Analyzing small amplitude of floor vibrations is a new promising means for identifying the types of human activities, e.g., walking around and accidental falls. In this paper, we consider the binary classification problem of floor vibrations for the applications like fall detection. For practical use, there are two main issues of the problem. First, the prediction of the classifier should be fast. Second, the training set is sometimes small and the diversity of negative samples brings extra challenges when the training samples are insufficient. The state-of-the-art methods for time series classification, such as HIVE-COTE and ResNet, are computationally intensive or susceptible to the size of the training set. Therefore, we propose a new feature extraction method based on dynamic mode decomposition (DMD) and high-frequency characteristics, whose time complexity is linear with the size of training set and quadratic with the length of time series. The method is evaluated on the dataset of floor vibrations proposed by Madarshahian et al. (2016). The results show higher accuracy compared to the ResNet classifier and time series forests, especially when the negative training samples are deficient in type.

分析地板振动的小幅度幅度是一种新的有前途的方法，可用于识别人类活动的类型，例如到处走动和意外跌倒。在本文中，我们考虑将地板振动的二进制分类问题用于跌倒检测等应用。对于实际使用，存在两个主要问题。首先，分类器的预测应该很快。其次，训练集有时很小，当训练样本不足时，负面样本的多样性带来了额外的挑战。时间序列分类的最新方法（例如HIVE-COTE和ResNet）的计算量很大，或者容易受训练集大小的影响。因此，我们提出了一种基于动态模式分解（DMD）和高频特性的新特征提取方法，其时间复杂度与训练集的大小呈线性关系，与时间序列的长度呈二次关系。该方法在Madarshahian等人提出的地板振动数据集上进行了评估。（2016）。与ResNet分类器和时间序列森林相比，结果显示出更高的准确性，尤其是当负训练样本类型不足时。