scispace - formally typeset
Search or ask a question
Topic

Dynamic time warping

About: Dynamic time warping is a research topic. Over the lifetime, 6013 publications have been published within this topic receiving 133130 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: Based on the dynamic time warping (DTW) distance method, the authors discusses the application of similarity measurement in the similarity analysis of simulated multi-point ground motions and the actual seismic array records.
Abstract: The reasonability of artificial multi-point ground motions and the identification of abnormal records in seismic array observations, are two important issues in application and analysis of multi-point ground motion fields. Based on the dynamic time warping (DTW) distance method, this paper discusses the application of similarity measurement in the similarity analysis of simulated multi-point ground motions and the actual seismic array records. Analysis results show that the DTW distance method not only can quantitatively reflect the similarity of simulated ground motion field, but also offers advantages in clustering analysis and singularity recognition of actual multi-point ground motion field.

29 citations

Journal ArticleDOI
TL;DR: This work proposes a Multi-variations Activity Recognition (MAR) system, based on an observation that the Channel State Information (CSI) is sensitive to the activities of body parts, and applies MAR in gaits recognition of multiple volunteers to evaluate the accuracy performance.
Abstract: With the prevalence of commercial WiFi devices and the development of Internet of Things (IoT), researchers have extended the usage of WiFi from communication to sensing. Recently, device-free human activity recognition has been applied to support WiFi based remote control and human-computer interaction. However, prior works usually recognize each individual activity by extracting the feature of corresponding WiFi signals, and hence distinguishing differences between human activities. Once the activity has multiple variations of different body parts (such as head, arm and leg), distinguishing such sub-activities is extremely difficult. In this paper, we propose a Multi-variations Activity Recognition (MAR) system to identify multiple-variations of body parts. Our work is based on an observation that the Channel State Information (CSI) is sensitive to the activities of body parts. Firstly, CANDECOMP/ PARAFAC (CP) decomposition and Dynamic Time Warping (DTW) are applied to recognize multi-variations activities. Secondly, we theoretically analyse the uniqueness of CP decomposition. Then, we design specific experiment to verify the reliability and stability of uniqueness. Finally, we apply MAR in gaits recognition of multiple volunteers to evaluate the accuracy performance. The experiment results demonstrate that MAR achieves average 95% accuracy in gaits recognition.

29 citations

Journal ArticleDOI
TL;DR: The results show that the prediction accuracy of the proposed hybrid model, based on dynamic time warping fuzzy clustering algorithm, is significantly better than the benchmarks considered in this paper.

29 citations

PatentDOI
TL;DR: In this article, it is proposed that speech recognition be implemented in the form of a predefined sequence of states, such that upon recognition of an appropriate voice command, the system changes from one state to another state, and this change takes place in dependence on at least one speech recognition parameter.
Abstract: To control an arbitrary system by speech recognition, it is proposed that speech recognition be implemented in the form of a predefined sequence of states, such that, upon recognition of an appropriate voice command, the system changes from one state to another state, and this change takes place in dependence on at least one speech recognition parameter. The speech recognition parameters can influence, for example, the so-called “false acceptance rate” (FAR) and/or the “false rejection rate” (FRR), which thus are set to state-specific values for the individual states, in order to achieve improved recognition accuracy.

29 citations

Journal ArticleDOI
TL;DR: A new parameter-free measure for the specific purpose of quickly and accurately assessing the similarity between two given long time series, which outperforms DTW while providing competitive results against popular distance-based classifiers and is orders of magnitude faster than DTW.
Abstract: The problem of similarity measures is a major area of interest within the field of time series classification (TSC). With the ubiquitous of long time series and the increasing demand for analyzing them on limited resource devices, there is a crucial need for efficient and accurate measures to deal with such kind of data. In fact, there are a plethora of good time series similarity measures in the literature. However, most existing methods achieve good performance for short time series, but their effectiveness decreases quickly as time series are longer. In this paper, we develop a new parameter-free measure for the specific purpose of quickly and accurately assessing the similarity between two given long time series. The proposed “Local Extrema Dynamic Time Warping” (LE-DTW) consists of two steps. The first is a time series representation technique that starts by reducing the dimensionality of a given time series using its local extrema. Next, it physically separates the minima and maxima points for more intuitiveness and consistency of the so-obtained time series representation. The second step consists in adapting the Dynamic Time Warping (DTW) measure so as to evaluate the score of similarity between the generated representations. We test the performance of LE-DTW on a wide range of real-world problems from the UCR time series archive for TSC. Experimental results indicate that for short time series, the proposed method achieves reasonable classification accuracy as compared to DTW. However, for long time series, LE-DTW performs much better. Indeed, it outperforms DTW while providing competitive results against popular distance-based classifiers. Moreover, in terms of efficiency, LE-DTW is orders of magnitude faster than DTW.

29 citations


Network Information
Related Topics (5)
Feature extraction
111.8K papers, 2.1M citations
91% related
Convolutional neural network
74.7K papers, 2M citations
87% related
Deep learning
79.8K papers, 2.1M citations
87% related
Image segmentation
79.6K papers, 1.8M citations
86% related
Artificial neural network
207K papers, 4.5M citations
84% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023236
2022471
2021341
2020416
2019420
2018377