Coupled Hidden Markov Model Research Articles

A Coupled Hidden Markov Model framework for measuring the dynamics of categorization

We introduce a new framework for measuring the dynamics of category learning using Coupled Hidden Markov Models (CHMMs). The key assumptions of the framework are that people maintain a latent assignment of every stimulus to a category, and that they can update the assignments for all stimuli whenever they encounter any stimulus. These assumptions contrast with many existing accounts of category learning, which either do not allow for what is learned about one stimulus to influence the category association of others, or allow only for indirect influence. The CHMM framework allows tailored models to be developed for specific category learning tasks, taking as input the stimulus sequence and category responses people make, and producing as output inferences about the underlying dynamics of category assignments and the mechanics of the response processes. We demonstrate the framework by applying it to a categorization task considered by Lee and Navarro (2002), showing how the model measures the change in participants’ latent category assignments as they learn the category structure. We conclude by discussing potential applications of the CHMM framework to category learning situations involving prior knowledge, changing category structures, and category learning tasks that involve the consideration of multiple stimuli at one time.

Application of CNN-LSTM Model for Vehicle Acceleration Prediction Using Car-following Behavior Data

Accurate vehicle acceleration prediction is useful for developing reliable Advanced Driving Assistance Systems (ADAS) and improving road safety. The existence of driver heterogeneity magnifies the variations in acceleration data, leading to consequential impacts on the precision of vehicle acceleration prediction. However, few studies have fully considered the driver heterogeneity when predicting vehicle acceleration. To model the characteristics of individual drivers, this study first identifies the driving behavior semantics which is defined as the underlying patterns of driving behaviors. The analysis results from the coupled hidden Markov model (CHMM) are used to evaluate the driving behavior differences between different drivers by Wasserstein distance. Then the convolutional neural network (CNN) and long short-term memory (LSTM) network are applied to predict vehicle acceleration. To validate the accuracy of the proposed prediction framework, vehicle acceleration data in car-following conditions is extracted from the safety pilot model deployment (SPMD) dataset. The segmentation results indicate that the CHMM possesses a robust capacity for modeling driving behavior. The prediction results demonstrate that the proposed framework, which incorporates driver clustering before prediction, significantly improves the accuracy of predictions. And the CNN-LSTM outperforms the LSTM in predicting vehicle acceleration during car-following scenarios. The findings from this study can enhance the development of personalized functionalities within ADAS to promote its deployment, thereby improving its acceptance and safety.

Tensor-Based Forward–Backward Algorithms in the Physics-Informed Coupled Hidden Markov Model

Imposing data-driven with physical laws for user activity prediction could effectively solve various physical problems such as smart care, surveillance and human-robot. In the growing field of artificial intelligence, the application of activity prediction based on physical Coupled Hidden Markov Model (CHMM) and tensor theory with physical properties has attracted increasing attentions. However, existing CHMMs usually only consider the time-series characteristic of data, while ignoring physical characteristics of user activity such as periodicity, timing and correlation. Moreover, they are all matrix-based models, which could not holistically analyze the dependencies among physical states. The above disadvantages lead to lower prediction accuracy of CHMM. To remove these disadvantages, three physics-informed tensor-based CHMMs are first constructed by incorporating prior physical knowledge. Then the corresponding forward-backward algorithms are designed for resolving the evaluation problem of CHMM. These algorithms could overall model multiple physical features by imposing physics and prior knowledge into CHMM during training to improve the precision of probabilistic computing. The algorithms reduce the dependence of the model on training data by adding physical features. Finally, the comparative experiments show that our algorithms have better performances than existing prediction methods in precision and efficiency. In addition, further selfcomparison experiments verify that our algorithms are effective and practical.

Tensor-Based Viterbi Algorithms for Collaborative Cloud-Edge Cyber-Physical-Social Activity Prediction

With the rapid development and application of smart city, Cyber-Physical-Social Systems (CPSS) as its superset is becoming increasingly important, and attracts extensive attentions. For satisfying the smart requirements of CPSS design, a cloud-edge collaborative CPSS framework is first proposed in this paper. Then Coupled-Hidden-Markov-Model (CHMM) and tensor algebra are used to improve existing activity prediction methods for providing CPSS with more intelligent decision support. There are three key features (timing, periodicity and correlation) implied in CPSS data from multi-edge, which affects the accuracy of activity prediction. Thus, these features are synthetically integrated into improved Tensor-based CHMMs (T-CHMMs) to enhance the prediction accuracy. Based on the multi-edge CPSS data, three Tensor-based Viterbi Algorithms (TVA) are correspondingly proposed to solve the prediction problem for T-CHMMs. Compared with traditional matrix-based methods, the proposed TVA could more accurately compute the optimal hidden state sequences under given observation sequences. Finally, the comprehensive performances of proposed models and algorithms are validated on three open datasets by self-comparison and other-comparison. The experimental results show that the proposed methods is superior to the compared three classical methods in terms of F1 measure, average precision and average recall.

Enhanced capsule generative adversarial network for spectrum and energy efficiency of cooperative spectrum prediction framework in cognitive radio network

AbstractCognitive radio network (CRN) is an intellective technology that frequently monitors the unused licensed spectrum in a particular frequency band. When more count of secondary users enters inside the channel may results in collisions. So, the secondary user may not sense the state of inactive and busy state of the primary user resulting reduces the spectrum efficiency (SE) and energy efficiency (EE) of CRN. To overcome these issues, an enhanced capsule generative adversarial network (ECGAN) for spectrum and energy efficiency of cooperative spectrum prediction (CSP) framework in CRN is proposed to improve the channel occupancy. Initially, CRN system is designed to improve the spectrum efficiency and energy efficiency. Then, channel state of primary user is predicted by using the coupled hidden Markov model (CHMM) and ECGAN predictors. Hence, the correlation among the secondary user predict the next channel status, like AND, OR, majority rule fusion methods. The prediction error, spectrum efficiency, energy efficiency based these methods are compared with CHMM and ECGAN predictors as the process of channel occupancy based on. Also, the effect of busy and idle state prediction errors on SE is detected. The proposed method is simulated in MATLAB. Then, the performance of the proposed method shows higher energy efficiency 13.86%, 24.84%, 34.75%, compared with existing methods, such as spectrum and energy efficiency of CSP in CRN (SEE‐CSP‐CRN‐HMM), (SEE‐CSP‐CRN‐MLP), differential evolution based machine learning scheme for secure cooperative spectrum sensing system (SEE‐CSP‐CRN‐EML), cooperative spectrum sensing for primary user detection in CRN utilizing support vector machine (SEE‐CSP‐CRN‐SVM), respectively.

Multivariate analysis of car-following behavior data using a coupled hidden Markov model

Accurate analysis of driving behavior data is important for improving traffic operations and for automakers to design safe and effective Advanced Driver Assistance System (ADAS). Although different approaches have been proposed to analyze driving behavior data and understand the car-following behavior, very few of them have considered the interaction effects among driving behavior variables and constructed a multivariate modeling framework to fully understand and extract the underlying primitive driving patterns. To bridge this gap, this paper introduces the coupled hidden Markov model (CHMM) and evaluates its applicability in obtaining the car-following behavioral semantics and comparing the similarity of different drivers’ driving styles. The Safety Pilot Model Deployment (SPMD) dataset, which contains two years of naturalistic driving data, is used to demonstrate the advantages of the proposed coupled modeling approach. The modeling results suggest that CHMM with 3 hidden states provides the most appropriate driving behavior pattern segmentation results. It generates reasonable durations for each behavioral semantic by accounting for the dependence relationship among variables in the analysis. Moreover, a similarity analysis is conducted considering three aspects of a driving behavior pattern: driving behavior pattern transfer, driving behavior pattern selection, and aggressiveness behavior. The result reveals that these three aspects do not necessarily show consistent trends for the same driving behavior. Overall, the findings from this paper provide a novel framework for better understanding and interpreting the driving behavior and primitive driving patterns, and confirm the importance of considering the dependence among different driving behavior variables.

Copper cross-market volatility transition based on a coupled hidden Markov model and the complex network method

Copper, as a strategic resource, has attracted widespread attention due to its price changes. Copper's futures prices and spot prices tend to be highly correlated in different countries. The copper market price change should consider the relationship between market trends of different countries, especially in the current situation where economic globalization and financial integration are inevitable. The volatility transition between datasets of different countries presents the characteristics of nonlinearity, interaction, dynamics and heterogeneity. To explore the cross-market transition of copper price fluctuations, this paper proposes a hybrid method of the coupled hidden Markov model (CHMM) and complex network. The coupled hidden Markov model (CHMM) is used to identify the transition characteristics of the market price trends, and the complex network method is used to analyze the evolutionary characteristics of the market state transition. We take the copper futures and spot market prices between the U.S., China, and the UK between January 2003 and December 2019 as the object. The results show that the copper futures market's state transition has a clustering effect, the price rise is easy to maintain, and the decline is rapid and easy to transfer. The relationship between daily markets is more volatile, and weekly and monthly are more stable. The British copper futures market has shown advantages in terms of transition ability, assimilation ability, media ability and transfer structure, while the influence of China's copper futures market is relatively weak. In addition, during the financial crisis, the UK spot market played an important role in the frequent declining transition. The nonlinear measurement model and conclusion established in this paper provide a new framework and analysis tool for explaining the volatility transition of the copper market.

Coupled Hidden Markov Fusion of Multichannel Fast Spectral Coherence Features for Intelligent Fault Diagnosis of Rolling Element Bearings

Comprehensive understanding for machinery health monitoring could be realized by multisource information. Nevertheless, effective fusion of faulty features extracted from multichannel sensors remains challenging in intelligent fault diagnostics. For this reason, coupled hidden Markov model (CHMM) is proposed in this work to efficiently fuse spectral coherence (SC) features extracted from multichannel sensors for improving the diagnosis performance of rolling element bearings (REBs). To this end, SC features extracted from multichannel vibration data of the REB under different operational conditions are fused by a multichain CHMM. Parameter estimation algorithms and probabilistic inference for the CHMM are also developed in the addressed fusion approach. The effectiveness of the proposed method is validated by two different diagnosis experiments, one for fault classification and another for lifecycle performance evaluation of REBs. Compared with the state-of-the-art peer methods, the proposed multichannel feature fusion method has the best performance when dealing with fault diagnosis tasks for REBs.

Tractable Inference and Observation Likelihood Evaluation in Latent Structure Influence Models

Latent Structure Influence Models (LSIMs) are a particular kind of Coupled Hidden Markov Models (CHMMs). Against CHMMs, LSIMs overcome the exponential growth of state-space parameters by considering the influence model for coupled Markov chains. Nevertheless, the exact inference in LSIMs requires exponential complexity. We propose a new recursive formulation to compute marginal forward and backward parameters by $\mathcal {O}(T{(NC)}^{2})$ instead of $\mathcal {O}(TN^{\text{2}\,C})$ for $C$ channels of $N$ states apiece observing $T$ data points. This formulation is derived systematically and carefully to increase the inference accuracy. Furthermore, a solution is presented for the evaluation problem of LSIMs based on the proposed marginal forward parameter. This solution is essential in statistical multi-channel time-series classification. The results show that the proposed algorithm is generally more accurate and reliable than other existing algorithms. Novelties in deriving the marginal backward parameter plays an important role in this superiority. The Hellinger distance is computed between the proposed and exact forward and one-slice parameters for various simulation scenarios. Distances are small enough, indicating that the proposed inference algorithm is sufficiently close to exact inference for various channels, hidden state numbers, and other parameters. Statistical multi-channel time-series classification is also considered for both proposed and exact algorithms. Classification results are almost similar, indicating that the proposed approximate inference is proper and acceptable in the classification task. Finally, the iEEG dataset's parameter learning indicates that the proposed inference algorithm leads to a higher log-likelihood than the existing algorithms.

A Spatio-Temporal Model of Seizure Propagation in Focal Epilepsy.

We propose a novel Coupled Hidden Markov Model (CHMM) to detect and localize epileptic seizures in clinical multichannel scalp electroencephalography (EEG) recordings. Our model captures the spatio-temporal spread of a seizure by assigning a sequence of latent states (i.e. baseline or seizure) to each EEG channel. The state evolution is coupled between neighboring and contralateral channels to mimic clinically observed spreading patterns. Since the latent state space is exponential, a structured variational algorithm is developed for approximate inference. The model is evaluated on simulated and clinical EEG from two different hospitals. One dataset contains seizure recordings of adult focal epilepsy patients at the Johns Hopkins Hospital; the other contains publicly available non-specified seizure recordings from pediatric patients at Boston Children's Hospital. Our CHMM model outperforms standard machine learning techniques in the focal dataset and achieves comparable performance to the best baseline method in the pediatric dataset. We also demonstrate the ability to track seizures, which is valuable information to localize focal onset zones.

Multichannel dynamic modeling of non-Gaussian mixtures

This paper presents a novel method that combines coupled hidden Markov models (HMM) and non-Gaussian mixture models based on independent component analyzer mixture models (ICAMM). The proposed method models the joint behavior of a number of synchronized sequential independent component analyzer mixture models (SICAMM), thus we have named it generalized SICAMM (G-SICAMM). The generalization allows for flexible estimation of complex data densities, subspace classification, blind source separation, and accurate modeling of both local and global dynamic interactions. In this work, the structured result obtained by G-SICAMM was used in two ways: classification and interpretation. Classification performance was tested on an extensive number of simulations and a set of real electroencephalograms (EEG) from epileptic patients performing neuropsychological tests. G-SICAMM outperformed the following competitive methods: Gaussian mixture models, HMM, Coupled HMM, ICAMM, SICAMM, and a long short-term memory (LSTM) recurrent neural network. As for interpretation, the structured result returned by G-SICAMM on EEGs was mapped back onto the scalp, providing a set of brain activations. These activations were consistent with the physiological areas activated during the tests, thus proving the ability of the method to deal with different kind of data densities and changing non-stationary and non-linear brain dynamics.

Spatial–temporal traffic outlier detection by coupling road level of service

Traffic outlier detection is an essential topic in city management and data mining. Most traffic outliers are caused by accidents, control, protests, disasters, and many other events. Recently, traffic outlier detection methods are based on counting traffic flow, where the detection is inherited from the periodical changes of road flow. However, these approaches fail to detect the impact from upstream roads. Furthermore, the mappings between traffic congestion level and traffic flow are distinct from road to road. In this study, a Poisson mixture model (PMM) – coupled hidden Markov model (CHMM) outlier detection method would be introduced for detecting traffic anomalies which are from taxi global positioning system data. To make a finer-grained outlier detection method, road traffic congestion estimation, as well as the impact from upstream roads, are considered. PMM serves as an estimator to determine the congestion level for every road and CHMM is used to couple the road's impact. The experiment applies both semi-synthetic and real outlier data from the Beijing map, and the results reveal the advantages of both datasets.

Predictability of Financial Crisis via Pair Coupling of Commodity Market and Stock Market

The complex interactions between stock market and commodity market in financial crisis has been investigated by many researchers, but there is less known about how useful the pair coupling of the two markets for predicting financial crisis, where the pair coupling is the hidden essence of market interactions. This article investigates three kinds of couplings, namely time coupling, frequency coupling and space coupling, which are the different aspects of the pair coupling. In addition, a two-layer model, namely CHMM-ANN, is proposed to investigate the couplings and evaluate the predicting abilities based on the couplings. Coupled Hidden Markov Model (CHMM) is adopted at the bottom level to capture the hidden couplings, and then the couplings are put as input to classical Artificial Neural Network (ANN) at the top level to predict financial crisis. The experiment results on real financial data confirm the advantages of the pair coupling in predicting financial crisis.

Fault diagnosis of rolling element bearing based on wavelet kernel principle component analysis-coupled hidden Markov model

Different description results will be obtained when apply hidden Markov model (HMM) to the two different channel signals from the same data collection point respectively. Besides, wrong fault diagnosis result might be obtained because fault feature information would not be described comprehensively by using only one single channel signal. In theory, two channel signals collected form the same data collection point will contain much more fault information than the single channel signal contain, but the coupled phenomenon might occur between the two channel signals. Coupled hidden Markov model (CHMM) is the improved method of HMM and it can fuse the information of two channel signals from the same data collection point efficiently, so much more reliable diagnosis result could be obtained by using CHMM than by using HMM. Stated thus, the fault diagnosis method of rolling element bearing based on wavelet kernel component analysis (WKPCA)-CHMM is proposed: Firstly, use WKPCA as fault feature vectors extraction method to increase the efficiency of the proposed method. Then apply CHMM to the extracted fault feature vectors and satisfactory fault diagnosis result is obtained at last. The feasibility and advantages of the proposed method are verified through experiment.

Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns

Background and objectiveCritical care patient events like sepsis or septic shock in intensive care units (ICUs) are dangerous complications which can cause multiple organ failures and eventual death. Preventive prediction of such events will allow clinicians to stage effective interventions for averting these critical complications. MethodsIt is widely understood that physiological conditions of patients on variables such as blood pressure and heart rate are suggestive to gradual changes over a certain period of time, prior to the occurrence of a septic shock. This work investigates the performance of a novel machine learning approach for the early prediction of septic shock. The approach combines highly informative sequential patterns extracted from multiple physiological variables and captures the interactions among these patterns via coupled hidden Markov models (CHMM). In particular, the patterns are extracted from three non-invasive waveform measurements: the mean arterial pressure levels, the heart rates and respiratory rates of septic shock patients from a large clinical ICU dataset called MIMIC-II. Evaluation and resultsFor baseline estimations, SVM and HMM models on the continuous time series data for the given patients, using MAP (mean arterial pressure), HR (heart rate), and RR (respiratory rate) are employed. Single channel patterns based HMM (SCP-HMM) and multi-channel patterns based coupled HMM (MCP-HMM) are compared against baseline models using 5-fold cross validation accuracies over multiple rounds. Particularly, the results of MCP-HMM are statistically significant having a p-value of 0.0014, in comparison to baseline models. Our experiments demonstrate a strong competitive accuracy in the prediction of septic shock, especially when the interactions between the multiple variables are coupled by the learning model. ConclusionsIt can be concluded that the novelty of the approach, stems from the integration of sequence-based physiological pattern markers with the sequential CHMM model to learn dynamic physiological behavior, as well as from the coupling of such patterns to build powerful risk stratification models for septic shock patients.

Coupled HMM-based multi-sensor data fusion for sign language recognition

Recent development of low cost depth sensors such as Leap motion controller and Microsoft kinect sensor has opened up new opportunities for Human-Computer-Interaction (HCI). In this paper, we propose a novel multi-sensor fusion framework for Sign Language Recognition (SLR) using Coupled Hidden Markov Model (CHMM). CHMM provides interaction in state-space instead of observation states as used in classical HMM that fails to model correlation between inter-modal dependencies. The framework has been used to recognize dynamic isolated sign gestures performed by hearing impaired persons. The dataset has been tested using existing data fusion approaches. The best recognition accuracy has been achieved as high as 90.80% with CHMM. Our CHMM-based approach shows improvement in recognition performance over popular existing data fusion techniques.

Modeling interaction in multi-resident activities

In this paper we investigate the problem of modeling multi-resident activities. Specifically, we explore different approaches based on Hidden Markov Models (HMMs) to deal with parallel activities and cooperative activities. We propose an HMM-based method, called CL-HMM, where activity labels as well as observation labels of different residents are combined to generate the corresponding sequence of activities as well as the corresponding sequence of observations on which a conventional HMM is applied. We also propose a Linked HMM (LHMM) in which activities of all residents are linked at each time step. We compare these two models to baseline models which are Coupled HMM (CHMM) and Parallel HMM (PHMM). The experimental results show that the proposed models outperform CHMM and PHMM when tested on parallel and cooperative activities.

Coupled HMM-based multimodal fusion for mood disorder detection through elicited audio–visual signals

Mood disorders encompass a wide array of mood issues, including unipolar depression (UD) and bipolar disorder (BD). In diagnostic evaluation on the outpatients with mood disorder, a high percentage of BD patients are initially misdiagnosed as having UD. It is crucial to establish an accurate distinction between BD and UD to make a correct and early diagnosis, leading to improvements in treatment and course of illness. In this study, eliciting emotional videos are firstly used to elicit the patients’ emotions. After watching each video clips, their facial expressions and speech responses are collected when they are interviewing with a clinician. In mood disorder detection, the facial action unit (AU) profiles and speech emotion profiles (EPs) are obtained, respectively, by using the support vector machines (SVMs) which are built via facial features and speech features adapted from two selected databases using a denoising autoencoder-based method. Finally, a Coupled Hidden Markov Model (CHMM)-based fusion method is proposed to characterize the temporal information. The CHMM is modified to fuse the AUs and the EPs with respect to six emotional videos. Experimental results show the promising advantage and efficacy of the CHMM-based fusion approach for mood disorder detection.

Exploiting Turn-Taking Temporal Evolution for Personality Trait Perception in Dyadic Conversations

In dyadic conversations, turn-taking is a dynamically evolving behavior strongly linked to paralinguistic communication. Turn-taking temporal evolution in a dyadic conversation is inevitable and can be incorporated into a modeling framework for characterizing and recognizing the personality traits (PTs) of two speakers. This study presents an approach to automatically predicting PTs in a dyadic conversation. First, a recurrent neural network (RNN) was used to model the relationship between Big Five Inventory 10 (BFI-10) items and linguistic features of spoken text in each turn of a speaker (speaker turn) to output a BFI-10 profile. The RNN applies a recurrent property to characterize the short-term temporal evolution of a dialog. Second, the coupled hidden Markov model (C-HMM) was employed to model the long-term turn-taking temporal evolution and cross-speaker contextual information for detecting the PTs of two individuals for the entire dialog represented by the BFI-10 profile sequence. The Mandarin Conversational Dialogue Corpus was used for evaluation. The evaluation result shows that an average perception accuracy of 79.66% for the big five traits was achieved using five-fold cross validation. Compared with conventional HMM and support vector machine-based methods, the proposed approach achieved a more favorable performance according to a statistical significance test. The encouraging results confirm the usability of this system for future applications.

Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments.

Activity recognition in smart environments is an evolving research problem due to the advancement and proliferation of sensing, monitoring and actuation technologies to make it possible for large scale and real deployment. While activities in smart home are interleaved, complex and volatile; the number of inhabitants in the environment is also dynamic. A key challenge in designing robust smart home activity recognition approaches is to exploit the users' spatiotemporal behavior and location, focus on the availability of multitude of devices capable of providing different dimensions of information and fulfill the underpinning needs for scaling the system beyond a single user or a home environment. In this paper, we propose a hybrid approach for recognizing complex activities of daily living (ADL), that lie in between the two extremes of intensive use of body-worn sensors and the use of ambient sensors. Our approach harnesses the power of simple ambient sensors (e.g., motion sensors) to provide additional 'hidden' context (e.g., room-level location) of an individual, and then combines this context with smartphone-based sensing of micro-level postural/locomotive states. The major novelty is our focus on multi-inhabitant environments, where we show how the use of spatiotemporal constraints along with multitude of data sources can be used to significantly improve the accuracy and computational overhead of traditional activity recognition based approaches such as coupled-hidden Markov models. Experimental results on two separate smart home datasets demonstrate that this approach improves the accuracy of complex ADL classification by over 30 %, compared to pure smartphone-based solutions.