Abstract

This paper analyses the periodic signals in the time series to recognize human activity by using a mobile accelerometer. Each point in the timeline corresponds to a segment of historical time series. This segments form a phase trajectory in phase space of human activity. The principal components of segments of the phase trajectory are treated as feature descriptions at the point in the timeline. The paper introduces a new distance function between the points in new feature space. To reval changes of types of the human activity the paper proposes an algorithm. This algorithm clusters points of the timeline by using a pairwise distances matrix. The algorithm was tested on synthetic and real data. This real data were obtained from a mobile accelerometer.

中文翻译：

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用于人类活动识别的准周期时间序列聚类

摘要

本文分析了时间序列中的周期性信号，以使用移动加速度计识别人类活动。时间轴中的每个点都对应于历史时间序列的一部分。这些片段在人类活动的相空间中形成相轨迹。在时间轴上的该点，将相轨迹的各段的主要成分视为特征描述。本文介绍了新特征空间中各点之间的新距离函数。为了评估人类活动类型的变化，本文提出了一种算法。该算法通过使用成对距离矩阵对时间轴的点进行聚类。对该算法进行了综合和真实数据测试。该真实数据是从移动加速度计获得的。