SM-DTW: Stability Modulated Dynamic Time Warping for signature verification

Parziale, Antonio; Diaz, Moises; Ferrer, Miguel A.; Marcelli, Angelo

doi:10.1016/j.patrec.2018.07.029

Cited by 49 publications

(22 citation statements)

References 35 publications

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“…DTW is used to compare the similarities between two time-series patterns at different speeds [ 54 ]. By matching the positions between two time-series datasets, DTW can be flexibly compared and properly matched against distorted vectors [ 55 , 56 ]. For two time-series graphs, each

in length, i.e.,

and

, distance

of two vectors is calculated as

and filled using a warping matrix.…”

Section: Methodsmentioning

confidence: 99%

Upper Limb Rehabilitation Tools in Virtual Reality Based on Haptic and 3D Spatial Recognition Analysis: A Pilot Study

Kim

Lee

2021

Sensors

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With aging, cerebrovascular diseases can occur more often. Stroke cases involve hemiplegia, which causes difficulties in performing activities of daily living. Existing rehabilitation treatments are based on the subjective evaluation of the therapist as the need for non-contact care arises; it is necessary to develop a system that can self-rehabilitate and offer objective analysis. Therefore, we developed rehabilitation tools that enable self-rehabilitation exercises in a virtual space based on haptics. Thirty adults without neurological damage were trained five times in a virtual environment, and the time, number of collisions, and coordinates were digitized and stored in real time. An analysis of variance (ANOVA) of the time and distance similarity changes revealed that as the number of rounds increased, no changes or increases occurred (p ≥ 0.05), and the collisions and paths were stable as the training progressed (p < 0.05). ANOVA showed a high correlation (0.90) with a decrease in the number of crashes and time required. It was meaningful to users when performing rehabilitation training more than four times and significantly impacted the analysis. This study analyzed the upper limb and cognitive rehabilitation of able-boded people in three-dimensional space in a virtual environment; the performance difficulty could be controlled through variations in rehabilitation models.

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in length, i.e.,

and

, distance

of two vectors is calculated as

and filled using a warping matrix.…”

Section: Methodsmentioning

confidence: 99%

Upper Limb Rehabilitation Tools in Virtual Reality Based on Haptic and 3D Spatial Recognition Analysis: A Pilot Study

Kim

Lee

2021

Sensors

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show abstract

“…Moreover, a sliding window was designed to process the raw signal using DTW frame by frame for effective calculation. Parziale et al [25] introduced the Stability Modulated Dynamic Time Warping algorithm for incorporating the stability regions on two datasets, i.e., the most similar parts between two signatures, into the distance measure between a pair of signatures computed by DTW for signature verification. The proposed algorithm improves the performance of the baseline system and is more favorable than other signature verification systems.…”

Section: Breakdown Of the Empirical Mode Decomposition (Emd) And Dynamentioning

confidence: 99%

Object Detection of Ground-Penetrating Radar Signals Using Empirical Mode Decomposition and Dynamic Time Warping

Senalik

Wacker

et al. 2020

Forests

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An object detection method of ground-penetrating radar (GPR) signals using empirical mode decomposition (EMD) and dynamic time warping (DTW) is proposed in this study. Two groups of timber specimens were examined. The first group comprised of Douglas fir (Pseudotsuga menziesii) timber sections prepared in the laboratory with inserts of known internal characteristics. The second group comprised of timber girders salvaged from the timber bridges on historic Route 66 over 80 years. A GSSI Subsurface Interface Radar (SIR) System 4000 with a 2 GHz palm antenna was used to scan these two groups of specimens. GPR sensed differences in dielectric constants (DC) along the scan path caused by the presence of water, metal, or air within the wood. This study focuses on the feature identification and defect classification. The results show that the processing methods were efficient for the illustration of GPR information.

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“…In these approaches the choice of the similarity measure is essential for their performance. Hence, in recent years a diverse collection of similarity measures has been proposed in the literature to further enrich the existing portfolio (Parziale et al, 2018;Cinti et al, 2017;Oregi et al, 2017a;Zhou and De la Torre, 2016;Zhao and Itti, 2018).…”

Section: Introductionmentioning

confidence: 99%

On-line Elastic Similarity Measures for time series

Oregi

Pérez

Ser

et al. 2019

Pattern Recognition

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The way similarity is measured among time series is of paramount importance in many data mining and machine learning tasks. For instance, Elastic Similarity Measures are widely used to determine whether two time series are similar to each other. Indeed, in off-line time series mining, these measures have been shown to be very effective due to their ability to handle time distortions and mitigate their effect on the resulting distance. In the on-line setting, where available data increase continuously over time and not necessary in a stationary manner, stream mining approaches are required to be fast with limited memory consumption and capable of adapting to different stationary intervals. In this sense, the computational complexity of Elastic Similarity Measures and their lack of flexibility to accommodate different stationary intervals, make these similarity measures incompatible with the requirements mentioned. To overcome these issues, this paper adapts the family of Elastic Similarity Measures-which includes Dynamic Time Warping, Edit Distance, Edit Distance for Real Sequences and Edit Distance with Real Penalty-to the on-line setting. The proposed adaptation is based on two main ideas: a forgetting mechanism and the incremental computation. The former makes the similarity consistent with streaming time series characteristics by giving more importance to recent observations, whereas the latter reduces the computational complexity by avoiding unnecessary computations. In order to assess the behavior of the proposed similarity measure in on-line settings, two different experiments have been carried out. The first aims at showing the efficiency of the proposed adaptation, to do so we calculate and compare the computation time for the elastic measures and their on-line adaptation. By analyzing the results drawn from a distance-based streaming machine learning model, the second experiment intends to show the effect of the forgetting mechanism on the resulting similarity value. The experimentation shows, for the aforementioned Elastic Similarity Measures, that the proposed adaptation meets the memory, computational complexity and flexibility constraints imposed by streaming data.

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SM-DTW: Stability Modulated Dynamic Time Warping for signature verification

Cited by 49 publications

References 35 publications

Upper Limb Rehabilitation Tools in Virtual Reality Based on Haptic and 3D Spatial Recognition Analysis: A Pilot Study

Upper Limb Rehabilitation Tools in Virtual Reality Based on Haptic and 3D Spatial Recognition Analysis: A Pilot Study

Object Detection of Ground-Penetrating Radar Signals Using Empirical Mode Decomposition and Dynamic Time Warping

On-line Elastic Similarity Measures for time series

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