Early detection of health changes in the elderly using in-home multi-sensor data streams

The rapid aging of the population worldwide requires increased attention from health care providers and the entire society. For the elderly to live independently, many health issues related to old age, such as frailty and risk of falling, need increased attention and monitoring. When monitoring daily routines for older adults, it is desirable to detect the early signs of health changes before serious health events, such as hospitalizations, happen, so that timely and adequate preventive care may be provided. By deploying multi-sensor systems in homes of the elderly, we can track trajectories of daily behaviors in a feature space defined using the sensor data. In this work, we investigate a methodology for learning data distribution from streaming data and tracking the evolution of the behavior trajectories over long periods (years) using high dimensional streaming clustering and provide very early indicators of changes in health. If we assume that habitual behaviors correspond to clusters in feature space and diseases produce a change in behavior, albeit not highly specific, tracking trajectory deviations can provide hints of early illness. Retrospectively, we visualize the streaming clustering results and track how the behavior clusters evolve in feature space with the help of two dimension-reduction algorithms, Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). Moreover, our tracking algorithm in the original high dimensional feature space generates early health warning alerts if a negative trend is detected in the behavior trajectory. We validated our algorithm on synthetic data, real-world data and tested it on a pilot dataset of four TigerPlace residents monitored with a collection of motion, bed, and depth sensors over ten years. We used the TigerPlace electronic health records (EHR) to understand the residents' behavior patterns and to evaluate and explain the health warnings generated by our algorithm. The results obtained on the TigerPlace dataset show that most of the warnings produced by our algorithm can be linked to health events documented in the EHR, providing strong support for a prospective deployment of the approach.Includes bibliographical references

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

Building environmentally-aware classifiers on streaming data

The three biggest challenges currently faced in machine learning, in our estimation, are the staggering quantity of data we wish to analyze, the incredibly small proportion of these data that are labeled, and the apparent lack of interest in creating algorithms that continually learn during inference. An unsupervised streaming approach addresses all three of these challenges, storing only a finite amount of information to model an unbounded dataset and adapting to new structures as they arise. Specifically, we are motivated by automated target recognition (ATR) in synthetic aperture sonar (SAS) imagery, the problem of finding explosive hazards on the sea oor. It has been shown that the performance of ATR can be improved by, instead of using a single classifier for the entire ATR task, creating several specialized classifers and fusing their predictions [44]. The prevailing opinion seems be that one should have different classifiers for varying complexity of sea oor [74], but we hypothesize that fusing classifiers based on sea bottom type will yield higher accuracy and better lend itself to making explainable classification decisions. The first step of building such a system is developing a robust framework for online texture classification, the topic of this research. xi In this work, we improve upon StreamSoNG [85], an existing algorithm for streaming data analysis (SDA) that models each structure in the data with a neural gas [69] and detects new structures by clustering an outlier list with the possibilistic 1-means [62] (P1M) algorithm. We call the modified algorithm StreamSoNGv2, denoting that it is the second version, or verse, if you will, of StreamSoNG. Notable improvements include detection of arbitrarily-shaped clusters by using DBSCAN [37] instead of P1M, using growing neural gas [43] to model each structure with an adaptive number of prototypes, and an automated approach to estimate the n parameters. Furthermore, we propose a novel algorithm called single-pass possibilistic clustering (SPC) for solving the same task. SPC maintains a fixed number of structures to model the data stream. These structures can be updated and merged based only on their "footprints", that is, summary statistics that contain all of the information from the stream needed by the algorithm without directly maintaining the entire stream. SPC is built on a damped window framework, allowing the user to balance the weight between old and new points in the stream with a decay factor parameter. We evaluate the two algorithms under consideration against four state of the art SDA algorithms from the literature on several synthetic datasets and two texture datasets: one real (KTH-TIPS2b [68]) and xii one simulated. The simulated dataset, a significant research effort in itself, is of our own construction in Unreal Engine and contains on the order of 6,000 images at 720 x 720 resolution from six different texture types. Our hope is that the methodology developed here will be effective texture classifiers for use not only in underwater scene understanding, but also in improving performance of ATR algorithms by providing a context in which the potential target is embedded.Includes bibliographical references

Unsupervised tracking of time-evolving data streams and an application to short-term urban traffic flow forecasting

I am indebted to many people for their help and support I receive during my Ph.D. study and research at DIBRIS-University of Genoa. First and foremost, I would like to express my sincere thanks to my supervisors Prof.Dr. Masulli, and Prof.Dr. Rovetta for the invaluable guidance, frequent meetings, and discussions, and the encouragement and support on my way of research. I thanks all the members of the DIBRIS for their support and kindness during my 4 years Ph.D. I would like also to acknowledge the contribution of the projects Piattaforma per la mobili\ue0 Urbana con Gestione delle INformazioni da sorgenti eterogenee (PLUG-IN) and COST Action IC1406 High Performance Modelling and Simulation for Big Data Applications (cHiPSet). Last and most importantly, I wish to thanks my family: my wife Shaimaa who stays with me through the joys and pains; my daughter and son whom gives me happiness every-day; and my parents for their constant love and encouragement

Speech data analysis for semantic indexing of video of simulated medical crises.

The Simulation for Pediatric Assessment, Resuscitation, and Communication (SPARC) group within the Department of Pediatrics at the University of Louisville, was established to enhance the care of children by using simulation based educational methodologies to improve patient safety and strengthen clinician-patient interactions. After each simulation session, the physician must manually review and annotate the recordings and then debrief the trainees. The physician responsible for the simulation has recorded 100s of videos, and is seeking solutions that can automate the process. This dissertation introduces our developed system for efficient segmentation and semantic indexing of videos of medical simulations using machine learning methods. It provides the physician with automated tools to review important sections of the simulation by identifying who spoke, when and what was his/her emotion. Only audio information is extracted and analyzed because the quality of the image recording is low and the visual environment is static for most parts. Our proposed system includes four main components: preprocessing, speaker segmentation, speaker identification, and emotion recognition. The preprocessing consists of first extracting the audio component from the video recording. Then, extracting various low-level audio features to detect and remove silence segments. We investigate and compare two different approaches for this task. The first one is threshold-based and the second one is classification-based. The second main component of the proposed system consists of detecting speaker changing points for the purpose of segmenting the audio stream. We propose two fusion methods for this task. The speaker identification and emotion recognition components of our system are designed to provide users the capability to browse the video and retrieve shots that identify ”who spoke, when, and the speaker’s emotion” for further analysis. For this component, we propose two feature representation methods that map audio segments of arbitary length to a feature vector with fixed dimensions. The first one is based on soft bag-of-word (BoW) feature representations. In particular, we define three types of BoW that are based on crisp, fuzzy, and possibilistic voting. The second feature representation is a generalization of the BoW and is based on Fisher Vector (FV). FV uses the Fisher Kernel principle and combines the benefits of generative and discriminative approaches. The proposed feature representations are used within two learning frameworks. The first one is supervised learning and assumes that a large collection of labeled training data is available. Within this framework, we use standard classifiers including K-nearest neighbor (K-NN), support vector machine (SVM), and Naive Bayes. The second framework is based on semi-supervised learning where only a limited amount of labeled training samples are available. We use an approach that is based on label propagation. Our proposed algorithms were evaluated using 15 medical simulation sessions. The results were analyzed and compared to those obtained using state-of-the-art algorithms. We show that our proposed speech segmentation fusion algorithms and feature mappings outperform existing methods. We also integrated all proposed algorithms and developed a GUI prototype system for subjective evaluation. This prototype processes medical simulation video and provides the user with a visual summary of the different speech segments. It also allows the user to browse videos and retrieve scenes that provide answers to semantic queries such as: who spoke and when; who interrupted who? and what was the emotion of the speaker? The GUI prototype can also provide summary statistics of each simulation video. Examples include: for how long did each person spoke? What is the longest uninterrupted speech segment? Is there an unusual large number of pauses within the speech segment of a given speaker

Context dependent spectral unmixing.

A hyperspectral unmixing algorithm that finds multiple sets of endmembers is proposed. The algorithm, called Context Dependent Spectral Unmixing (CDSU), is a local approach that adapts the unmixing to different regions of the spectral space. It is based on a novel function that combines context identification and unmixing. This joint objective function models contexts as compact clusters and uses the linear mixing model as the basis for unmixing. Several variations of the CDSU, that provide additional desirable features, are also proposed. First, the Context Dependent Spectral unmixing using the Mahalanobis Distance (CDSUM) offers the advantage of identifying non-spherical clusters in the high dimensional spectral space. Second, the Cluster and Proportion Constrained Multi-Model Unmixing (CC-MMU and PC-MMU) algorithms use partial supervision information, in the form of cluster or proportion constraints, to guide the search process and narrow the space of possible solutions. The supervision information could be provided by an expert, generated by analyzing the consensus of multiple unmixing algorithms, or extracted from co-located data from a different sensor. Third, the Robust Context Dependent Spectral Unmixing (RCDSU) introduces possibilistic memberships into the objective function to reduce the effect of noise and outliers in the data. Finally, the Unsupervised Robust Context Dependent Spectral Unmixing (U-RCDSU) algorithm learns the optimal number of contexts in an unsupervised way. The performance of each algorithm is evaluated using synthetic and real data. We show that the proposed methods can identify meaningful and coherent contexts, and appropriate endmembers within each context. The second main contribution of this thesis is consensus unmixing. This approach exploits the diversity and similarity of the large number of existing unmixing algorithms to identify an accurate and consistent set of endmembers in the data. We run multiple unmixing algorithms using different parameters, and combine the resulting unmixing ensemble using consensus analysis. The extracted endmembers will be the ones that have a consensus among the multiple runs. The third main contribution consists of developing subpixel target detectors that rely on the proposed CDSU algorithms to adapt target detection algorithms to different contexts. A local detection statistic is computed for each context and then all scores are combined to yield a final detection score. The context dependent unmixing provides a better background description and limits target leakage, which are two essential properties for target detection algorithms

Biclustering on expression data: A review

Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. In such cases, the development of both a suitable heuristics and a good measure for guiding the search are essential for discovering interesting biclusters in an expression matrix. Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters. There exists a diverse set of biclustering tools that follow different strategies and algorithmic concepts which guide the search towards meaningful results. In this paper we present a extensive survey of biclustering approaches, classifying them into two categories according to whether or not use evaluation metrics within the search method: biclustering algorithms based on evaluation measures and non metric-based biclustering algorithms. In both cases, they have been classified according to the type of meta-heuristics which they are based on.Ministerio de Economía y Competitividad TIN2011-2895

Outline of a new feature space deformation approach in fuzzy pattern recognition

Sposobnost prepoznavanja oblika je jedno od najznačajnijih svojstava koja karakterišu inteligentno ponašanje bioloških ili veštačkih sistema. Matematičko prepoznavanje oblika predstavlja formalnu osnovu za rešavanje ovog zadatka primenom precizno forumulisanih algoritama, koji su u najvećem delu bazirni na konvencionalnoj matematici. Kod kompleksnih sistema ovakav pristup pokazuje značajne nedostatke, prvenstveno zbog zahteva za obimnim izračunavanjima i nedovoljne robusnosti. Algoritmi koji su bazirani na 'soft computing' metodama predstavljaju dobru alternativu, otvarajući prostor za razvoj efikasnih algoritama za primenu u realnom vremenu, polazeći od činjenice da značenje sadržaja informacija nosi veću vrednost u odnosu na preciznost. U ovom radu izlaže se modifikacija i proširenje 'Subrtactive Clustering' metode, koja se pokazala efikasnom u obradi masivnih skupova oblika u realnom vremenu. Novi pristup koji je baziran prvenstveno na povezivanju parametara algoritma sa informacionim sadržajem prisutnim u skupu oblika koji se obrađuje, daje dodatne stepene slobode i omogućava da proces prepoznavanja bude vođen podacima koji se obrađuju. Predloženi algoritam je verifikovan velikim brojem simulacionih eksperimenata, od kojih su neki navedeni u ovom radu.Pattern recognition ability is one of the most important features that characterize intelligent behavior of either biological or artificial systems. Mathematical pattern recognition is the way to solve this problem using transparent algorithms that are mostly based on conventional mathematics. In complex systems it shows inadequacy, primary due to the needs for extensive computation and insufficient robustness. Algorithms based on soft computing approach offer a good alternative, giving a room to design effective tools for real-time application, having in mind that relevance (significance) prevails precision in complex systems. In this article is modified and extended subtractive clustering method, which is proven to be effective in real-time applications, when massive pattern sets is processed. The new understanding and new relations that connect parameters of the algorithm with the information underlying the pattern set are established, giving on this way the algorithm ability to be data driven to the maximum extent. Proposed algorithm is verified by a number of experiments and few of them are presented in this article