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通过增强视听合奏学习与决策支持方法进行步行识别
Ruixiang Kan1, Mei Wang2, Tian Luo1
1School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
这项研究引入了一种新的双Kinect V2系统,用于通过骨架关节数据和声学信号增强步态识别. 该系统通过集体学习和Dempster-Shafer证据理论提高了复杂场景的准确性.
科学领域:
- 生物识别和人机交互的人机交互
- 信号处理和机器学习
背景情况:
- 步行是一种有价值的生物识别,但当前的识别方法在复杂的环境中扎.
- 现有系统需要改进,以获得强大的性能和更广泛的应用.
研究的目的:
- 开发使用双Kinect V2的先进步态识别系统,专注于骨架关节和声学数据.
- 在具有挑战性的场景中提高识别准确性和可靠性.
主要方法:
- 使用双Kinect V2系统捕获步行骨架关节数据和声学信号.
- 使用Dempster-Shafer证据理论 (D-SET) 实现增强组合学习以提供决策支持.
- 开发了改进的AdaBoost方法,包括循环混乱映射,格拉米安角场 (GAF) 和并行卷积神经网络 (PCNN).
主要成果:
- 通过使用AdaBoost,Circle Chaotic Mapping和GAF实现了改善的步行骨关节识别.
- 通过AdaBoost,GAF和PCNN证明了对数据进行适应的声信号识别.
- 集成三角拓聚合优化器 (TTAO) 与D-SET提供了强大的决策支持机制,提高了整体准确性.
结论:
- 拟议的双Kinect V2系统与集体学习和D-SET显著提高了步态识别的准确性.
- 处理骨和声学数据的新方法在复杂的生物识别场景中显示出相当大的应用价值.
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