Efficient Kernel-based Subsequence Search for User Identification from Walking Activity

TL;DR

This paper introduces a kernel-based method to efficiently identify user walking patterns in streaming sensor data, approximating DTW to reduce computational costs while maintaining accuracy.

Contribution

It proposes a novel kernel learning approach that approximates DTW for fast subsequence search in streaming data from wearable sensors.

Findings

The method reduces computation time compared to traditional DTW.

It accurately identifies users based on walking activity.

Validated on a benchmark dataset with promising results.

Abstract

This paper presents an efficient approach for subsequence search in data streams. The problem consists in identifying coherent repetitions of a given reference time-series, eventually multi-variate, within a longer data stream. Dynamic Time Warping (DTW) is the metric most widely used to implement pattern query, but its computational complexity is a well-known issue. In this paper we present an approach aimed at learning a kernel able to approximate DTW to be used for efficiently analyse streaming data collected from wearable sensors, reducing the burden of computation. Contrary to kernel, DTW allows for comparing time series with different length. Thus, to use a kernel, a feature embedding is used to represent a time-series as a fixed length vector. Each vector component is the DTW between the given time-series and a set of 'basis' series, usually randomly chosen. The vector size is the number of basis series used for the feature embedding. Searching for the portion of the data stream minimizing the DTW with the reference subsequence leads to a global optimization problem. The proposed approach has been validated on a benchmark dataset related to the identification of users depending on their walking activity. A comparison with a traditional DTW implementation is also provided.