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Journal articles on the topic 'Dynamic time warping algorithm'

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Published: 30 May 2022
Last updated: 8 June 2024

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1

Jeong, Seung-Do. "Speaker Identification Using Dynamic Time Warping Algorithm." Journal of the Korea Academia-Industrial cooperation Society 12, no. 5 (May 31, 2011): 2402–9. http://dx.doi.org/10.5762/kais.2011.12.5.2402.

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2

Stübinger, Johannes, and Dominik Walter. "Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships." Sensors 22, no. 18 (September 12, 2022): 6884. http://dx.doi.org/10.3390/s22186884.

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This paper develops a multi-dimensional Dynamic Time Warping (DTW) algorithm to identify varying lead-lag relationships between two different time series. Specifically, this manuscript contributes to the literature by improving upon the use towards lead-lag estimation. Our two-step procedure computes the multi-dimensional DTW alignment with the aid of shapeDTW and then utilises the output to extract the estimated time-varying lead-lag relationship between the original time series. Next, our extensive simulation study analyses the performance of the algorithm compared to the state-of-the-art methods Thermal Optimal Path (TOP), Symmetric Thermal Optimal Path (TOPS), Rolling Cross-Correlation (RCC), Dynamic Time Warping (DTW), and Derivative Dynamic Time Warping (DDTW). We observe a strong outperformance of the algorithm regarding efficiency, robustness, and feasibility.
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3

Gao, Cuifang, Junjie Li, Wanqiang Shen, and Ping Yin. "Two-dimensional dynamic time warping algorithm for matrices similarity." Intelligent Data Analysis 26, no. 4 (July 11, 2022): 859–71. http://dx.doi.org/10.3233/ida-215908.

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Dynamic Time Warping (DTW algorithm) provides an effective method to obtain the similarity between unequal-sized signals. However, it cannot directly deal with high-dimensional samples such as matrices. Expanding a matrix to one dimensional vector as the input data of DTW will decrease the measure accuracy because of the losing of position information in the matrix. Aiming at this problem, a two-dimensional dynamic time warping algorithm (2D-DTW) is proposed in this paper to directly measure the similarity between matrices. In 2D-DTW algorithm, a three dimensional distance-cuboid is constructed, and its mapped distance matrix is defined by cutting and compressing the distance-cuboid. By introducing the dynamic programming theory to search the shortest warping path in the mapped matrix, the corresponding shortest distance can be obtained as the expected similarity measure. The experimental results suggest that the performance of 2D-DTW distance is superior to the traditional Euclidean distance and can improve the similarity accuracy between matrices by introducing the warping alignment mechanisms. 2D-DTW algorithm extends the application ranges of traditional DTW and is especially suitable for high-dimensional data.
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Ali, Aya Hamdy, Ayman Atia, and Mostafa-Sami M. Mostafa. "Recognizing Driving Behavior and Road Anomaly using Smartphone Sensors." International Journal of Ambient Computing and Intelligence 8, no. 3 (July 2017): 22–37. http://dx.doi.org/10.4018/ijaci.2017070102.

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Road traffic accidents are caused 1.25 million deaths per year worldwide. To improve road safety and reducing road accidents, a recognition method for driving events is introduced in this paper. The proposed method detected and classified both driving behaviors and road anomalies patterns based on smartphone sensors (accelerometer and gyroscope). k-Nearest Neighbor and Dynamic Time Warping algorithms were utilized for method evaluation. Experiments were conducted to evaluate k-nearest neighbor and dynamic time warping algorithms accuracy for road anomalies and driving behaviors detection, moreover, driving behaviors classification. Evaluation results showed that k-nearest neighbor algorithm detected road anomalies and driving behaviors with total accuracy 98.67%. Dynamic time warping algorithm classified (normal and abnormal) driving behaviors with total accuracy 96.75%.
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Hagen, Cedric J., Brendan T. Reilly, Joseph S. Stoner, and Jessica R. Creveling. "Dynamic time warping of palaeomagnetic secular variation data." Geophysical Journal International 221, no. 1 (January 9, 2020): 706–21. http://dx.doi.org/10.1093/gji/ggaa004.

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SUMMARY We present and make publicly available a dynamic programming algorithm to simultaneously align the inclination and declination vector directions of sedimentary palaeomagnetic secular variation data. This algorithm generates a library of possible alignments through the systematic variation of assumptions about the relative accumulation rate and shared temporal overlap of two or more time-series. The palaeomagnetist can then evaluate this library of reproducible and objective alignments using available geological constraints, statistical methods and expert knowledge. We apply the algorithm to align previously (visually) correlated medium to high accumulation rate northern North Atlantic Holocene deposits (101–102 cm ka–1) with strong radiocarbon control. The algorithm generates plausible alignments that largely conform with radiocarbon and magnetic acquisition process uncertainty. These alignments illustrate the strengths and limitations of this numerical approach.
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Hale, Dave. "Dynamic warping of seismic images." GEOPHYSICS 78, no. 2 (March 1, 2013): S105—S115. http://dx.doi.org/10.1190/geo2012-0327.1.

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The problem of estimating relative time (or depth) shifts between two seismic images is ubiquitous in seismic data processing. This problem is especially difficult where shifts are large and vary rapidly with time and space, and where images are contaminated with noise or for other reasons are not shifted versions of one another. A new solution to this problem requires only simple extensions of a classic dynamic time warping algorithm for speech recognition. A key component of that classic algorithm is a nonlinear accumulation of alignment errors. By applying the same nonlinear accumulator repeatedly in all directions along all sampled axes of a multidimensional image, I obtain a new and effective method for dynamic image warping (DIW). In tests where known shifts vary rapidly, this new method is more accurate than methods based on crosscorrelations of windowed images. DIW also aligns seismic reflectors well in examples where shifts are unknown, for images with differences not limited to time shifts.
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Liu, Zhen Wu, Zhi Wu Shang, Ya Feng Li, and Tai Yong Wang. "A Fault Diagnosis System Based on Bistable Stochastic Resonance and Dynamic Time Warping." Key Engineering Materials 693 (May 2016): 1294–99. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1294.

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Stator current signal of driving motor can be easily measured. Using it in the gearbox fault diagnosis system is inexpensive and suitable for remote monitoring. According to the application of the Motor Current Signal Analysis in machinery fault detection, we present a new gearbox fault diagnosis system. In modern signal processing technology, Stochastic Resonance theory is widely used to improve SNR (signal to noise ratio). Dynamic time warping algorithm is a simple and efficient way of the pattern identified. Combine the Stochastic Resonance theory and dynamic time warping algorithm as the basic theory of fault diagnosis. To realize the development of fault diagnosis software, we use the mixed-programming of MATLAB algorithms library and VC++.
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Wang, Hairong, and Qiufang Zheng. "Improvement and Application of Hale’s Dynamic Time Warping Algorithm." Symmetry 16, no. 6 (May 23, 2024): 645. http://dx.doi.org/10.3390/sym16060645.

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Due to the different generation and propagation mechanisms of P- and S-waves, there may be significant differences in the seismic data collected by the two, which poses a great obstacle to the time domain matching of P- and S-waves in multiwave exploration. Furthermore, the quality and accuracy of the matching effect will directly affect the subsequent multiwave joint inversion and interpretation effect. Therefore, the study of P and S-wave-matching methods plays a crucial role in seismic exploration. In 2013, Hale improved the classical Dynamic Time Warping (DTW) algorithm applied to solve the problem of speech recognition, and obtained the DTW algorithm suitable for solving the matching of P-waves and S-waves. The seismic wave-matching results generated by this algorithm are horizontal discontinuous (different trajectories) and need further processing. This study analyses the algorithm based on simulations of seismic waves using Ricker wavelets. In response to existing problems, this paper proposes strategies to improve the DTW algorithm. The algorithm in this study significantly improved the continuity of the registration results of the actual seismic wave data in the horizontal direction (different traces).
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Pielmus, Alexandru-Gabriel, Michael Klum, Timo Tigges, Reinhold Orglmeister, and Mike Urban. "Progressive Dynamic Time Warping for Noninvasive Blood Pressure Estimation." Current Directions in Biomedical Engineering 6, no. 3 (September 1, 2020): 579–82. http://dx.doi.org/10.1515/cdbme-2020-3148.

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AbstractArterial blood pressure is one of the most important cardiovascular parameters. Yet, current-generation devices for continuous, noninvasive acquisition are few, expensive and bulky. Novel signal processing applied to easily acquired unimodal signals can alleviate this issue, reducing size, cost and expanding the use of such devices to ambulatory, everyday settings. The features of pulse waves acquired by photo- or impedance-plethysmography can be used to estimate the underlying blood pressure. We present a progressive dynamic time warping algorithm, which implicitly parametrizes the morphological changes in these waves. This warping method is universally applicable to most pulse wave shapes, as it is largely independent of fiducial point detection or explicit parametrization. The algorithm performance is validated in a feature selection and regression framework against a continuous, noninvasive Finapres NOVA monitor, regarding systolic, mean and diastolic pressures during a light physical strain test protocol on four clinically healthy subjects (age18- 33, one female). The obtained mean error is 2.13 mmHg, the mean absolute error is 5.4 mmHg and the standard deviation is 5.6 mmHg. These results improve on our previous work on dynamic time warping. Using single-sensor, peripherally acquired pulse waves, progressive dynamic time warping can thus improve the flexibility of noninvasive, continuous blood pressure estimation.
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C.Bhokse, Bhushan, and Bhushan S. Thakare. "Devnagari Handwriting Recognition System using Dynamic Time Warping Algorithm." International Journal of Computer Applications 52, no. 9 (August 30, 2012): 7–13. http://dx.doi.org/10.5120/8228-0241.

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11

Gollmer, K., and C. Posten. "Supervision of bioprocesses using a dynamic time warping algorithm." Control Engineering Practice 4, no. 9 (September 1996): 1287–95. http://dx.doi.org/10.1016/0967-0661(96)00136-0.

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12

Zhang, Zheng, Ping Tang, Lianzhi Huo, and Zengguang Zhou. "MODIS NDVI time series clustering under dynamic time warping." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 05 (September 2014): 1461011. http://dx.doi.org/10.1142/s0219691314610116.

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For MODIS NDVI time series with cloud noise and time distortion, we propose an effective time series clustering framework including similarity measure, prototype calculation, clustering algorithm and cloud noise handling. The core of this framework is dynamic time warping (DTW) distance and its corresponding averaging method, DTW barycenter averaging (DBA). We used 12 years of MODIS NDVI time series to perform annual land-cover clustering in Poyang Lake Wetlands. The experimental result shows that our method performs better than classic clustering based on ordinary Euclidean methods.
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13

Hensel, Stefan, and Marin B. Marinov. "Comparison of Time Warping Algorithms for Rail Vehicle Velocity Estimation in Low Speed Scenarios." Metrology and Measurement Systems 24, no. 1 (March 1, 2017): 161–73. http://dx.doi.org/10.1515/mms-2017-0012.

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Abstract Precise measurement of rail vehicle velocities is an essential prerequisite for the implementation of modern train control systems and the improvement of transportation capacity and logistics. Novel eddy current sensor systems make it possible to estimate velocity by using cross-correlation techniques, which show a decline in precision in areas of high accelerations. This is due to signal distortions within the correlation interval. We propose to overcome these problems by employing algorithms from the field of dynamic programming. In this paper we evaluate the application of correlation optimized warping, an enhanced version of dynamic time warping algorithms, and compare it with the classical algorithm for estimating rail vehicle velocities in areas of high accelerations and decelerations.
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14

Chen, Shuangquan, Song Jin, Xiang-Yang Li, and Wuyang Yang. "Nonstretching normal-moveout correction using a dynamic time warping algorithm." GEOPHYSICS 83, no. 1 (January 1, 2018): V27—V37. http://dx.doi.org/10.1190/geo2016-0673.1.

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Normal-moveout (NMO) correction is one of the most important routines in seismic processing. NMO is usually implemented by a sample-by-sample procedure; unfortunately, such implementation not only decreases the frequency content but also distorts the amplitude of seismic waveforms resulting from the well-known stretch. The degree of stretch increases with increasing offset. To minimize severe stretch associated with far offset, we use a dynamic time warping (DTW) algorithm to achieve an automatic dynamic matching NMO nonstretch correction, which does not handle crossing events and convoluted events such as thin layers. Our algorithm minimizes the stretch through an automatic static temporal correction of seismic wavelets. The local static time shifts are obtained using a DTW algorithm, which is a nonlinear optimization method. To mitigate the influence of noise, we evaluated a multitrace window strategy to improve the signal-to-noise ratio of seismic data by obtaining a more precise moveout correction at far-offset traces. To illustrate the effectiveness of our algorithm, we first applied our method to synthetic data and then to field seismic data. Both tests illustrate that our algorithm minimizes the stretch associated with far offsets, and the method preserves the amplitude fidelity.
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15

He, Zhi Guo, and Ze Min Liu. "Chinese Connected Word Speech Recognition Based on Derivative Dynamic Time Warping." Advanced Materials Research 542-543 (June 2012): 1324–29. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1324.

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The algorithm of derivative dynamic time warping (DDTW) can overcome the shortcoming of dynamic time warping (DTW) and the computational complexity has not increased. In this paper, the algorithm of DDTW was applied to Chinese connected word speech recognition. For each isolated word, as an independent reference template and as basic recognition unit, there was an independent reference template to correspond; the matching between some word of the test string and a reference template was done by the DDTW, and the reference string which had the minimum cumulative distance was as output. The experimental results show that our method is obviously superior to all the methods based on DTW, and the recognition rate has reached 90%.
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16

Jin, Song, ShuangQuan Chen, Jianxin Wei, and Xiang-Yang Li. "Automatic seismic event tracking using a dynamic time warping algorithm." Journal of Geophysics and Engineering 14, no. 5 (August 14, 2017): 1138–49. http://dx.doi.org/10.1088/1742-2140/aa7309.

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17

Son, Nguyen Thanh. "Pattern matching under dynamic time warping for time series prediction." Tạp chí Khoa học 15, no. 3 (September 20, 2019): 148. http://dx.doi.org/10.54607/hcmue.js.15.3.146(2018).

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Time series forecasting based on pattern matching has received a lot of interest in the recent years due to its simplicity and the ability to predict complex nonlinear behavior. In this paper, we investigate into the predictive potential of the method using k-NN algorithm based on R*-tree under dynamic time warping (DTW) measure. The experimental results on four real datasets showed that this approach could produce promising results in terms of prediction accuracy on time series forecasting when comparing to the similar method under Euclidean distance.
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18

Vu, Hoa, Clifton Carey, and Sridhar Mahadevan. "Manifold Warping: Manifold Alignment over Time." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1155–61. http://dx.doi.org/10.1609/aaai.v26i1.8281.

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Knowledge transfer is computationally challenging, due in part to the curse of dimensionality, compounded by source and target domains expressed using different features (e.g., documents written in different languages). Recent work on manifold learning has shown that data collected in real-world settings often have high-dimensional representations, but lie on low-dimensional manifolds. Furthermore, data sets collected from similar generating processes often present different high-dimensional views, even though their underlying manifolds are similar. The ability to align these data sets and extract this common structure is critical for many transfer learning tasks. In this paper, we present a novel framework for aligning two sequentially-ordered data sets, taking advantage of a shared low-dimensional manifold representation. Our approach combines traditional manifold alignment and dynamic time warping algorithms using alternating projections. We also show that the previously-proposed canonical time warping algorithm is a special case of our approach. We provide a theoretical formulation as well as experimental results on synthetic and real-world data, comparing manifold warping to other alignment methods.
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19

Kwong, S., Q. H. He, K. F. Man, K. S. Tang, and C. W. Chau. "Parallel Genetic-Based Hybrid Pattern Matching Algorithm for Isolated Word Recognition." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 05 (August 1998): 573–94. http://dx.doi.org/10.1142/s0218001498000348.

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Dynamic Time Warping (DTW) is a common technique widely used for nonlinear time normalization of different utterances in many speech recognition systems. Two major problems are usually encountered when the DTW is applied for recognizing speech utterances: (i) the normalization factors used in a warping path; and (ii) finding the K-best warping paths. Although DTW is modified to compute multiple warping paths by using the Tree-Trellis Search (TTS) algorithm, the use of actual normalization factor still remains a major problem for the DTW. In this paper, a Parallel Genetic Time Warping (PGTW) is proposed to solve the above said problems. A database extracted from the TIMIT speech database of 95 isolated words is set up for evaluating the performance of the PGTW. In the database, each of the first 15 words had 70 different utterances, and the remaining 80 words had only one utterance. For each of the 15 words, one utterance is arbitrarily selected as the test template for recognition. Distance measure for each test template to the utterances of the same word and to those of the 80 words is calculated with three different time warping algorithms: TTS, PGTW and Sequential Genetic Time Warping (SGTW). A Normal Distribution Model based on Rabiner23 is used to evaluate the performance of the three algorithms analytically. The analyzed results showed that the PGTW had performed better than the TTS. It also showed that the PGTW had very similar results as the SGTW, but about 30% CPU time is saved in the single processor system.
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Mei, Jiangyuan, Jian Hou, Hamid Reza Karimi, and Jiarao Huang. "A Novel Data-Driven Fault Diagnosis Algorithm Using Multivariate Dynamic Time Warping Measure." Abstract and Applied Analysis 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/625814.

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Process monitoring and fault diagnosis (PM-FD) has been an active research field since it plays important roles in many industrial applications. In this paper, we present a novel data-driven fault diagnosis algorithm which is based on the multivariate dynamic time warping measure. First of all, we propose a Mahalanobis distance based dynamic time warping measure which can compute the similarity of multivariate time series (MTS) efficiently and accurately. Then, a PM-FD framework which consists of data preprocessing, metric learning, MTS pieces building, and MTS classification is presented. After that, we conduct experiments on industrial benchmark of Tennessee Eastman (TE) process. The experimental results demonstrate the improved performance of the proposed algorithm when compared with other classical PM-FD classical methods.
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Abdelkader, Mostefai. "Using Dynamic Time Warping to Detect Clones in Software Systems." International Journal of Software Innovation 9, no. 1 (January 2021): 20–36. http://dx.doi.org/10.4018/ijsi.2021010103.

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Software clone detection is a widely researched area over the last two decades. Code clones are fragments of code judged similar by some metric of similarity. This paper proposes an approach for code clone detection using dynamic time warping technique (i.e., DTW). DTW is a well-known algorithm for aligning and measuring similarity of time series and it has been found effective in many domains where similarity plays an important role such as speech and gesture recognition. The proposed approach finds clones in three steps. First software modules are extracted. Then, the extracted modules are turned to time series. Finally, the time series are compared using the DTW algorithm to find clones. The results of the experiment conducted on a well-known Benchmark show that the approach can detect clones effectively in software systems.
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GUIDO, RODRIGO CAPOBIANCO, SYLVIO BARBON JUNIOR, LUCIMAR SASSO VIEIRA, FABRÍCIO LOPES SANCHEZ, CARLOS DIAS MACIEL, PAULO ROGÉRIO SCALASSARA, JOSÉ CARLOS PEREIRA, and VITOR MULLER PUIA. "SPOKEN DOCUMENT SUMMARIZATION BASED ON DYNAMIC TIME WARPING AND WAVELETS." International Journal of Semantic Computing 01, no. 03 (September 2007): 347–57. http://dx.doi.org/10.1142/s1793351x07000214.

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This work presents a spoken document summarization (SDS) scheme that is based on an improved version of the Dynamic Time Warping (DTW) algorithm, and on the Discrete Wavelet Transform (DWT). Tests and results with sentences extracted from TIMIT speech corpus show the efficacy of the proposed technique.
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Alex, John Sahaya Rani, and Mitali Bhojwani. "FIELD-PROGRAMMABLE GATE ARRAY IMPLEMENTATION OF THE DYNAMIC TIME WARPING ALGORITHM FOR SPEECH RECOGNITION." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 248. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19753.

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Objective of this research is to implement a speech recognition algorithm in smaller form factor device. Speech recognition is an extensively used inmobile and in numerous consumer electronics devices. Dynamic time warping (DTW) method which is based on dynamic programming is chosen tobe implemented for speech recognition because of the latest trend in evolving computing power. Implementation of DTW in field-programmable gatearray is chosen for its featured flexibility, parallelization and shorter time to market. The above algorithm is implemented using Verilog on Xilinx ISE.The warping cost is less if the similarity is found and is more for dissimilar sequences which is verified in the simulation output. The results indicatethat real time implementation of DTW based speech recognition could be done in future.
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Xi, Yinfei, Di Huang, Yu Yuan, Zhiyuan Liu, Khadka Anish, and Nan Zheng. "Improved Dynamic Time Warping Algorithm for Bus Route Trajectory Curve Fitting." Journal of Transportation Engineering, Part A: Systems 147, no. 8 (August 2021): 04021044. http://dx.doi.org/10.1061/jtepbs.0000544.

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Kim, Sang, Hee Lee, Han Ko, Seung Jeong, Hyun Byun, and Kyong Oh. "Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm." Sustainability 10, no. 12 (December 6, 2018): 4641. http://dx.doi.org/10.3390/su10124641.

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The futures market plays a significant role in hedging and speculating by investors. Although various models and instruments are developed for real-time trading, it is difficult to realize profit by processing and trading a vast amount of real-time data. This study proposes a real-time index futures trading strategy that uses the KOSPI 200 index futures time series data. We construct a pattern matching trading system (PMTS) based on a dynamic time warping algorithm that recognizes patterns of market data movement in the morning and determines the afternoon’s clearing strategy. We adopt 13 and 27 representative patterns and conduct simulations with various ranges of parameters to find optimal ones. Our experimental results show that the PMTS provides stable and effective trading strategies with relatively low trading frequencies. Financial market investors are able to make more efficient investment strategies by using the PMTS. In this sense, the system developed in this paper contributes the efficiency of the financial markets and helps to achieve sustained economic growth.
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Lee, Hyun-Seob. "Application of dynamic time warping algorithm for pattern similarity of gait." Journal of Exercise Rehabilitation 15, no. 4 (August 28, 2019): 526–30. http://dx.doi.org/10.12965/jer.1938384.192.

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Jang, Seok-Woo, Young-Jae Park, and Gye-Young Kim. "Efficient Handwritten Character Verification Using an Improved Dynamic Time Warping Algorithm." Journal of the Korea Society of Computer and Information 15, no. 7 (July 31, 2010): 19–26. http://dx.doi.org/10.9708/jksci.2010.15.7.019.

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Zhang, Zusheng, Tiezhu Zhao, Xin Ao, and Huaqiang Yuan. "A Vehicle Speed Estimation Algorithm Based on Dynamic Time Warping Approach." IEEE Sensors Journal 17, no. 8 (April 15, 2017): 2456–63. http://dx.doi.org/10.1109/jsen.2017.2672735.

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Eisen, Andrew, Kim Roberts, and Peter Lawrence. "Morphological measurement of the SEP using a dynamic time warping algorithm." Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section 65, no. 2 (March 1986): 136–41. http://dx.doi.org/10.1016/0168-5597(86)90046-8.

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Li, Huayu, Jianhua Wan, Shanwei Liu, Hui Sheng, and Mingming Xu. "Wetland Vegetation Classification through Multi-Dimensional Feature Time Series Remote Sensing Images Using Mahalanobis Distance-Based Dynamic Time Warping." Remote Sensing 14, no. 3 (January 21, 2022): 501. http://dx.doi.org/10.3390/rs14030501.

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Efficient methodologies for vegetation-type mapping are significant for wetland’s management practices and monitoring. Nowadays, dynamic time warping (DTW) based on remote sensing time series has been successfully applied to vegetation classification. However, most of the previous related studies only focused on Normalized Difference Vegetation Index (NDVI) time series while ignoring multiple features in each period image. In order to further improve the accuracy of wetland vegetation classification, Mahalanobis Distance-based Dynamic Time Warping (MDDTW) using multi-dimensional feature time series was employed in this research. This method extends the traditional DTW algorithm based on single-dimensional features to multi-dimensional features and solves the problem of calculating similarity distance between multi-dimensional feature time series. Vegetation classification experiments were carried out in the Yellow River Delta (YRD). Compared with different classification methods, the results show that the K-Nearest Neighbors (KNN) algorithm based on MDDTW (KNN-MDDTW) has achieved better classification accuracy; the overall accuracy is more than 90%, and kappa is more than 0.9.
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Yang, Chan-Yun, Pei-Yu Chen, Te-Jen Wen, and Gene Eu Jan. "IMU Consensus Exception Detection with Dynamic Time Warping—A Comparative Approach." Sensors 19, no. 10 (May 14, 2019): 2237. http://dx.doi.org/10.3390/s19102237.

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A dynamic time warping (DTW) algorithm has been suggested for the purpose of devising a motion-sensitive microelectronic system for the realization of remote motion abnormality detection. In combination with an inertial measurement unit (IMU), the algorithm is potentially applicable for remotely monitoring patients who are at risk of certain exceptional motions. The fixed interval signal sampling mechanism has normally been adopted when devising motion detection systems; however, dynamically capturing the particular motion patterns from the IMU motion sensor can be difficult. To this end, the DTW algorithm, as a kind of nonlinear pattern-matching approach, is able to optimally align motion signal sequences tending towards time-varying or speed-varying expressions, which is especially suitable to capturing exceptional motions. Thus, this paper evaluated this kind of abnormality detection using the proposed DTW algorithm on the basis of its theoretical fundamentals to significantly enhance the viability of the methodology. To validate the methodological viability, an artificial neural network (ANN) framework was intentionally introduced for performance comparison. By incorporating two types of designated preprocessors, i.e., a DFT interpolation preprocessor and a convolutional preprocessor, to equalize the unequal lengths of the matching sequences, two kinds of ANN frameworks were enumerated to compare the potential applicability. The comparison eventually confirmed that the direct template-matching DTW is excellent in practical application for the detection of time-varying or speed-varying abnormality, and reliably captures the consensus exceptions.
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Chang, Claire, Thaxter Shaw, Arya Goutam, Christina Lau, Mengyi Shan, and Timothy J. Tsai. "Parameter-Free Ordered Partial Match Alignment with Hidden State Time Warping." Applied Sciences 12, no. 8 (April 8, 2022): 3783. http://dx.doi.org/10.3390/app12083783.

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This paper investigates an ordered partial matching alignment problem, in which the goal is to align two sequences in the presence of potentially non-matching regions. We propose a novel parameter-free dynamic programming alignment method called hidden state time warping that allows an alignment path to switch between two different planes: a “visible” plane corresponding to matching sections and a “hidden” plane corresponding to non-matching sections. By defining two distinct planes, we can allow different types of time warping in each plane (e.g., imposing a maximum warping factor in matching regions while allowing completely unconstrained movements in non-matching regions). The resulting algorithm can determine the optimal continuous alignment path via dynamic programming, and the visible plane induces a (possibly) discontinuous alignment path containing matching regions. We show that this approach outperforms existing parameter-free methods on two different partial matching alignment problems involving speech and music.
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Pang, Hai Bo, and You Dong Ding. "Dynamic Hand Gesture Recognition Using Kinematic Features Based on Dynamic Time Warping." Applied Mechanics and Materials 235 (November 2012): 68–73. http://dx.doi.org/10.4028/www.scientific.net/amm.235.68.

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Hand gesture provides an attractive alternative to cumbersome interface devices for human computer interface. Many hand gesture recognition methods using visual analysis have been proposed. In our research, we exploit multiple cues including divergence features, vorticity features and hand motion direction vector. Divergence and vorticity are derived from the optical flow for hand gesture recognition in videos. Then these features are computed by principal component analysis method. The hand tracking algorithm finds the hand centroids for every frame, computes hand motion direction vector. At last, we introduced dynamic time warping method to verify the robustness of our features. Those experimental results demonstrate that the proposed approach yields a satisfactory recognition rate.
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Zou, Zheng, Ming-Xing Nie, Xing-Sheng Liu, and Shi-Jian Liu. "Improved LDTW Algorithm Based on the Alternating Matrix and the Evolutionary Chain Tree." Sensors 22, no. 14 (July 15, 2022): 5305. http://dx.doi.org/10.3390/s22145305.

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Dynamic time warping under limited warping path length (LDTW) is a state-of-the-art time series similarity evaluation method. However, it suffers from high space-time complexity, which makes some large-scale series evaluations impossible. In this paper, an alternating matrix with a concise structure is proposed to replace the complex three-dimensional matrix in LDTW and reduce the high complexity. Furthermore, an evolutionary chain tree is proposed to represent the warping paths and ensure an effective retrieval of the optimal one. Experiments using the benchmark platform offered by the University of California-Riverside show that our method uses 1.33% of the space, 82.7% of the time used by LDTW on average, which proves the efficiency of the proposed method.
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Sun, Lishan, Qingsheng Gong, Liya Yao, Wei Luo, and Tianqi Zhang. "A Dynamic Time Warping Algorithm Based Analysis of Pedestrian Shockwaves at Bottleneck." Journal of Advanced Transportation 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/1269439.

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Since the quantitative methodology analysis of the high-density pedestrian shockwaves at a bottleneck is limited, this paper proposes a dynamic time warping (DTW) algorithm for identifying, analyzing, and verifying the shockwaves. A set of real-world trajectory data is used to illustrate the proposed algorithm. Results show that the DTW algorithm is capable of depicting the pedestrian shockwaves elaborately and accurately. Results also show that the shockwave velocity is unsteady, as throughout time the gathering wave velocity and the evanescent wave velocity are decreasing and increasing, respectively. The mutual influence between followers and leaders is decreased when the shockwave spreads. There is a linear relationship between the shockwave velocity and density. Furthermore, singularities present a potential match solution to help identify the changing of pedestrian behaviors. The DTW algorithm for evaluating the pedestrian system stability has significant intrinsic features in the pedestrian traffic control and management.
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Wan, Xiaoji, Hailin Li, Liping Zhang, and Yenchun Jim Wu. "Multivariate Time Series Data Clustering Method Based on Dynamic Time Warping and Affinity Propagation." Wireless Communications and Mobile Computing 2021 (June 24, 2021): 1–8. http://dx.doi.org/10.1155/2021/9915315.

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In view of the importance of various components and asynchronous shapes of multivariate time series, a clustering method based on dynamic time warping and affinity propagation is proposed. From the two perspectives of the global and local properties information of multivariate time series, the relationship between the data objects is described. It uses dynamic time warping to measure the similarity between original time series data and obtain the similarity between the corresponding components. Moreover, it also uses the affinity propagation to cluster based on the similarity matrices and, respectively, establishes the correlation matrices for various components and the whole information of multivariate time series. In addition, we further put forward the synthetical correlation matrix to better reflect the relationship between multivariate time series data. Again the affinity propagation algorithm is applied to clustering the synthetical correlation matrix, which realizes the clustering analysis of the original multivariate time series data. Numerical experimental results demonstrate that the efficiency of the proposed method is superior to the traditional ones.
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Li, Wenguo, Zhizeng Luo, and Xugang Xi. "Movement Trajectory Recognition of Sign Language Based on Optimized Dynamic Time Warping." Electronics 9, no. 9 (August 29, 2020): 1400. http://dx.doi.org/10.3390/electronics9091400.

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Movement trajectory recognition is the key link of sign language (SL) translation research, which directly affects the accuracy of SL translation results. A new method is proposed for the accurate recognition of movement trajectory. First, the gesture motion information collected should be converted into a fixed coordinate system by the coordinate transformation. The SL movement trajectory is reconstructed using the adaptive Simpson algorithm to maintain the originality and integrity of the trajectory. The algorithm is then extended to multidimensional time series by using Mahalanobis distance (MD). The activation function of generalized linear regression (GLR) is modified to optimize the dynamic time warping (DTW) algorithm, which ensures that the local shape characteristics are considered for the global amplitude characteristics and avoids the problem of abnormal matching in the process of trajectory recognition. Finally, the similarity measure method is used to calculate the distance between two warped trajectories, to judge whether they are classified to the same category. Experimental results show that this method is effective for the recognition of SL movement trajectory, and the accuracy of trajectory recognition is 86.25%. The difference ratio between the inter-class features and intra-class features of the movement trajectory is 20, and the generalization ability of the algorithm can be effectively improved.
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Al-Dulaimi, Al-Waled, Todd K. Moon, and Jacob H. Gunther. "Voice Transformation Using Two-Level Dynamic Warping and Neural Networks." Signals 2, no. 3 (July 14, 2021): 456–74. http://dx.doi.org/10.3390/signals2030028.

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Voice transformation, for example, from a male speaker to a female speaker, is achieved here using a two-level dynamic warping algorithm in conjunction with an artificial neural network. An outer warping process which temporally aligns blocks of speech (dynamic time warp, DTW) invokes an inner warping process, which spectrally aligns based on magnitude spectra (dynamic frequency warp, DFW). The mapping function produced by inner dynamic frequency warp is used to move spectral information from a source speaker to a target speaker. Artifacts arising from this amplitude spectral mapping are reduced by reconstructing phase information. Information obtained by this process is used to train an artificial neural network to produce spectral warping information based on spectral input data. The performance of the speech mapping compared using Mel-Cepstral Distortion (MCD) with previous voice transformation research, and it is shown to perform better than other methods, based on their reported MCD scores.
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Yazid, Haniza, Hamzah Arof, Hafizal Yazid, and Norazian Abd Razak. "Weld Detect Identification Using Texture Features and Dynamic Time Warping." Applied Mechanics and Materials 752-753 (April 2015): 1045–50. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.1045.

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In this paper, a simple yet robust algorithm for texture identification using 1 Dimensional Discrete Fourier Transform (1-D DFT) and Dynamic Time Warping (DTW) is presented with illumination variations. In the first stage, several image processing techniques namely Fuzzy C means (FCM) clustering, edge detection, Otsu thresholding and inverse surface thresholding method are utilized to locate the region of interest (ROI) where defects might exist. Next, the image undergoes the feature extraction process using 1-D DFT and finally, the features are classified using DTW. Several defect images consist of 2 types of defect namely the porosity and crack are experimented and classified using the DTW.
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Choi, Hyo-Rim, and TaeYong Kim. "Modified Dynamic Time Warping Based on Direction Similarity for Fast Gesture Recognition." Mathematical Problems in Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/2404089.

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We propose a modified dynamic time warping (DTW) algorithm that compares gesture-position sequences based on the direction of the gestural movement. Standard DTW does not specifically consider the two-dimensional characteristic of the user’s movement. Therefore, in gesture recognition, the sequence comparison by standard DTW needs to be improved. The proposed gesture-recognition system compares the sequences of the input gesture’s position with gesture positions saved in the database and selects the most similar gesture by filtering out unrelated gestures. The suggested algorithm uses the cosine similarity of the movement direction at each moment to calculate the difference and reflects the characteristics of the gesture movement by using the ratio of the Euclidean distance and the proportional distance to the calculated difference. Selective spline interpolation assists in solving the issue of recognition-decline at instances of gestures. Through experiments with public databases (MSRC-12 and G3D), the suggested algorithm revealed an improved performance on both databases compared to other methods.
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Liu, Mingqin, Xiaoguang Zhang, and Guiyun Xu. "Continuous Motion Classification and Segmentation Based on Improved Dynamic Time Warping Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 02 (November 12, 2017): 1850002. http://dx.doi.org/10.1142/s0218001418500027.

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The continuous image sequence recognition is more difficult than the single image recognition because the classification of continuous image sequences and the image edge recognition must be very accurate. Hence, a method based on sequence alignment for action segmentation and classification is proposed to reconstruct a template sequence by estimating the mean action of a class category, which calculates the distance between a single image and a template sequence by sparse coding in Dynamic Time Warping. The proposed method, the methods of Kulkarni et al. [Continuous action recognition based on sequence alignment, Int. J. Comput. Vis. pp. 1–26.] and Hoai et al. [Joint segmentation and classification of human actions in video, IEEE Conf. Computer Vision and Pattern Recognition, 2008, pp. 108–119.] are compared on the recognition accuracy of the continuous recognition and isolated recognition, which clearly shows that the proposed method outperforms the other methods. When applied to continuous gesture classification, it not only can recognize the gesture categories more quickly and accurately, but is more realistic in solving continuous action recognition problems in a video than the other existing methods.
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Dijkstra, E., and C. Piguet. "On minimizing memory in systolic arrays for the dynamic time warping algorithm." Integration 4, no. 2 (June 1986): 155–73. http://dx.doi.org/10.1016/s0167-9260(86)80005-0.

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Zhao, Chen Xi, Jin Wu Xu, Min Li, and Jian Hong Yang. "Time Delay Estimation on COREX Parameters Based on Dynamic Time Warping Method." Applied Mechanics and Materials 241-244 (December 2012): 1168–75. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1168.

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In order to solve the time delay problem between the process parameters and the quality indicators in the modeling processes, a method of time delay estimation on COREX parameters is proposed based on Dynamic Time Warping (DTW) algorithm. The method solves the problem existing in the conventional methods which demand the number of calculating sample to be same. Taking the real field data from Baosteel COREX-3000 as the research object, the DTW distances between the process parameters and the quality indicators are calculated, and then the delay time is estimated. The real field data are used for verification, the results show that the proposed method can estimate the time daley effectively, and the prediction accuracy of model which used time delay estimation becomes higher. It provides an effective measure for model preprocessing.
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Jiang, Yihang, Yuankai Qi, Will Ke Wang, Brinnae Bent, Robert Avram, Jeffrey Olgin, and Jessilyn Dunn. "EventDTW: An Improved Dynamic Time Warping Algorithm for Aligning Biomedical Signals of Nonuniform Sampling Frequencies." Sensors 20, no. 9 (May 9, 2020): 2700. http://dx.doi.org/10.3390/s20092700.

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The dynamic time warping (DTW) algorithm is widely used in pattern matching and sequence alignment tasks, including speech recognition and time series clustering. However, DTW algorithms perform poorly when aligning sequences of uneven sampling frequencies. This makes it difficult to apply DTW to practical problems, such as aligning signals that are recorded simultaneously by sensors with different, uneven, and dynamic sampling frequencies. As multi-modal sensing technologies become increasingly popular, it is necessary to develop methods for high quality alignment of such signals. Here we propose a DTW algorithm called EventDTW which uses information propagated from defined events as basis for path matching and hence sequence alignment. We have developed two metrics, the error rate (ER) and the singularity score (SS), to define and evaluate alignment quality and to enable comparison of performance across DTW algorithms. We demonstrate the utility of these metrics on 84 publicly-available signals in addition to our own multi-modal biomedical signals. EventDTW outperformed existing DTW algorithms for optimal alignment of signals with different sampling frequencies in 37% of artificial signal alignment tasks and 76% of real-world signal alignment tasks.
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Zhiwu, Shang, Yu Yan, Geng Rui, Gao Maosheng, and Li Wanxiang. "Research on a feature extraction method for local faults in planetary gearboxes based on improved dynamic time warping." Insight - Non-Destructive Testing and Condition Monitoring 63, no. 8 (August 1, 2021): 465–71. http://dx.doi.org/10.1784/insi.2021.63.8.465.

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Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.
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46

Vash, Yurii, Mariana Rol, and Mykola Chyzhmar. "Accelerating dynamic time warping for speech recognition with SSE." Scientific journal of the Ternopil national technical university 114, no. 2 (2024): 30–38. http://dx.doi.org/10.33108/visnyk_tntu2024.02.030.

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This study presents a significant enhancement to the Dynamic Time Warping (DTW) algorithm for real-time applications like speech recognition. Through integration of SIMD (Single Instruction Multiple Data) instructions to distance function, the research demonstrates how SSE accelerates DTW, markedly reducing computation time. The paper not only explores the theoretical aspects of DTW and this optimization but also provides empirical evidence of its effectiveness. Diverse dataset of 18 voice command classes was assembled, recorded in controlled settings to ensure audio quality. The audio signal of each speech sample was segmented into frames for detailed analysis of temporal dynamics. DTW search was performed on features set based on Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC), combined with delta features. A comprehensive set of 27 features was extracted from each frame to capture critical speech characteristics. The core of the study involved applying traditional DTW as a baseline for performance comparison with the SSE-optimized DTW. The evaluation, focusing on computational time, included measurements like minimum, maximum, average, and total computation times for both standard and SSE-optimized implementations. Experimental results, conducted on datasets ranging from 5 to 60 WAV files per class, revealed that the SSE-optimized DTW significantly outperformed the standard implementation across all dataset sizes. Particularly noteworthy was the consistent speed of the SSE-optimized Manhattan and Euclidean distance functions, which is crucial for real-time applications. The SSE-optimized DTW maintained a low average time, demonstrating remarkable stability and efficiency, especially with larger datasets. The study illustrates the potential of SSE optimizations in speech recognition, emphasizing the SSE-optimized DTW's capability to efficiently process large datasets.
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Ding, Lei, Yong Jun Luo, Yang Yang Wang, Zheng Li, and Bing Yin Yao. "Based on EADTW On-Line Handwriting Signature Handwriting Signature Verification System Design and Implementation." Applied Mechanics and Materials 556-562 (May 2014): 5902–5. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5902.

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In view of poor accuracy and slow calculation of the traditional on-line handwriting signature verification, an on-line handwriting signature verification based on Early Abandon Dynamic Time Warping (EADTW) was designed and implemented after numerous researched. The training template followed the mechanism of benchmark signature, while the certification part adopted EADTW algorithm. The experimental results showed that compared with on-line handwritten signature system based on DTW (dynamic time warping), this new system not only greatly reduced cumbersome and repeated calculation, but also obviously improved the accuracy, The bigger the test sample is, the more obvious the advantage is.
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Filippou, Valeria, Michael R. Backhouse, Anthony C. Redmond, and David C. Wong. "Person-Specific Template Matching Using a Dynamic Time Warping Step-Count Algorithm for Multiple Walking Activities." Sensors 23, no. 22 (November 9, 2023): 9061. http://dx.doi.org/10.3390/s23229061.

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This study aimed to develop and evaluate a new step-count algorithm, StepMatchDTWBA, for the accurate measurement of physical activity using wearable devices in both healthy and pathological populations. We conducted a study with 30 healthy volunteers wearing a wrist-worn MOX accelerometer (Maastricht Instruments, NL). The StepMatchDTWBA algorithm used dynamic time warping (DTW) barycentre averaging to create personalised templates for representative steps, accounting for individual walking variations. DTW was then used to measure the similarity between the template and accelerometer epoch. The StepMatchDTWBA algorithm had an average root-mean-square error of 2 steps for healthy gaits and 12 steps for simulated pathological gaits over a distance of about 10 m (GAITRite walkway) and one flight of stairs. It outperformed benchmark algorithms for the simulated pathological population, showcasing the potential for improved accuracy in personalised step counting for pathological populations. The StepMatchDTWBA algorithm represents a significant advancement in accurate step counting for both healthy and pathological populations. This development holds promise for creating more precise and personalised activity monitoring systems, benefiting various health and wellness applications.
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Gordić, Zaviša, and Kosta Jovanović. "Collision Detection on Industrial Robots in Repetitive Tasks Using Modified Dynamic Time Warping." Robotica 38, no. 10 (October 8, 2019): 1717–36. http://dx.doi.org/10.1017/s0263574719001425.

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SUMMARYThis paper presents a non-model-based collision detection algorithm for robots without external sensors and with closed control architecture. A reference signal of repetitive motion is recorded from the robot operation. To detect collisions, the reference is compared with measurements from the robot. One of the key contributions is a novel approach to optimal matching of compared signals, which is ensured by the newly developed modified Dynamic Time Warping (mDTW) method presented in this paper. One of the main improvements of the mDTW is that it enables comparing a signal with the most similar section of the other signal. Partial matching also enables online application of time warping principles and reduces the time and computation resources needed to perform matching. In addition to mDTW, two complementary decision rules are developed to identify collisions. The first rule, based on the absolute difference between compared matched samples, uses statistically determined thresholds to perform rapid detection of unambiguous collisions. The second rule is based on Eigen values of the covariance matrix of matched samples, and it employs its higher sensitivity to detect collisions with lower intensity. Results from experimental validation of the proposed collision algorithm on two industrial robots are shown.
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Yang, Xiong, Jiahao Guo, Xuhui Zhang, and Chenyang Zhu. "Terminal Waveform Similarity Measurement Method Based on the Improved Dynamic Time Warping Algorithm." Mathematical Problems in Engineering 2022 (November 16, 2022): 1–14. http://dx.doi.org/10.1155/2022/2180550.

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The dynamic time warping algorithm (DTW) has problems such as high computational complexity and “ill-conditioned matching.” Aiming at the above two main problems, this paper proposes an improved DTW algorithm for the final wave recording of the primary and secondary deep fusion equipment detection platform. The terminal recorded waveform and the waveform with non-Gaussian noise added as the research object, the two sets of waveforms are divided into frames and windowed, and the short-term energy entropy ratio of the two sets of waveforms is input into the DTW as the test vector. Using the optimal matching paths and distances of the two input vectors, the common substring lengths of the two sets of short-term energy entropy ratio sequences are calculated. Then, we define the optimal matching coefficient and correct the waveform similarity. The experimental data show that the improved DTW algorithm can accurately quantify the similarity between terminal waveforms, which can provide effective data support for the health status assessment of power distribution terminals.
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