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.
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TL;DR: An improved version of parametric time warping is presented, which enables the method to be used in LC-MS measurements in proteomics and includes a new similarity measure for comparing warped chromatograms, an insurance against peaks at the extremes of the chromatogram disappearing because of the warping, and the possibility to select and use multiple traces in searching the optimal alignment.
77 citations
TL;DR: This analysis shows that for chirp-periodic signals the FChT can reach the limit of the time-frequency (TF) uncertainty principle, while simultaneously keeping the cross-terms at minimum level.
76 citations
29 Oct 1998
TL;DR: A new single-lead method based on dynamic time warping (DTW) is presented, which produces a smaller mean error, but has a higher standard deviation than Laguna et al.'s (1997) two- lead method.
Abstract: To detect an abnormal conduction of the heart, cardiologists annotate certain points in the electrocardiogram (ECG) manually. As this is a very strenuous task, several algorithms have been developed to segment the ECG automatically. In this paper, we describe several such methods, and we further present a new single-lead method based on dynamic time warping (DTW). The results are tested on the QT database and compared to Laguna et al.'s (1997) two-lead method. DTW produces a smaller mean error, but has a higher standard deviation than Laguna's method.
76 citations
22 Oct 2007
TL;DR: A novel algorithm for activity recognition that considers the variability in movement speed, by using dynamic programming is proposed, by means of a matching and recognition technique that determines the distance between the signal input and a set of previously defined templates.
Abstract: In the context of tele-monitoring, great interest is presently devoted to physical activity, mainly of elderly or people with disabilities. In this context, many researchers studied the recognition of activities of daily living by using accelerometers. The present work proposes a novel algorithm for activity recognition that considers the variability in movement speed, by using dynamic programming. This objective is realized by means of a matching and recognition technique that determines the distance between the signal input and a set of previously defined templates. Two different approaches are here presented, one based on Dynamic Time Warping (DTW) and the other based on the Derivative Dynamic Time Warping (DDTW). The algorithm was applied to the recognition of gait, climbing and descending stairs, using a biaxial accelerometer placed on the shin. The results on DDTW, obtained by using only one sensor channel on the shin showed an average recognition score of 95%, higher than the values obtained with DTW (around 85%). Both DTW and DDTW consistently show higher classification rate than classical Linear Time Warping (LTW).
76 citations
02 Dec 2013
TL;DR: A technique for gait cycle extraction by incorporating the Piecewise Linear Approximation (PLA) technique is presented and two new approaches to classify gait features extracted from the cycle-based segmentation by using Support Vector Machines (SVMs) are presented.
Abstract: Biometric gait authentication using Personal Mobile Device (PMD) based accelerometer sensors offers a user-friendly, unobtrusive, and periodic way of authenticating individuals on PMD. In this paper, we present a technique for gait cycle extraction by incorporating the Piecewise Linear Approximation (PLA) technique. We also present two new approaches to classify gait features extracted from the cycle-based segmentation by using Support Vector Machines (SVMs); a) pre-computed data matrix, b) pre-computed kernel matrix. In the first approach, we used Dynamic Time Warping (DTW) distance to compute data matrices, and in the later DTW is used for constructing an elastic similarity measure based kernel function called Gaussian Dynamic Time Warp (GDTW) kernel. Both approaches utilize the DTW similarity measure and can be used for classifying equal length gait cycles, as well as different length gait cycles. To evaluate our approaches we used normal walk biometric gait data of 51 participants. This gait data is collected by attaching a PMD to the belt around the waist, on the right-hand side of the hip. Results show that these new approaches need to be studied more, and potentially lead us to design more robust and reliable gait authentication systems using PMD based accelerometer sensor.
76 citations