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.

Recognizing wrist pulse waveforms with improved dynamic time warping algorithm

Abstract: This paper proposes an improved DTW algorithm to recognize pulse waveform. Our approach avoids the pathologic alignment that can be caused by original DTW algorithm and DDTW algorithm. Extensive experiments on 1000 pulse waveforms demonstrate the effect of our approach. Compared with original DTW and DDTW algorithms, our algorithm reduces the FRR and FAR greatly. By calculating the similarities between the pulse sample and the five pulse templates, our approach has 92.3% agreement rate with expert.

Spectro-Temporal Sparsity Characterization for Dysarthric Speech Detection

Abstract: To assist the clinical diagnosis and treatment of neurological diseases that cause speech dysarthria such as Parkinson's disease (PD), it is of paramount importance to craft robust features which can be used to automatically discriminate between healthy and dysarthric speech. Since dysarthric speech of patients suffering from PD is breathy, semi-whispery, and is characterized by abnormal pauses and imprecise articulation, it can be expected that its spectro-temporal sparsity differs from the spectro-temporal sparsity of healthy speech. While we have recently successfully used temporal sparsity characterization for dysarthric speech detection, characterizing spectral sparsity poses the challenge of constructing a valid feature vector from signals with a different number of unaligned time frames. Further, although several non-parametric and parametric measures of sparsity exist, it is unknown which sparsity measure yields the best performance in the context of dysarthric speech detection. The objective of this paper is to demonstrate the advantages of spectro-temporal sparsity characterization for automatic dysarthric speech detection. To this end, we first provide a numerical analysis of the suitability of different non-parametric and parametric measures (i.e., $l_1$ -norm, kurtosis, Shannon entropy, Gini index, shape parameter of a Chi distribution, and shape parameter of a Weibull distribution) for sparsity characterization. It is shown that kurtosis, the Gini index, and the parametric sparsity measures are advantageous sparsity measures, whereas the $l_1$ -norm and entropy measures fail to robustly characterize the temporal sparsity of signals with a different number of time frames. Second, we propose to characterize the spectral sparsity of an utterance by initially time-aligning it to the same utterance uttered by a (arbitrarily selected) reference speaker using dynamic time warping. Experimental results on a Spanish database of healthy and dysarthric speech show that estimating the spectro-temporal sparsity using the Gini index or the parametric sparsity measures and using it as a feature in a support vector machine results in a high classification accuracy of 83.3%.

A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance

Abstract: Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy.

Impact of sensor misplacement on dynamic time warping based human activity recognition using wearable computers

Abstract: Daily living activity monitoring is important for early detection of the onset of many diseases and for improving quality of life especially in elderly. A wireless wearable network of inertial sensor nodes can be used to observe daily motions. Continuous stream of data generated by these sensor networks can be used to recognize the movements of interest. Dynamic Time Warping (DTW) is a widely used signal processing method for time-series pattern matching because of its robustness to variations in time and speed as opposed to other template matching methods. Despite this flexibility, for the application of activity recognition, DTW can only find the similarity between the template of a movement and the incoming samples, when the location and orientation of the sensor remains unchanged. Due to this restriction, small sensor misplacements can lead to a decrease in the classification accuracy. In this work, we adopt DTW distance as a feature for real-time detection of human daily activities like sit to stand in the presence of sensor misplacement. To measure this performance of DTW, we need to create a large number of sensor configurations while the sensors are rotated or misplaced. Creating a large number of closely spaced sensors is impractical. To address this problem, we use the marker based optical motion capture system and generate simulated inertial sensor data for different locations and orientations on the body. We study the performance of the DTW under these conditions to determine the worst-case sensor location variations that the algorithm can accommodate.