Title | ||
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Efficient Kernel-Based Subsequence Search for Enabling Health Monitoring Services in IoT-Based Home Setting. |
Abstract | ||
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This paper presents an efficient approach for subsequence search in data streams. The problem consists of identifying coherent repetitions of a given reference time-series, also in the multivariate case, within a longer data stream. The most widely adopted metric to address this problem is Dynamic Time Warping (DTW), but its computational complexity is a well-known issue. In this paper, we present an approach aimed at learning a kernel approximating DTW for efficiently analyzing streaming data collected from wearable sensors, while reducing the burden of DTW computation. Contrary to kernel, DTW allows for comparing two time-series with different length. To enable the use of kernel for comparing two time-series with different length, a feature embedding is required in order to obtain a fixed length vector representation. Each vector component is the DTW between the given time-series and a set of "basis" series, randomly chosen. The approach has been validated on two benchmark datasets and on a real-life application for supporting self-rehabilitation in elderly subjects has been addressed. A comparison with traditional DTW implementations and other state-of-the-art algorithms is provided: results show a slight decrease in accuracy, which is counterbalanced by a significant reduction in computational costs. |
Year | DOI | Venue |
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2019 | 10.3390/s19235192 | SENSORS |
Keywords | DocType | Volume |
data stream analysis,pattern query,kernel learning,dynamic time warping,subsequence search | Journal | 19 |
Issue | ISSN | Citations |
23 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Antonio Candelieri | 1 | 10 | 8.37 |
Stanislav Fedorov | 2 | 0 | 0.34 |
Enza Messina | 3 | 214 | 23.18 |