• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.143-150
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• 2015

As smart devices get popular, research on gesture recognition using their embedded-accelerometer draw attention. As Dynamic Time Warping(DTW), recently, has been used to perform gesture recognition on data sequence from accelerometer, in this paper we propose Feature-Strengthened Gesture Recognition(FsGr) Model which can improve the recognition success rate when DTW is used. FsGr model defines feature-strengthened parts of data sequences to similar gestures which might produce unsuccessful recognition, and performs additional DTW on them to improve the recognition rate. In training phase, FsGr model identifies sets of similar gestures, and analyze features of gestures per each set. During recognition phase, it makes additional recognition attempt based on the result of feature analysis to improve the recognition success rate, when the result of first recognition attempt belongs to a set of similar gestures. We present the performance result of FsGr model, by experimenting the recognition of lower case alphabets.

• Journal of Korea Multimedia Society
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• v.21 no.9
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• pp.1119-1130
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• 2018

Writing letters or patterns on a virtual space by moving a person's gaze is called "eye writing," which is a promising tool for various human-computer interface applications. This paper investigates the use of conventional eye writing recognition algorithms for the purpose of user-independent recognition of eye-written characters. Two algorithms are presented to build the user-independent system: eye-written region extraction using wavelet coefficients and template generation. The experimental results of the proposed system demonstrated that with dynamic positional warping, an F1 score of 79.61% was achieved for 12 eye-written patterns, thereby indicating the possibility of user-independent use of eye writing.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.45-46
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• 2015

가전기기별 에너지 사용정보를 제공함으로써 가정에서 효율적인 에너지 사용을 유도할 수 있다. 가전기기별 사용정보를 효과적으로 제공하기 위해서는 NILM (Non-Intrusive Load Monitoring) 기법이 필요하다.본 논문에서는 개별 가전기기 분류단계에서 쓰이는 DTW(Dynamic Time Warping) 기법을 소개한다. DTW 기법은 다른 두 시계열 데이턴간의 유사도를 측정하는 패턴인식 기법 중 하나이다. 이 유사도를 이용하여 가전기기의 동작여부를 판별하고 분류를 수행한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.977-980
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• 2007

본 논문은 인식된 컨테이너 식별자 문자열과 컨테이너 작업리스트를 비교하여 작업리스트와 인식된 컨테이너 식별자 문자열을 매칭하는 효율적인 방법을 소개하고자 한다. Dynamic Time Warping 기법을 이용하여 오인식되거나 인식이 되지 않은 문자에 대하여 오독률을 최소화할 수 있는 효율적인 방법을 제안한다. 기존의 문자열 비교방식에 비하여 제안하는 방법을 사용하였을 경우 더 나은 성능을 보였다.

• Phonetics and Speech Sciences
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• v.14 no.1
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• pp.29-36
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• 2022

The English word-final /rl/ cluster poses a particular problem for Korean learners of English because it is the sequence of two sounds, /r/ and /l/, which are not contrastive in Korean. This study compared the similarity distances between English and Korean speakers' /rl/ productions using the dynamic time warping (DTW) algorithm. The words with /rl/ (pearl, world) and without /rl/ (bird, word) were recorded by four English speakers and four Korean speakers, and compared pairwise. The F2-F1 trajectories, the acoustic correlate of velarized /l/, and F3 trajectories, the acoustic correlate of /r/, were examined. Formant analysis showed that English speakers lowered F2-F1 values toward the end of a word, unlike Korean speakers, suggesting the absence of /l/ in Korean speakers. In contrast, there was no significant difference in F3 values. Mixed-effects regression analyses of the DTW distances revealed that Korean speakers produced /r/ similarly to English speakers but failed to produce the velarized /l/ in /rl/ clusters.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.851-876
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• 2018

Dynamic time warping (DTW) is the main algorithms for time series alignment. However, it is unsuitable for quasi-periodic time series. In the current situation, except the recently published the shape exchange algorithm (SEA) method and its derivatives, no other technique is able to handle alignment of this type of very complex time series. In this work, we propose a novel algorithm that combines the advantages of the SEA and the DTW methods. Our main contribution consists in the elevation of the DTW power of alignment from the lowest level (Class A, non-periodic time series) to the highest level (Class C, multiple-periods time series containing different number of periods each), according to the recent classification of time series alignment methods proposed by Boucheham (Int J Mach Learn Cybern, vol. 4, no. 5, pp. 537-550, 2013). The new method (quasi-periodic dynamic time warping [QP-DTW]) was compared to both SEA and DTW methods on electrocardiogram (ECG) time series, selected from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) public database and from the PTB Diagnostic ECG Database. Results show that the proposed algorithm is more effective than DTW and SEA in terms of alignment accuracy on both qualitative and quantitative levels. Therefore, QP-DTW would potentially be more suitable for many applications related to time series (e.g., data mining, pattern recognition, search/retrieval, motif discovery, classification, etc.).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1143-1149
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• 2010

In this study, we propose a novel method to detect and enhance the feature of ST-segment which offers the crucial information for the diagnosis of myocardial infarction and ischemia. With this aim, PQRST features of Electrocardiogram initially are detected and subsequently ST-segment are estimated. And Dynamic Time Warping(DTW) transformation is applied recursively to minimize the difference in time between ST-segments and calculate the minimum cumulative distance that decides the degree of similarity among ST-segments. As of the results, the inherent characteristic of ST-segment can be emphasized in terms of time parameter and thus the diagnostic features of a ST-segment can be revealed further.

• International Journal of Contents
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• v.7 no.1
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• pp.14-22
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• 2011

Temporal Sign Language Recognition (TSLR) from hand motion is an active area of gesture recognition research in facilitating efficient communication with deaf people. TSLR systems consist of two stages: a motion sensing step which extracts useful features from signers' motion and a classification process which classifies these features as a performed sign. This work focuses on two of the research problems, namely unknown time varying signal of sign languages in feature extraction stage and computing complexity and time consumption in classification stage due to a very large sign sequences database. In this paper, we propose a combination of Dynamic Time Warping (DTW) and application of the Single hidden Layer Feedforward Neural networks (SLFNs) trained by Extreme Learning Machine (ELM) to cope the limitations. DTW has several advantages over other approaches in that it can align the length of the time series data to a same prior size, while ELM is a useful technique for classifying these warped features. Our experiment demonstrates the efficiency of the proposed method with the recognition accuracy up to 98.67%. The proposed approach can be generalized to more detailed measurements so as to recognize hand gestures, body motion and facial expression.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.665-670
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• 2006

This paper deals with a modified method of the dynamic time warping and feature points to extract various important information of the signature for the dynamic signature verification. We could achieve lower equal error rate, small and efficient feature points and fast processing time for the notification.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.329-338
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• 2017

This study attempts to classify 1008 alley markets in Seoul through cluster analysis using Dynamic Time Warping, one of the methods used to analyze the similarity of time series, and evaluate the possibility of opening new stores. The sequence of the gross sales of an alley market and that of gross sales per store stand for the potential of growth and profitability of the market, respectively and are used as variables for cluster analysis. Five clusters are obtained for the gross sales and four clusters for the gross sales per store. These two types of clusters are again classified as rising and falling trends, respectively, and the combination of these trends produces four categories. These categories are used to evaluate the possibility of opening new stores in alley markets. The results show that the southeast which is relatively wealthy inferior to other regions in opening new stores. Alley markets in the northeast and the southwest are better than other regions such that opening a new store is justified. In the northwest, there are many markets with trend of gross sales and that of gross sales per store moving in opposite directions, and new store openings in these markets should be postponed.