Hidden Markov Model Approach Research Articles

Powered Two-Wheeler Riding Pattern Recognition Using a Machine-Learning Framework

In this paper, a machine-learning framework is used for riding pattern recognition. The problem is formulated as a classification task to identify the class of riding patterns using data collected from 3-D accelerometer/gyroscope sensors mounted on motorcycles. These measurements constitute an experimental database used to analyze powered two-wheeler rider behavior. Several well-known machine-learning techniques are investigated, including the Gaussian mixture models, the $k$ -nearest neighbor model, the support vector machines, the random forests, and the hidden Markov models (HMMs), for both discrete and continuous cases. Additionally, an approach for sensor selection is proposed to identify the significant measurements for improved riding pattern recognition. The experimental study, performed on a real data set, shows the effectiveness of the proposed methodology and the effectiveness of the HMM approach in riding pattern recognition. These results encourage the development of these methodologies in the context of naturalistic riding studies.

Hidden Markov Model for Dependent Mark Loss and Survival Estimation

Mark-recapture estimators assume no loss of marks to provide unbiased estimates of population parameters. We describe a hidden Markov model (HMM) framework that integrates a mark loss model with a Cormack–Jolly–Seber model for survival estimation. Mark loss can be estimated with single-marked animals as long as a sub-sample of animals has a permanent mark. Double-marking provides an estimate of mark loss assuming independence but dependence can be modeled with a permanently marked sub-sample. We use a log-linear approach to include covariates for mark loss and dependence which is more flexible than existing published methods for integrated models. The HMM approach is demonstrated with a dataset of black bears (Ursus americanus) with two ear tags and a subset of which were permanently marked with tattoos. The data were analyzed with and without the tattoo. Dropping the tattoos resulted in estimates of survival that were reduced by 0.005–0.035 due to tag loss dependence that could not be modeled. We also analyzed the data with and without the tattoo using a single tag. By not using. Supplementary materials accompanying this paper appear on-line.

The carbohydrate-binding promiscuity of Euonymus europaeus lectin is predicted to involve a single binding site.

Euonymus europaeus lectin (EEL) is a carbohydrate-binding protein derived from the fruit of the European spindle tree. EEL was first identified for its erythrocyte agglutinating properties and specificity for B and H blood groups. However, a detailed molecular picture of the structural basis of carbohydrate recognition by EEL remains to be developed. In this study, we performed fluorescence titrations of a range of carbohydrates against EEL. Binding of EEL to a wide range of carbohydrates was observed, including a series of blood group-related carbohydrates, mannosides, chitotriose and sialic acid. Affinity was strongest for carbohydrates with H-related structures and the B trisaccharide. A homology model of EEL was produced from templates identified using the HHPred server, which employs hidden Markov models (HMMs) to identify templates. The HMM approach identified that the best templates for EEL were proteins featuring a ricin B-like (R-type) fold. Separate templates were used to model the core and binding site regions of the lectin. Through the use of constrained docking and spatial comparison with a template ligand, binding modes for the carbohydrate ligands were predicted. A relationship between the experimental binding energies and the computed binding energies of the selected docked poses was determined and optimized. Collectively, our results suggest that EEL utilizes a single site for recognition of carbohydrates terminating in a variety of monosaccharides.

An inverse Yarbus process: predicting observers' task from eye movement patterns.

In this paper we develop a probabilistic method to infer the visual-task of a viewer given measured eye movement trajectories. This method is based on the theory of hidden Markov models (HMM) that employs a first order Markov process to predict the coordinates of fixations given the task. The prediction confidence level of each task-dependent model is used in a Bayesian inference formulation, whereby the task with the maximum a posteriori (MAP) probability is selected. We applied this technique to a challenging dataset consisting of eye movement trajectories obtained from subjects viewing monochrome images of real scenes tasked with answering questions regarding the scenes. The results show that the HMM approach, combined with a clustering technique, can be a reliable way to infer visual-task from eye movements data.

Assessment of optimized Markov models in protein fold classification.

Protein fold classification is a challenging task strongly associated with the determination of proteins' structure. In this work, we tested an optimization strategy on a Markov chain and a recently introduced Hidden Markov Model (HMM) with reduced state-space topology. The proteins with unknown structure were scored against both these models. Then the derived scores were optimized following a local optimization method. The Protein Data Bank (PDB) and the annotation of the Structural Classification of Proteins (SCOP) database were used for the evaluation of the proposed methodology. The results demonstrated that the fold classification accuracy of the optimized HMM was substantially higher compared to that of the Markov chain or the reduced state-space HMM approaches. The proposed methodology achieved an accuracy of 41.4% on fold classification, while Sequence Alignment and Modeling (SAM), which was used for comparison, reached an accuracy of 38%.

A multi-dimensional Hidden Markov Model approach to automated identification of fetal cardiac valve motion.

Fetal cardiac assessment techniques are aimed to identify fetuses at risk of intrauterine compromise or death. Evaluation of the electromechanical coupling as a fundamental part of the fetal heart physiology, provides valuable information about the fetal wellbeing during pregnancy. It is based on the opening and closing time of the cardiac valves and the onset of the QRS complex of the fetal electrocardiogram (fECG). The focus of this paper is on the automated identification of the fetal cardiac valve opening and closing from Doppler Ultrasound signal and fECG as a reference. To this aim a novel combination of Emprical Mode Decomposition (EMD) and multi-dimensional Hidden Markov Models (MD-HMM) was employed which provided beat-to-beat estimation of cardiac valve event timings with improved precision (82.9%) compared to the one dimensional HMM (77.4%) and hybrid HMM-Support Vector Machine (SVM) (79.8%) approaches.

Keyword Recognition Based on MFCC

After about 50 years of development, speech recognition technology has been able to achieve large vocabulary, non-specific human continuous speech recognition system. On account of Chinese pronunciation features, we research the small vocabulary, non-specific Chinese speech recognition based on continuous Hidden Markov Model approach. With comparing the datasets of VQ/DTW, VQ/DHMM, CHMM state-1 recognition algorithm and CHMM state-2 recognition algorithm, the results of our experiment show that: (1) CHMM state-2 branch method performs primely in reduction of the recognition time; and (2) the recognition accuracy is improved eventually.

A Hidden Markov Model for short term prediction of traffic conditions on freeways

Accurate short-term prediction of traffic conditions on freeways and major arterials has recently become increasingly important because of its vital role in the basic traffic management functions and trip decision making processes. Given the dynamic and stochastic nature of freeway traffic, this study proposes a stochastic approach, Hidden Markov Model (HMM), for short-term freeway traffic prediction during peak periods. The data used in the study was gathered from real-time traffic monitoring devices over six years on a 60.8-km (38-mile) corridor of Interstate-4 in Orlando, Florida. The HMM defines traffic states in a two-dimensional space using first-order statistics (Mean) and second-order statistics (Contrast) of speed observations. The dynamic changes of freeway traffic conditions are addressed with state transition probabilities. For a sequence of traffic speed observations, HMMs estimate the most likely sequence of traffic states. The model performance was evaluated using prediction errors, which are measured by the relative length of the distance between the predicted state and the observed state in the two-dimensional space. Reasonable prediction errors lower than or around 10% were obtained from HMMs. Also, the model performance was not remarkably affected by location, travel direction, and peak period time. The HMMs were compared to two naïve predication methods. The results showed that HMMs perform better and are more robust than the naïve methods. Therefore, the study concludes that the HMM approach was successful in modeling short-term traffic condition prediction during peak periods and in accounting for the inherent stochastic nature of traffic conditions.

A Hidden Markov Model Approach to Information-Based Trading: Theory and Applications

A Hidden Markov Model Approach to Information-Based Trading: Theory and Applications

Development of a tool for computational prediction of σ70 promoters in Pseudomonas spp using SVM and HMM approaches

Promoters are regions in DNA that play important role in the regulation of gene expression. The ability to locate promoters within a section of DNA is known to be a very difficult and important task in DNA analysis. Since experimental techniques to identify promoters are costly and time consuming, in silico methods offer an alternative. In this study, we have developed a tool for identification of s70 promoters in the –10 and –35 regions of sequences from Pseudomonas spp. Promoters were predicted using both Support Vector Machine (SVM) and Hidden Markov Model (HMM) based approaches. SVM performed better when trained using RBF kernel with a cross-validation of 5 and a value of 0.03 for the gamma parameter. The module developed using SVM showed a sensitivity of 78% and a specificity of 80%. The programmes required to process the user input were written using Perl and HTML codes were used to create a user interface. The user interface accepts a query sequence and the processed result will be displayed in a new window. The tool named ‘PROMIT’ (PROMoter Identification Tool), was developed in the Windows platform, has a user friendly interface and works well for sequences from Pseudomonas spp.

Hidden Markov Model-Based CNV Detection Algorithms for Illumina Genotyping Microarrays.

Somatic alterations in DNA copy number have been well studied in numerous malignancies, yet the role of germline DNA copy number variation in cancer is still emerging. Genotyping microarrays generate allele-specific signal intensities to determine genotype, but may also be used to infer DNA copy number using additional computational approaches. Numerous tools have been developed to analyze Illumina genotype microarray data for copy number variant (CNV) discovery, although commonly utilized algorithms freely available to the public employ approaches based upon the use of hidden Markov models (HMMs). QuantiSNP, PennCNV, and GenoCN utilize HMMs with six copy number states but vary in how transition and emission probabilities are calculated. Performance of these CNV detection algorithms has been shown to be variable between both genotyping platforms and data sets, although HMM approaches generally outperform other current methods. Low sensitivity is prevalent with HMM-based algorithms, suggesting the need for continued improvement in CNV detection methodologies.

A Hidden Markov Model based transitional description of camera networks

We consider the problem of building a transitional model of an initially uncalibrated camera network. More specifically, we discuss a Hidden Markov Model (HMM) based strategy in which the model's state-space is defined in terms of a partition of the physical network coverage. Transitions between any two such states are described by the distribution of the underlying Markov Process. Extending previous work in (Cenedese et al., 2010), we show how it is possible to infer the model structure and parameters from coordinate free observations and we introduce a novel performance index for model validation. We moreover show the predictive power of this HMM approach in simulated and real settings that comprise Pan-Tilt-Zoom (PTZ) cameras.

Continuous Hidden Markov Model Based Incipient Fault Monitoring in Filtered Analog Circuits

The small variations of components parameters often lead to severe performance degradation in Filtered Analog Circuits (FAC). Most of the researches on soft fault diagnosis in analog circuit are focused on the variations beyond 30% of the nominal parameter in recent years, which is usually unacceptable in FAC. To diagnose the soft fault of small deviation as earlier as possible, the Hidden Markov Model (HMM) was introduced to monitor the FAC. Different from the existing diagnosis approaches based on HMM, in which the variations of components parameters were considered to be random, the continuous variations of the fault component parameter are discretized and modeled by the hidden states of the proposed HMM method. The experiment demonstrates that the proposed HMM approach can model the FAC effectively and recognize the incipient fault earlier.

Position-specific scoring matrix and hidden Markov model complement each other for the prediction of conopeptide superfamilies

Classified into 16 superfamilies, conopeptides are the main component of cone snail venoms that attract growing interest in pharmacology and drug discovery. The conventional approach to assigning a conopeptide to a superfamily is based on a consensus signal peptide of the precursor sequence. While this information is available at the genomic or transcriptomic levels, it is not present in amino acid sequences of mature bioactives generated by proteomic studies. As the number of conopeptide sequences is increasing exponentially with the improvement in sequencing techniques, there is a growing need for automating superfamily elucidation. To face this challenge we have defined distinct models of the signal sequence, propeptide region and mature peptides for each of the superfamilies containing more than 5 members (14 out of 16). These models rely on two robust techniques namely, Position-Specific Scoring Matrices (PSSM, also named generalized profiles) and hidden Markov models (HMM). A total of 50 PSSMs and 47 HMM profiles were generated. We confirm that propeptide and mature regions can be used to efficiently classify conopeptides lacking a signal sequence. Furthermore, the combination of all three-region models demonstrated improvement in the classification rates and results emphasise how PSSM and HMM approaches complement each other for superfamily determination. The 97 models were validated and offer a straightforward method applicable to large sequence datasets.

A Hidden Markov Model Approach to Information-Based Trading: Theory and Applications

A Hidden Markov Model Approach to Information-Based Trading: Theory and Applications

Hidden conditional random field-based soccer video events detection

Detect highlight event is an important step for semantic-based video retrieval. Hidden conditional random field (HCRF) is a discriminative model, which is effective in fusing observations for event inference. Mid-level semantics and their refinements are more robust than low-level visual features in event detection for learning models. To make full use of the contextual information, two aspects are taken into account during soccer video event detection. The first is parsing video sequences into event clips. The second is fusing the temporal transitions of the mid-level semantics of an event clip to determine the event type. In this study, HCRF is utilised to model the observations of mid-level semantics of an event clip for event detection. Comparisons are made with the dynamic Bayesian networks, hidden Markov model (HMM), enhanced HMM and conditional random field-based event detection approaches. Experimental results show the effectiveness of the proposed method.

A Physically Segmented Hidden Markov Model Approach for Continuous Tool Condition Monitoring: Diagnostics and Prognostics

In this paper, a temporal probabilistic approach based on the hidden Markov model (HMM), named physically segmented HMM with continuous output, is introduced for continuous tool condition monitoring in machinery systems. The proposed approach has the advantage of providing an explicit relationship between the actual health states and the hidden state values. The provided relationship is further exploited for formulation and parameter estimation in the proposed approach. The introduced approach is tested for continuous tool wear prediction in a computer numerical control milling machine and compared with two well-established neural network (NN) approaches, namely, multilayer perceptron and Elman network. In the experimental study, the prediction results are provided and compared after adopting appropriate hyper-parameter values for all the approaches by cross-validation. Based on the experimental results, physically segmented HMM approach outperforms the NN approaches. Moreover, the prognosis ability of the proposed approach is studied.

Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions

We discuss hidden Markov-type models for fitting a variety of multistate random walks to wildlife movement data. Discrete-time hidden Markov models (HMMs) achieve considerable computational gains by focusing on observations that are regularly spaced in time, and for which the measurement error is negligible. These conditions are often met, in particular for data related to terrestrial animals, so that a likelihood-based HMM approach is feasible. We describe a number of extensions of HMMs for animal movement modeling, including more flexible state transition models and individual random effects (fitted in a non-Bayesian framework). In particular we consider so-called hidden semi-Markov models, which may substantially improve the goodness of fit and provide important insights into the behavioral state switching dynamics. To showcase the expediency of these methods, we consider an application of a hierarchical hidden semi-Markov model to multiple bison movement paths.

Activation Detection on fMRI Time Series Using Hidden Markov Model

This paper introduces two unsupervised learning methods for analyzing functional magnetic resonance imaging (fMRI) data based on hidden Markov model (HMM). HMM approach is focused on capturing the first-order statistical evolution among the samples of a voxel time series, and it can provide a complimentary perspective of the BOLD signals. Two-state HMM is created for each voxel, and the model parameters are estimated from the voxel time series and the stimulus paradigm. Two different activation detection methods are presented in this paper. The first method is based on the likelihood and likelihood-ratio test, in which an additional Gaussian model is used to enhance the contrast of the HMM likelihood map. The second method is based on certain distance measures between the two state distributions, in which the most likely HMM state sequence is estimated through the Viterbi algorithm. The distance between the on-state and off-state distributions is measured either through a t-test, or using the Kullback-Leibler distance (KLD). Experimental results on both normal subject and brain tumor subject are presented. HMM approach appears to be more robust in detecting the supplemental active voxels comparing with SPM, especially for brain tumor subject.

A Hidden Markov Process Approach to Information-Based Trading

This paper proposes a novel approach to information-based trading, incorporating both asymmetric information and symmetric order-flow shocks. It focuses on the dynamics of securities trading and postulates that trading activities are determined by the state of nature. A two-dimensional Markov chain is used to model the hidden information states of the market and the state set is allowed to vary across time and companies. Distinguished from the prevailing approaches to information-based trading, the Hidden Markov Model (HMM) approach updates the prior belief of information states using newly observed order flows and identifies trading motives in a data-driven manner. Each trading day is associated with dynamic measures of probability of information based trading (PIN) and probability of symmetric order-flow shock (PSOS). In addition, it allows multiple occurrences of information events rather than limit the frequency of information arrival to once a day. The HMM approach does not rely on a particular market structure and it can be applied to various markets. To evaluate the HMM approach, we conduct extensive Monte Carlo simulation experiments. It shows superior performance in dynamic daily PIN and PSOS estimates as well as cumulative estimates over any time interval in all simulation scenarios, evidenced by their negligible errors compared with true values and higher accuracy than the estimates obtained from alternative approaches. Using a sample of 30 NYSE stocks, we examine the properties of the dynamic PIN estimate as a proxy for information asymmetry and the dynamic PSOS estimate as a measure of illiquidity. We also show how they can be used to explain and predict realized volatility of returns. Dynamic PSOS is a significant contributor to the realized volatility for all the stocks in the sample and its effect is more profound than dynamic PIN, reflecting dominant effect of investors' dispersed beliefs on price fluctuations.