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.

TimeNet: Pre-trained deep recurrent neural network for time series classification

Abstract: Inspired by the tremendous success of deep Convolutional Neural Networks as generic feature extractors for images, we propose TimeNet: a deep recurrent neural network (RNN) trained on diverse time series in an unsupervised manner using sequence to sequence (seq2seq) models to extract features from time series. Rather than relying on data from the problem domain, TimeNet attempts to generalize time series representation across domains by ingesting time series from several domains simultaneously. Once trained, TimeNet can be used as a generic off-the-shelf feature extractor for time series. The representations or embeddings given by a pre-trained TimeNet are found to be useful for time series classification (TSC). For several publicly available datasets from UCR TSC Archive and an industrial telematics sensor data from vehicles, we observe that a classifier learned over the TimeNet embeddings yields significantly better performance compared to (i) a classifier learned over the embeddings given by a domain-specific RNN, as well as (ii) a nearest neighbor classifier based on Dynamic Time Warping.

Identification of Individual Walking Patterns Using Gait Acceleration

Abstract: This paper presents an improved approach on identifying users based on three-dimensional gait acceleration signal characteristics produced by walking. When the user carries the wearable gait acceleration acquiring system, acceleration signals are registered by the accelerometer. Through dividing the signals into gait cycles, gait feature code which represents the walking pattern of the user can be extracted. Recognition is based on the general idea of template matching. We use dynamic time warping (DTW) algorithm for matching so that non-linear time normalization could be used to dispose the problems resulted from naturally occurring changes in walking speed. Experiments were performed on 35 healthy subjects walking on their normal speed; Equal Error Rate of 6.7% was achieved. Our preliminary experiments confirm the possibility of recognizing users based on their gait acceleration.

Automatic recognition of facial expressions using hidden markov models and estimation of expression intensity

Abstract: Facial expressions provide sensitive cues about emotional responses and play a major role in the study of psychological phenomena and the development of nonverbal communication. Facial expressions regulate social behavior, signal communicative intent, and are related to speech production. Most facial expression recognition systems focus on only six basic expressions. In everyday life, however, these six basic expressions occur relatively infrequently, and emotion or intent is more often communicated by subtle changes in one or two discrete features, such as tightening of the lips which may communicate anger. Humans are capable of producing thousands of expressions that vary in complexity, intensity, and meaning. The objective of this dissertation is to develop a computer vision system, including both facial feature extraction and recognition, that automatically discriminates among, subtly different facial expressions based on Facial Action Coding System (FACS) action units (AUs) using Hidden Markov Models (HMMs). Three methods are developed to extract facial expression information for automatic recognition. The first method is facial feature point tracking using the coarse-to-fine pyramid method, which can be sensitive to subtle feature motion and is capable to handle large displacements with sub-pixel accuracy. The second is dense flow tracking together with principal component analysis, where the entire facial motion information per frame is compressed to a low-dimensional weight vector for discrimination. And the third is high gradient component (i.e., furrow) analysis in the spatio-temporal domain, which exploits the transient variance associated with the facial expression. Upon extraction of the facial information, non-rigid facial expressions are separated from the rigid head motion components, and the face images are automatically aligned and normalized using an affine transformation. The resulting motion vector sequence is vector quantized to provide input to an HMM-based classifier, which addresses the time warping problem. A method is developed for determining the HMM topology optimal for our recognition system. The system also provides expression intensity estimation, which has significant effect on the actual meaning of the expression. We have studied more than 400 image sequences obtained from 90 subjects. The experimental results of our trained system showed an overall recognition accuracy of 87%, and also 87% in distinguishing among sets of three and six subtly different facial expressions for upper and lower facial regions, respectively.

Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data

Abstract: The rapid development of information and communication technologies (ICTs) has provided rich resources for spatio-temporal data mining and knowledge discovery in modern societies. Previous research has focused on understanding aggregated urban mobility patterns based on mobile phone datasets, such as extracting activity hotspots and clusters. In this paper, we aim to go one step further from identifying aggregated mobility patterns. Using hourly time series we extract and represent the dynamic mobility patterns in different urban areas. A Dynamic Time Warping (DTW) algorithm is applied to measure the similarity between these time series, which also provides input for classifying different urban areas based on their mobility patterns. In addition, we investigate the outlier urban areas identified through abnormal mobility patterns. The results can be utilized by researchers and policy makers to understand the dynamic nature of different urban areas, as well as updating environmental and transportation policies.