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.

Pairwise curve synchronization for functional data

Abstract: SUMMARY Data collected by scientists are increasingly in the form of trajectories or curves. Often these can be viewed as realizations of a composite process driven by both amplitude and time variation. We consider the situation in which functional variation is dominated by time variation, and develop a curve-synchronization method that uses every trajectory in the sample as a reference to obtain pairwise warping functions in the first step. These initial pairwise warping functions are then used to create improved estimators of the underlying individual warping functions in the second step. A truncated averaging process is used to obtain robust estimation of individual warping functions. The method compares well with other available time-synchronization approaches and is illustrated with Berkeley growth data and gene expression data for multiple sclerosis.

A Novel Accelerometer-Based Gesture Recognition System

Abstract: In this paper, we address the problem of gesture recognition using the theory of random projection (RP) and by formulating the whole recognition problem as an l1-minimization problem. The gesture recognition system operates primarily on data from a single 3-axis accelerometer and comprises two main stages: a training stage and a testing stage. For training, the system employs dynamic time warping as well as affinity propagation to create exemplars for each gesture while for testing, the system projects all candidate traces and also the unknown trace onto the same lower dimensional subspace for recognition. A dictionary of 18 gestures is defined and a database of over 3700 traces is created from seven subjects on which the system is tested and evaluated. To the best of our knowledge, our dictionary of gestures is the largest in published studies related to acceleration-based gesture recognition. The system achieves almost perfect user-dependent recognition, and mixed-user and user-independent recognition accuracies that are highly competitive with systems based on statistical methods and with the other accelerometer-based gesture recognition systems available in the literature.

Discovering Characteristic Actions from On-Body Sensor Data

Abstract: We present an approach to activity discovery, the unsupervised identification and modeling of human actions embedded in a larger sensor stream. Activity discovery can be seen as the inverse of the activity recognition problem. Rather than learn models from hand-labeled sequences, we attempt to discover motifs, sets of similar subsequences within the raw sensor stream, without the benefit of labels or manual segmentation. These motifs are statistically unlikely and thus typically correspond to important or characteristic actions within the activity. The problem of activity discovery differs from typical motif discovery, such as locating protein binding sites, because of the nature of time series data representing human activity. For example, in activity data, motifs will tend to be sparsely distributed, vary in length, and may only exhibit intra-motif similarity after appropriate time warping. In this paper, we motivate the activity discovery problem and present our approach for efficient discovery of meaningful actions from sensor data representing human activity. We empirically evaluate the approach on an exercise data set captured by a wrist-mounted, three-axis inertial sensor. Our algorithm successfully discovers motifs that correspond to the real exercises with a recall rate of 96.3% and overall accuracy of 86.7% over six exercises and 864 occurrences.

Keyword recognition system and method

Abstract: A keyword recognition system for speaker dependent, dynamic time warping (DTW) recognition systems uses all of the trained word templates in the system, (keyword and vocabulary), to determine if an utterance is a keyword utterance or not. The utterance is selected as the keyword if a keyword score indicates a significant match to the keyword template and if the keyword score indicates a better match than do the entirety of scores to the vocabulary word templates.

Improving connected letter recognition by lipreading

Abstract: The authors show how recognition performance in automated speech perception can be significantly improved by additional lipreading, so called speech-reading. They show this on an extension of a state-of-the-art speech recognition system, a modular multistage time delay neural network architecture (MS-TDNN). The acoustic and visual speech data are preclassified in two separate front-end phoneme TDNNs and combined with acoustic-visual hypotheses for the dynamic time warping algorithm. This is shown on a connected word recognition problem, the notoriously difficult letter spelling task. With speech-reading, the error rate could be reduced by up to half of the error rate of the pure acoustic recognition. >