LSTM).
- 使用带有潜伏特征的支向量分类器来测试运动表示.
主要成果:
- 身份训练深度LSTM网络实现了最佳性能.
- 一个自我中心的观点与认同训练的深层LSTM网络相结合,产生了最好的结果.
- 这些模型有效地编码了类似肥胖的运动表现.
结论:
- 深度学习,特别是具有自我中心观点的身份训练深度LSTM网络,可以在小鼠模型中有效检测与肥胖相关的运动.
- 这种方法为识别健康风险和肥胖早期迹象提供了一种新的方法.
- 这些发现表明人类肥胖检测的潜在应用.
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