589 research outputs found
Adversarial Unsupervised Representation Learning for Activity Time-Series
Sufficient physical activity and restful sleep play a major role in the
prevention and cure of many chronic conditions. Being able to proactively
screen and monitor such chronic conditions would be a big step forward for
overall health. The rapid increase in the popularity of wearable devices
provides a significant new source, making it possible to track the user's
lifestyle real-time. In this paper, we propose a novel unsupervised
representation learning technique called activity2vec that learns and
"summarizes" the discrete-valued activity time-series. It learns the
representations with three components: (i) the co-occurrence and magnitude of
the activity levels in a time-segment, (ii) neighboring context of the
time-segment, and (iii) promoting subject-invariance with adversarial training.
We evaluate our method on four disorder prediction tasks using linear
classifiers. Empirical evaluation demonstrates that our proposed method scales
and performs better than many strong baselines. The adversarial regime helps
improve the generalizability of our representations by promoting subject
invariant features. We also show that using the representations at the level of
a day works the best since human activity is structured in terms of daily
routinesComment: Accepted at AAAI'19. arXiv admin note: text overlap with
arXiv:1712.0952
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Statistical and Dynamical Modeling of Riemannian Trajectories with Application to Human Movement Analysis
abstract: The data explosion in the past decade is in part due to the widespread use of rich sensors that measure various physical phenomenon -- gyroscopes that measure orientation in phones and fitness devices, the Microsoft Kinect which measures depth information, etc. A typical application requires inferring the underlying physical phenomenon from data, which is done using machine learning. A fundamental assumption in training models is that the data is Euclidean, i.e. the metric is the standard Euclidean distance governed by the L-2 norm. However in many cases this assumption is violated, when the data lies on non Euclidean spaces such as Riemannian manifolds. While the underlying geometry accounts for the non-linearity, accurate analysis of human activity also requires temporal information to be taken into account. Human movement has a natural interpretation as a trajectory on the underlying feature manifold, as it evolves smoothly in time. A commonly occurring theme in many emerging problems is the need to \emph{represent, compare, and manipulate} such trajectories in a manner that respects the geometric constraints. This dissertation is a comprehensive treatise on modeling Riemannian trajectories to understand and exploit their statistical and dynamical properties. Such properties allow us to formulate novel representations for Riemannian trajectories. For example, the physical constraints on human movement are rarely considered, which results in an unnecessarily large space of features, making search, classification and other applications more complicated. Exploiting statistical properties can help us understand the \emph{true} space of such trajectories. In applications such as stroke rehabilitation where there is a need to differentiate between very similar kinds of movement, dynamical properties can be much more effective. In this regard, we propose a generalization to the Lyapunov exponent to Riemannian manifolds and show its effectiveness for human activity analysis. The theory developed in this thesis naturally leads to several benefits in areas such as data mining, compression, dimensionality reduction, classification, and regression.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Multi-Sensor Event Detection using Shape Histograms
Vehicular sensor data consists of multiple time-series arising from a number
of sensors. Using such multi-sensor data we would like to detect occurrences of
specific events that vehicles encounter, e.g., corresponding to particular
maneuvers that a vehicle makes or conditions that it encounters. Events are
characterized by similar waveform patterns re-appearing within one or more
sensors. Further such patterns can be of variable duration. In this work, we
propose a method for detecting such events in time-series data using a novel
feature descriptor motivated by similar ideas in image processing. We define
the shape histogram: a constant dimension descriptor that nevertheless captures
patterns of variable duration. We demonstrate the efficacy of using shape
histograms as features to detect events in an SVM-based, multi-sensor,
supervised learning scenario, i.e., multiple time-series are used to detect an
event. We present results on real-life vehicular sensor data and show that our
technique performs better than available pattern detection implementations on
our data, and that it can also be used to combine features from multiple
sensors resulting in better accuracy than using any single sensor. Since
previous work on pattern detection in time-series has been in the single series
context, we also present results using our technique on multiple standard
time-series datasets and show that it is the most versatile in terms of how it
ranks compared to other published results
Automatic Plane Pose Estimation for Cardiac Left Ventricle Coverage Estimation via Deep Adversarial Regression Network
Accurate segmentation of the ventricles plays a crucial role in determining cardiac functional parameters such as ventricular volume, ventricular mass, or ejection fraction. However, poor image quality, such as inadequate coverage of the left ventricle (LV) and right ventricle (RV) in cardiac magnetic resonance (CMR) image sequences, can significantly affect the assessment of cardiac function. This study investigates issues related to missing or corrupted imaging planes, which often lead to incomplete ventricle coverage. To address the challenge of estimating ventricle coverage in CMR images regardless of variations in imaging parameters such as device type, magnetic field strength, and protocol execution, we introduce a novel convolutional neural network (CNN) based on adversarial learning. Additionally, we integrate supplementary information (e.g., cross-view image data) as privileged information to enhance the interpretability of our model’s predictions and identify potential biases or inaccuracies. This research represents the first attempt to automatically estimate ventricular coverage by identifying missing slices and plane orientations in CMR images using a dataset-agnostic approach. The effectiveness of the proposed model is demonstrated through the evaluation of datasets from three diverse and sizable image acquisition cohorts, demonstrating superior performance compared to existing methods
Leveraging Image-based Generative Adversarial Networks for Time Series Generation
Generative models for images have gained significant attention in computer
vision and natural language processing due to their ability to generate
realistic samples from complex data distributions. To leverage the advances of
image-based generative models for the time series domain, we propose a
two-dimensional image representation for time series, the Extended
Intertemporal Return Plot (XIRP). Our approach captures the intertemporal time
series dynamics in a scale-invariant and invertible way, reducing training time
and improving sample quality. We benchmark synthetic XIRPs obtained by an
off-the-shelf Wasserstein GAN with gradient penalty (WGAN-GP) to other image
representations and models regarding similarity and predictive ability metrics.
Our novel, validated image representation for time series consistently and
significantly outperforms a state-of-the-art RNN-based generative model
regarding predictive ability. Further, we introduce an improved stochastic
inversion to substantially improve simulation quality regardless of the
representation and provide the prospect of transfer potentials in other
domains
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational Costs
Shapelets are discriminative time series subsequences that allow generation
of interpretable classification models, which provide faster and generally
better classification than the nearest neighbor approach. However, the shapelet
discovery process requires the evaluation of all possible subsequences of all
time series in the training set, making it extremely computation intensive.
Consequently, shapelet discovery for large time series datasets quickly becomes
intractable. A number of improvements have been proposed to reduce the training
time. These techniques use approximation or discretization and often lead to
reduced classification accuracy compared to the exact method.
We are proposing the use of ensembles of shapelet-based classifiers obtained
using random sampling of the shapelet candidates. Using random sampling reduces
the number of evaluated candidates and consequently the required computational
cost, while the classification accuracy of the resulting models is also not
significantly different than that of the exact algorithm. The combination of
randomized classifiers rectifies the inaccuracies of individual models because
of the diversity of the solutions. Based on the experiments performed, it is
shown that the proposed approach of using an ensemble of inexpensive
classifiers provides better classification accuracy compared to the exact
method at a significantly lesser computational cost
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