Pattern Recognition and Artificial Intelligence - Third International Conference, ICPRAI 2022, Paris, France, June 1-3, 2022, Proceedings, Part II
Pattern Recognition and Artificial Intelligence - Third International Conference, ICPRAI 2022, Paris, France, June 1-3, 2022, Proceedings, Part II
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模式识别和人工智能 - 第三届国际会议，ICPRAI 2022，法国巴黎，2022 年 6 月 1-3 日，会议记录，第二部分
DOI：
10.1007/978-3-031-09282-4_10
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Boukhennoufa I
中科院分区：
文献类型：
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作者：
Boukhennoufa I
中风后的准确监测是非常重要的，一种常用的方法是使用可穿戴传感器来收集数据，并应用卷积神经网络（CNN）来处理它。本文在一个复杂的数据集上评估了TS分类的管道，该数据集包括从惯性测量单元传感器收集的18个不同的ADL。该管道涉及通过采用三种编码技术对分割的TS数据进行成像，即：Gramian求和角场（GASF）、Gramian差角场（GADF）和Markov转移场（MKV）。这些编码技术最初是为单变量时间序列设计的，这项工作的一个贡献是提出了一种方法，通过分别对传感器的每个轴进行成像并将它们融合在一起来创建多通道图像，从而使其适应多变量TS。另一个限制来自于这样的事实，即得到的图像大小等于原始TS的序列长度，我们解决了这个问题，采用线性插值的TS序列，以增加或减少it.A比较的性能精度相对于所采用的编码技术和图像大小已经完成。结果表明，GASF和GADF的性能优于MTF编码，除了将图像融合在一起并将图像大小增加到一定限度外，还将准确率从最新结果的87.5%提高到91.5%。
It is of vital importance to accurately monitor post-stroke and one common way is to use wearable sensors to collect data and apply Convolutional Neural Networks (CNNs) to process it. In this paper a pipeline for TS classification is evaluated on a complex dataset which comprises 18 different ADLs collected from inertial measurement units sensors. The pipeline involves imaging the segmented TS data by employing three encoding techniques namely: Gramian Summation Angular Fields (GASF), Gramian Difference Angular Fields (GADF) and Markov Transition Fields (MKV). These encoding techniques were originally designed for univariate time-series, one contribution of this work is to propose a way to adapt it to multivariate TS by imaging each axis of the sensors separately and fusing them together to create multi-channel images. Another limitation comes from the fact that the resulting image size equals the sequence length of the original TS, we tackle this by employing a linear interpolation on the TS sequence to increase or decrease it. A comparison of the performance accuracy with respect to the employed encoding technique and the image size has been done. Results showed that GASF and GADF performed better than MTF encoding, besides fusing the images together and increasing the image size to a certain limit improved the accuracy from 87.5% for the state-of-the-art results to 91.5%.