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增强残疾人的室内活动识别,使用多头自我注意反复神经网络,改进算法.

Munya A Arasi1, Hanadi Alkhudhayr2, Abdulwhab Alkharashi3

  • 1Department of Computer Science, Applied College at RijalAlmaa, King Khalid University, Abha, Saudi Arabia. marasi@kku.edu.sa.

Scientific reports
|September 26, 2025
PubMed
概括

本研究介绍了一种使用深度学习的改进为残疾人室内活动识别 (IPOIAR-DPRNN) 的优化. 该方法在检测室内活动时达到97.11%的准确性,以提高安全性和幸福感.

关键词:
适应性的双边过.残疾人残疾人残疾人残疾人室内活动检测检测器佩利坎优化算法的优化算法经常性的神经网络.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 室内活动监测对于老年人和视力受损者等弱势群体的福祉和安全至关重要.
  • 深度学习 (DL) 模型,特别是人类活动识别 (HAR) 技术,为精确的室内监控提供了先进的功能.
  • 目前的DL方法需要仔细选择和优化架构和参数,以有效检测室内活动.

研究的目的:

  • 通过一种新的深度学习方法,增强残疾人室内活动检测系统.
  • 提高在室内环境中识别人类行为的准确性和可靠性.
  • 引入"改善鱼优化用于残疾人室内活动识别" (IPOIAR-DPRNN) 方法.

主要方法:

  • 使用自适应双边过 (ABF) 的图像预处理来减少图像扭曲.
  • 使用EfficientNetB7模型进行特征提取.
  • 通过双向长期短期记忆与多头自我注意 (BiLSTM-MHSA) 进行活动检测和分类.
  • 使用改进的客优化算法 (IPOA) 的BiLSTM-MHSA模型的超参数调整.

主要成果:

  • 在室内活动识别方面,IPOIAR-DPRNN方法表现出卓越的性能.
  • 在佛罗伦萨3D Actions数据集上获得了97.11%的高精度.
  • 在室内环境中检测和分类人类活动方面表现优于现有技术.

结论:

  • 拟议的IPOIAR-DPRNN方法显著提高了残疾人室内活动的认可.
  • 自适应双边过,EfficientNetB7,BiLSTM-MHSA和IPOA的整合提供了一个强大的解决方案.
  • 这种方法为改善室内环境的安全,保安和个性化护理提供了有希望的进步.