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Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
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功能学习网络用于基于地板传感器的步态识别.

Ala Salehi, Alex Roberts, Angkoon Phinyomark

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括

    功能学习网络显著提高了人身份验证准确性,使用有限的数据进行步态识别. 这些深度学习方法的表现优于传统方法,PCANet表现最好.

    科学领域:

    • 计算机科学 计算机科学
    • 生物识别信息 生物识别信息
    • 模式识别 模式识别

    背景情况:

    • 深度学习 (DL) 模型通常需要大型数据集,以实现图像分类的高准确性.
    • 在许多现实应用中,有限的数据可用性对DL模型性能构成重大挑战.
    • 传统的特征提取方法可能无法充分利用深度学习框架的功能.

    研究的目的:

    • 评估特征学习网络的有效性,与基于压力的传统方法相比,基于有限的数据进行基于压力的脚步识别.
    • 调查各种传统特征提取技术及其相应的DL网络对应的性能.
    • 确定在低数据场景中用于人身份验证的最有效的功能学习网络.

    主要方法:

    • 与传统方法进行比较:离散波纹变换 (DWT),离散等号变换 (DCT),独立组件分析 (ICA) 和主要组件分析 (PCA).
    • 评估的DL具有学习网络:ScatNet,DCTNet,ICANet和PCANet,基于一个卷积神经网络 (CNN) 框架.
    • 使用有限的样本大小对基于压力的脚步识别进行人身份认证的评估性能.

    主要成果:

    • 特性学习网络的平均准确率 (90.6%) 比传统方法 (79.7%) (p < 0.05) 高得多.
    • 在所有测试的特征网络中,PCANet表现出最好的验证性能,达到92.2%的准确性.

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  • 该研究证实了综合特征学习网络在独立的传统技术上的优越性能.

    • 结论:

    • 功能学习网络在训练数据有限的场景中,为步态识别和人身份验证提供了一个有希望和有效的解决方案.
    • 这些方法特别适用于安全访问应用程序,如工作空间环境和边境控制.
    • 传统特征提取与DL框架的整合提高了模型的稳定性和准确性,当数据稀缺时.