Hidden Markov Model Method Research Articles

Development of a voiding diary using urination recognition technology in mobile environment

We invented a wearable device that can measure voiding time and frequency by checking a habitual series of characteristic motions among men. This study collected and analyzed urination time data collected smart bands worn by patients to resolve the clinical issues posed by using voiding charts. By developing a smart band-based algorithm for assessing urination time in patients, this study aimed to explore the feasibility of urination management systems. This study aimed to assess urination time based on a patient’s posture and changes in posture. Motion data were obtained from a smart band on the arm. An algorithm that identifies the three stages of urination (forward movement, urination, backward movement) was developed based on data collected from a 3-axis accelerometer and tilt angle data. Therefore, we analyze hidden Markov model (HMM)-based sequential data to determine urination time. Real-time data were acquired from the smart band. For data corresponding to a specific duration, the value of the signals was calculated and then compared with the set analysis model to calculate the time of urination. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 92.5%, proving the robustness of the proposed algorithm. The proposed urination time recognition technology draws on acceleration data and tilt angle data collected via a smart band; these data were then analyzed using a classifier after applying the HMM method.

Building Balinese Part-of-Speech Tagger Using Hidden Markov Model (HMM)

Part-of-Speech tagging or word class labeling is a process for labeling a word class in a word in a sentence. Previous research on POS Tagger, especially for Indonesian, has been done using various approaches and obtained high accuracy values. However, not many researchers have built POS Tagger for Balinese. In this article, we are interested in building a POS Tagger for Balinese using a probabilistic approach, specifically the Hidden Markov Model (HMM). HMM is selected to deal with ambiguity since it gives higher accuracy and fast processing time. We used k-fold cross-validation (with k = 10) and tagged corpus around 3669 tokens with 21 tags. Based on the experiments conducted, the HMM method obtained an accuracy of 68.56%.

Mortality forecasting using a Lexis-based state-space model

AbstractA new method of forecasting mortality is introduced. The method is based on the continuous-time dynamics of the Lexis diagram, which given weak assumptions implies that the death count data are Poisson distributed. The underlying mortality rates are modelled with a hidden Markov model (HMM) which enables a fully likelihood-based inference. Likelihood inference is done by particle filter methods, which avoids approximating assumptions and also suggests natural model validation measures. The proposed model class contains as special cases many previous models with the important difference that the HMM methods make it possible to estimate the model efficiently. Another difference is that the population and latent variable variability can be explicitly modelled and estimated. Numerical examples show that the model performs well and that inefficient estimation methods can severely affect forecasts.

HMM conditional-likelihood based change detection with strict delay tolerance

Hidden Markov models (HMMs) have been widely used for anomaly and change point detection due to their representation power and computational efficiency in capturing statistical dependencies in time series. However, often information is integrated over relatively long observation windows, with detections made when the observed sequence’s likelihood under the (null) HMM deviates significantly from its typical range. Three related limitations are: i) use of long windows entails large decision delay, which may e.g. fail to prevent machine failure/damage; ii) typical approaches do not narrowly identify an interval within which the change point occurred. Such information could be useful e.g. for process control, where one wants to know how long it takes for control inputs to induce desired change points; iii) The decision statistic is usually the likelihood of the data in the current window, without consideration of past observations. This is suboptimal – this likelihood should be conditioned on past observations to optimally account for statistical dependency in the time series. In this paper, we propose a framework for change point detection which overcomes all of these limitations: i) it applies a standard HMM Forward recursion, but used to evaluate the likelihood of an observation subsequence conditioned on the subsequence’s entire past. This approach is used to efficiently evaluate the conditional likelihoods of all intervals of fixed length (hence with fixed delay, d), until a change point is first detected. Here d is a design parameter whose proper value (needed to have a quick response/mitigate damage) may be known for a given application domain; ii) the algorithm narrowly estimates the interval within which a detected change point lies; iii) we propose a novel performance criterion well-matched to low-delay, narrowly localized change point detection – the true detection interval rate (TDIR) – and also evaluate the false positive rate (FPR) and the bias and variance of the estimated change point, all as a function of d. The proposed method is shown to outperform a CUSUM algorithm, symbolic time series analysis (STSA) methods, and a standard HMM method (evaluating the unconditioned likelihood) for instability onset in combustion systems and fatigue failure initiation in a material.

A Discrete Hidden Markov Model for SMS Spam Detection

Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naïve Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each word in SMS, is unable to distinguish the importance of words, due to the length limitation of SMS. This paper proposes a new method based on the discrete hidden Markov model (HMM) to use the word order information and to solve the low term frequency issue in SMS spam detection. The popularly adopted SMS spam dataset from the UCI machine learning repository is used for performance analysis of the proposed HMM method. The overall performance is compatible with deep learning by employing CNN and LSTM models. A Chinese SMS spam dataset with 2000 messages is used for further performance evaluation. Experiments show that the proposed HMM method is not language-sensitive and can identify spam with high accuracy on both datasets.

Part of Speech Tagging for Javanese Language with Hidden Markov Model

Indonesia has many cultures and local language, one of the most is Javanese with the Javanese language. The Javanese language is used in the region of Central Java and East Java, the word structure of the Javanese language has a similar to the Indonesian word class. Part of Speech (POS) Tagging is a process for labeling word classes for each input word that corresponding. POS Tag for Indonesian Language has been done a lot and got very good accuracy with various method application. This study aims to provide the word class label for Javanese language and the datasets used was obtained from online news with Javanese Ngoko language. The method used in this study is the Hidden Markov Model (HMM) with use of the HMM method get the highest accuracy is 96.2 %.
 Keywords: POS Tagging, Javanese Ngoko, Labeling, Hidden Markov Model

Model Checking for Hidden Markov Models

Residual analysis is a useful tool for checking lack of fit and for providing insight into model improvement. However, literature on residual analysis and the goodness of fit for hidden Markov models (HMMs) is limited. As HMMs with complex structures are increasingly used to accommodate different types of data, there is a need for further tools to check the validity of models applied to real world data. We review model checking methods for HMMs and develop new methods motivated by a particular case study involving a two-dimensional HMM developed for time series with many null events. We propose new residual analysis and stochastic reconstruction methods, which are adapted from model checking techniques for point process models. We apply the new methods to the case study model and discuss their adequacy. We find that there is not one “best” test for diagnostics but that our new methods have some advantages over previously developed tools. The importance of multiple tests for complex HMMs is highlighted and we use the results of our model checking to provide suggestions for possible improvements to the case study model. Supplementary materials for this article are available online.

Bidirectional LSTM Recurrent Neural Network Plus Hidden Markov Model for Wearable Sensor-Based Dynamic State Estimation

Abstract Behavior of animals living in the wild is often studied using visual observations made by trained experts. However, these observations tend to be used to classify behavior during discrete time periods and become more difficult when used to monitor multiple individuals for days or weeks. In this work, we present automatic tools to enable efficient behavior and dynamic state estimation/classification from data collected with animal borne bio-logging tags, without the need for statistical feature engineering. A combined framework of an long short-term memory (LSTM) network and a hidden Markov model (HMM) was developed to exploit sequential temporal information in raw motion data at two levels: within and between windows. Taking a moving window data segmentation approach, LSTM estimates the dynamic state corresponding to each window by parsing the contiguous raw data points within the window. HMM then links all of the individual window estimations and further improves the overall estimation. A case study with bottlenose dolphins was conducted to demonstrate the approach. The combined LSTM–HMM method achieved a 6% improvement over conventional methods such as K-nearest neighbor (KNN) and support vector machine (SVM), pushing the accuracy above 90%. In addition to performance improvements, the proposed method requires a similar amount of training data to traditional machine learning methods, making the method easily adaptable to new tasks.

Sound Recognition of Four Stroke Manual Transmission Motorcycle Engine’s Damage

Sound recognition that learns about the source of a sound, which is then used as a reference in learning. Sound recognition referred to in this study is the sound produced from the sound of a motorcycle engine. From the sound of the engine, the sound is then studied more and the results of the recorded sound are then used as objects of research. The management of motorcycle engine sound is carried out using Matlab and assisted by using the HMM (Hidden Markov Model) method with GMM (Gaussian Mixture Model) where HMM is used as chain pattern formation and GMM is used as a sound matching pattern. In this study indeed really learns from every sound of a motorcycle engine that will be used as a sample in the laboratory. From the sound of a motorcycle engine that is heard by the human ear can already indicate the conditions and eligibility limits of the motorcycle. Therefore, from this study trying to find answers to each condition and type of damage from a motorcycle through the sound of a motorcycle engine. With this research, it can help the vehicle owner, because the results of this study warn the vehicle owner to immediately conduct a scale check at the nearest official workshop and from this study also provide benefits and benefits for researchers and motorcycle vehicle owners to find out the condition of the damage early vehicles and help prevent them so as not to cause severe damage which can add air pollution in the city of Dili, East Timor. And the results of existing research are very helpful both for researchers, mechanics and motorcycle vehicle owners.

Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems

Fault Detection and Diagnosis (FDD) for overall modern Wind Energy Conversion (WEC) systems, particularly its converter, is still a challenge due to the high randomness to their operating environment. This paper presents an advanced FDD approach aims to increase the availability, reliability and required safety of WEC Converters (WECC) under different conditions. The developed FDD approach must be able to detect and correctly diagnose the occurrence of faults in WEC systems. The developed approach exploits the benefits of the machine learning (ML)-based Hidden Markov model (HMM) and the principal component analysis (PCA) model. The PCA technique is used for efficiently extracting and selecting features to be fed to HMM classifier. The effectiveness and higher classification accuracy of the developed PCA-based HMM approach are demonstrated via simulated data collected from the WEC.The obtained results demonstrate the efficiency of the PCA-based HMM method over the PCA-based support vector machine (SVM) method. The comparison is made based on several performance metrics through different operating conditions of the WEC systems.

Pengenalan Gangguan Ginjal Melalui Iridologi Menggunakan Hidden Markov Model (HMM)

ABSTRACTIridology has not been widely applied for the recognition of kidney disorders. identification of kidney disorders through iris image using iridology chart, can make it easier to make diagnosis to find out about kidney disorders. The method used in the process of recognition of kidney disorders through iridology is the Hidden Markov Model (HMM) method, with a HMM parameter determination system using the calculation of the koefisien Singular Value Decomposition (SVD) coefficient. The size of the codebook used is 7, i.e. 16, 32, 64, 128, 256, 512 and 1024. Different sizes of codebooks will result in different recognition times. The time needed will be longer when the size of the codebook is getting bigger. The accuracy of the process of recognition of kidney disorders through iridology using the HMM method in this study is 68.75% for codebook 16, 87.5% for codebook 32, 100% for codebook 128 and 100% for codebook 512. Keywords : iridology, codebook, image processing, singular value decomposition (SVD), Hidden Markov Model (HMM).

Food quality and safety risk assessment using a novel HMM method based on GRA

Food quality and safety are closely related to people's health and living standard, and the risk assessment of food quality and safety has great social significance. Therefore, a novel hidden Markov model (HMM) based on gray relational analysis (GRA) (GRA-HMM), in terms of the high complexity and temporal characteristics of food detection data, is proposed to dynamically assess the food quality and safety risk. The weight of food detection indicators is calculated based on the GRA method to obtain risk indexes. Then risk indexes are used as the input data of the HMM to build the risk assessment model of food quality and safety. Moreover, the accuracy and reliability of the risk assessment model is proved based on the fuzzy matrix. Finally, the risk grading results of sterilized milk quality and safety are obtained using the proposed method. The results show that the GRA-HMM method has high accuracy in risk assessment. Meanwhile, risk control measures can be formulated or adjusted in advance and recommendations can be made for the food production, inspection and management departments.

End-to-end vision-based detection, tracking and activity analysis of earthmoving equipment filmed at ground level

Abstract This paper presents a new benchmark dataset for validating vision-based methods that automatically identifies visually distinctive working activities of excavators and dump trucks from individual frames of a video sequence. Our dataset consists of 10 videos of interacting pairs of construction equipment filmed at ground level with accompanying ground truth annotations. These annotations consist of per-equipment and per-frame equipment bounding boxes that also have associated identities and activity labels. Our videos depict an excavator interacting with 1 or more dump trucks. We also propose a deep learning-based method for detecting and tracking objects based on Convolutional Neural Networks (CNNs). The tracking trajectories are fed into a Hidden Markov Model (HMM) that automatically discovers and assigns activity labels for any observed object. Our HMM method leverages trajectories to train a Gaussian Mixture Model (GMM) with which we estimate the probability density function of each activity using Support Vector Machine (SVM) classifiers. The proposed HMM also models activity duration and the transition between activities. We show that our method can accurately distinguish between individual equipment working activities. Results show 97.43% detection Average Precision (AP) for excavators and 75.29% AP for dump trucks, as well as cross-category tracking accuracy of 81.94% and tracking precision of 87.45%. Separate experiment results show activity analysis results of 86.8% accuracy for excavators and 88.5% for dump trucks. Our results show that our method can accurately conduct activity analysis and can be fused with methods that detect motion trajectories to scale to the needs of practical applications.

Moving average filtering with deconvolution (MAD) for hidden Markov model with filtering and correlated noise.

Ion channel data recorded using the patch clamp technique are low-pass filtered to remove high-frequency noise. Almanjahie et al. (Eur Biophys J 44:545-556, 2015) based statistical analysis of such data on a hidden Markov model (HMM) with a moving average adjustment for the filter but without correlated noise, and used the EM algorithm for parameter estimation. In this paper, we extend their model to include correlated noise, using signal processing methods and deconvolution to pre-whiten the noise. The resulting data can be modelled as a standard HMM and parameter estimates are again obtained using the EM algorithm. We evaluate this approach using simulated data and also apply it to real data obtained from the mechanosensitive channel of large conductance (MscL) in Escherichia coli. Estimates of mean conductances are comparable to literature values. The key advantages of this method are that it is much simpler and computationally considerably more efficient than currently used HMM methods that include filtering and correlated noise.

An Adaptive Contourlet HMM–PCNN Model of Sparse Representation for Image Denoising

In this paper, we propose a novel model of sparse representation for image denoising that we call an adaptive contourlet hidden Markov model (HMM)-pulse-coupled neural network (PCNN). In this study, we first adopted a contourlet transform to decompose a noisy image to be some subband coefficients of various directions at various scales. The contourlet emulated extremely well the sparse representation performance of human visual perception, such as its multiscale characteristics, geometric features, and bandpass properties. Second, we used an HMM method to create a statistical model that expressed the coefficient relationships in intrabands, interbands, intrascales, and interscales. Then we used an expectation-maximization training algorithm to obtain the state probability. The result included the state, scale, and direction, the position of the coefficient, the noisy image, and the parameter set of the HMM model. Third, we put the state probability into the PCNN model, which could adaptively optimize the parameters of the HMM model and get better coefficients of clean images. Finally, we transformed the image denoising problem into a Bayesian posterior probability estimation problem. We also reconstructed a denoised image based on the clean coefficients obtained from our proposed method. The experimental results show that the contourlet HMM-PCNN model proposed in this paper is superior to the contourlet with hidden Markov tree model and the wavelet threshold method.

A discrete regularization method for hidden Markov models embedded into reproducing kernel Hilbert space

Hidden Markov models are a well-known probabilistic graphical model for time series of discrete, partially observable stochastic processes. We consider the method to extend the application of hidden Markov models to non-Gaussian continuous distributions by embedding a priori probability distribution of the state space into reproducing kernel Hilbert space. Corresponding regularization techniques are proposed to reduce the tendency to overfitting and computational complexity of the algorithm, i.e. Nystr¨om subsampling and the general regularization family for inversion of feature and kernel matrices. This method may be applied to various statistical inference and learning problems, including classification, prediction, identification, segmentation, and as an online algorithm it may be used for dynamic data mining and data stream mining. We investigate, both theoretically and empirically, the regularization and approximation bounds of the discrete regularization method. Furthermore, we discuss applications of the method to real-world problems, comparing the approach to several state-of-the-art algorithms.

Spike detection and spike sorting with a hidden Markov model improves offline decoding of motor cortical recordings

Objective. Detection and sorting (classification) of action potentials from extracellular recordings are two important pre-processing steps for brain–computer interfaces (BCIs) and some neuroscientific studies. Traditional approaches perform these two steps serially, but using shapes of action potential waveforms during detection, i.e. combining the two steps, may lead to better performance, especially during high noise. We propose a hidden Markov model (HMM) based method for combined detecting and sorting of spikes, with the aim of improving the final decoding accuracy of BCIs. Approach. The states of the HMM indicate whether there is a spike, what unit a spike belongs to, and the time course within a waveform. The HMM outputs probabilities of spike detection, and from this we can calculate expectations of spike counts in time bins, which can replace integer spike counts as input to BCI decoders. We evaluate the HMM method on simulated spiking data. We then examine the impact of using this method on decoding real neural data recorded from primary motor cortex of two Rhesus monkeys. Main results. Our comparisons on simulated data to detection-then-sorting approaches and combined detection-and-sorting algorithms indicate that the HMM method performs more accurately at detection and sorting (0.93 versus 0.73 spike count correlation, 0.73 versus 0.49 adjusted mutual information). On real neural data, the HMM method led to higher adjusted mutual information between spike counts and kinematics (monkey K: 0.034 versus 0.027; monkey M: 0.033 versus 0.022) and better neuron encoding model predictions (K: 0.016 dB improvement; M: 0.056 dB improvement). Lastly, the HMM method facilitated higher offline decoding accuracy (Kalman filter, K: 8.5% mean squared error reduction, M: 18.6% reduction). Significance. The HMM spike detection and sorting method offers a new approach to spike pre-processing for BCIs and neuroscientific studies.

A state dependent mandatory lane-changing model for urban arterials with hidden markov model method

The inherent intention and decision process of lane changes are complex. Though the external environment and traffic conditions are changing along the traveling direction, the drivers’ characteristics and preferences may lead to persistence of preferable lane choice. The lane-changing decision process is commonly separated into two steps after the decision to consider a lane change: lane choices and acceptance of available gaps, while the outcome of first step is unobservable. To better represent the mandatory lane-changing behaviors on arterials, a two-stage lane-changing model with Hidden Markov Model (HMM) method is proposed. The proposed model is estimated and validated using detail Next Generation Simulation (NGSIM) vehicle trajectory data from an urban arterial. Comparison between generated lane position sequences and original trajectories validated the model’s capability of representing mandatory lane changes.

A hidden markov model for identifying differentially methylated sites in bisulfite sequencing data.

DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called "DMCHMM" which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks. Our proposed method is different from other HMM methods since it profiles methylation of each sample separately, hence exploiting inter-CpG autocorrelation within samples, and it is more flexible than previous approaches by allowing multiple hidden states. Using simulations, we show that DMCHMM has the best performance among several competing methods. An analysis of cell-separated blood methylation profiles is also provided.

Identification and quality evaluation of Tripterygium wilfordii

With the extensive clinical application of Tripterygium wilfordii, there are many counterfeit products on the market. Traditional technology can not effectively identify the authenticity of the traditional Chinese medicine. Therefore, a strategy of accurate identification and quality evaluation of Tripterygium based on DNA barcode and chemical fingerprint spectrum was established. Based on DNA barcode technology, HMMer annotation method of hidden Markov model and K2P model were used to analyze genetic distance.BLAST1, nearest distance and phylogenetic tree (NJ-tree) methods were used to assess the identification efficiency of the ITS2 barcode. The fingerprint of 27 T. wilfordii was established by UPLC-PDA method, and the similarity of the fingerprint of different sources was evaluated. The main components of T. wilfordii were determined by LC-MS/MS. The results revealed that the intraspecific genetic distances of T. wilfordii were lower than the interspecific genetic distances between T. wilfordii and its adulterants. The results of similarity search showed that ITS2 sequence was used to identify T. wilfordii and its adulterants. The clustering of T. wilfordii and its adulterants was clear in the tree of NJ cluster, and 12 of 27 samples were identified as true T. wilfordii.The chemical fingerprint spectrum research indicates that the feature one region can distinguish the false product of tripterygium glycosides more intuitively. The cluster analysis of HCA-thermal map showed that the contents of six active components of T. wilfordii from different habitats were significantly different, which could be used to evaluate the quality of T. wilfordii. This paper is of guiding significance for the accurate identification and quality evaluation of Tripterygium medicinal plants.