Fuzzy Hidden Markov Model Research Articles

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human–computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM’s uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.

Driver Behavior Prediction Based on Environmental Observation Using Fuzzy Hidden Markov Model

The development of autonomous vehicle systems has progressed rapidly in recent years. One challenge that persists is the capability of the autonomous system to respond to human drivers. Human behavior is an integral part of driving; thus, driver behavior determines changing lanes and speed adjustments. However, human behavior is unpredictable and immeasurable. Some traffic accidents are caused due to the erratic behavior of the driver. Although, traffic laws, such as in Indonesia, regulate the use of lanes concerning the vehicle’s speed. The drivers’ behavior in the lane is more likely to be influenced by the regulation. This paper proposes a novel method of predicting drivers’ behavior by utilizing the concept of fuzzy Hidden Markov Model (fuzzy HMM). HMM has been proven reliable in predicting human behavior by observing measurable states to determine unmeasurable hidden states. The use of fuzzy logic is to mimic the way that humans perceive the speeds of other vehicles. The fuzzy logic determines the relative observed state of other vehicles according to the measured velocity of an ego vehicle and the observed state of observed vehicles. Observation data is obtained by equipping an ego vehicle with an action camera. The observed data, in the form of a video, is then discretized every 2 seconds. The resulting sequence of images is processed to determine several variables: speed and state of the observed vehicles (lane position and speed) and the time instance of the observation. The fuzzy HMM is generated based on observational data. A predictor created using fuzzy HMM equipped with a training and prediction algorithm successfully predicts the behavior of other drivers on the road.

Speech recognition using HMM and Soft Computing

The region of speech acknowledgment is one of the fascinating fields with regard to speech signal handling. Accomplishing precision and strength is an extremely troublesome limitation to different natural elements. Reformist work and audits in the speech acknowledgment application have been received utilizing fuzzy HMM, as one of the procedures to improve the acknowledgment exactness. This survey paper audits the different ideas of fuzzy HMM strategy and its applications to speech signal handling territory. Since the idea of a speech signal is dubious, it doesn't handle consistency at untouched stretches. To manage this dubiousness and vulnerabilities, numerous scientists have proposed fuzzy HMM is one of the better strategy to break down the speech signals. This paper presents the writing work accessible identified with speech acknowledgment utilizing fuzzy HMM procedures.

Fuzzy‐HMM modeling for emotion detection using electrocardiogram signals

AbstractIn this paper, a Fuzzy Hidden Markov Model (FHMM) for electrocardiogram (ECG)‐based emotion recognition is proposed. The FHMM model is modeled using the fuzzy membership of each class of feature vectors to compute the elements of the matric in the model. Each element in the matric is determined by the two nearest classes of feature vectors with fuzzy classification to avoid the winner‐takes‐all situation that usually happens in tradition discrete Hidden Markov Models (HMM) and that reduces the accuracy of the modeling results. The FHMM modeling proposed in this paper can improve the precision of traditional discrete HMM modeling. Also, the features for emotion recognition, which are selected according to those used in prior research, are calculated from ECG signals. The selected features form a feature vector and these are calculated for all ECG signals. Some feature vectors are used to train the FHMM model and the remaining are used for testing. Experimental results show that the proposed FHMM model can achieve impressive improvements on the three indices, sensitivities (Se%), positive predictive value (PPV%), and total classification accuracy (TCA%), compared to traditional HMM. Moreover, compared to some existing studies, the recognition rates of the proposed method are higher. These results verify the efficiency of the proposed method for emotion recognition from ECG signals.

Fuzzy Optimization Techniques by Hidden Markov Model with Interval Type-2 Fuzzy Parameters

Fuzzy hidden Markov model is the efficient way of finding an optimized path among the states where uncertainty exists. Aggregation operators replaced the conventional operators in the fuzzy environment and play a vital role in real-world problems. Here, the aggregation operators namely trapezoidal interval type-2 weighted arithmetic (TpIT2FFWA) and trapezoidal interval type-2 weighted geometric (TpIT2FFWG) operators have been derived and their desired properties also have been proved based on Frank triangular norms. Using the proposed operators and Viterbi algorithm, decision-making process has been analyzed to choose the best medicine company.

Plantar pressure image fusion for comfort fusion in diabetes mellitus using an improved fuzzy hidden Markov model

Diabetes mellitus is a clinical syndrome caused by the interaction of genetic and environmental factors. The change of plantar pressure in diabetic patients is one of the important reasons for the occurrence of diabetic foot. The abnormal increase of plantar pressure is a predictor of the common occurrence of foot ulcers. The feature extraction of plantar pressure distribution will be beneficial to the design and manufacture of diabetic shoes that will be beneficial for early protection of diabetes mellitus patients. In this research, texture-based features of the angular second moment (ASM), moment of inertia (MI), inverse difference monument (IDM), and entropy (E) have been selected and fused by using the up–down algorithm. The fused features are normalized to predict comfort plantar pressure imaging dataset using an improved fuzzy hidden Markov model (FHMM). In FHMM, type-I fuzzy set is proposed and fuzzy Baum–Welch algorithm is also applied to estimate the next features. The results are discussed, and by comparing with other back–forward algorithms and different fusion operations in FHMM. Improved HMMs with up–down fusion using type-I fuzzy definition performs high effectiveness in prediction comfort plantar pressure distribution in an image dataset with an accuracy of 82.2% and the research will be applied to the shoe-last personalized customization in the industry.

(1610-3210) MAN - MACHINE INTERACTION SYSTEM FOR SUBJECT INDEPENDENT SIGN LANGUAGE RECOGNITION USING FUZZY HIDDEN MARKOV MODEL

Sign language recognition has spawned more and more interest in human–computer interaction society. The major challenge that SLR recognition faces now is developing methods that will scale well with increasing vocabulary size with a limited set of training data for the signer independent application. The automatic SLR based on hidden Markov models (HMMs) is very sensitive to gesture's shape information that makes the accurate parameters of the HMM not capable of characterizing the ambiguous distributions of the observations in gesture's features. This paper presents an extension of the HMMs using interval type-2 fuzzy sets (IT2FSs) to produce interval type-2 fuzzy HMMs to model uncertainties of hypothesis spaces (unknown varieties of parameters of the decision function). The benefit of this enlargement is that it can control both the randomness and fuzziness of traditional HMM mapping. Membership function (MF) of type-2 FS is three-dimensional that provides additional degrees of freedom to evaluate HMM's uncertainties. This system aspires to be a solution to the scalability problem, i.e. has real potential for application on a large vocabulary. Furthermore, it does not rely on the use of data gloves or other means as input devices, and operates in isolated signer-independent modes. Experimental results show that the interval type-2 fuzzy HMM has a comparable performance as that of the fuzzy HMM but is more robust to the gesture variation, while it retains almost the same computational complexity as that of the FHMM.

Quantized Control of Markov Jump Nonlinear Systems Based on Fuzzy Hidden Markov Model.

This paper considers the problem of asynchronous guaranteed cost control (GCC) for nonlinear Markov jump systems with stochastic quantization. Hidden Markov model is used to describe the nonsynchronous controller and the random quantization phenomenon. Based on Takagi-Sugeno fuzzy technique and Lyapunov function approach, a sufficient condition is obtained, which can not only ensure the asymptotic stability of the closed-loop system and existence of the desired controller, but also can yield the minimal upper bound of GCC performance. Finally, two examples are provided to demonstrate the correctness and reliability of our developed approaches.

Microscopic abnormality classification of cardiac murmurs using ANFIS and HMM.

Auscultation of heart dispenses identification of the cardiac valves. An electronic stethoscope is used for the acquisition of heart murmurs that is further classified into normal or abnormal murmurs. The process of heart sound segmentation involves discrete wavelet transform to obtain individual components of the heart signal and its separation into systole and diastole intervals. This research presents a novel scheme to develop a semi-automatic cardiac valve disorder diagnosis system. Accordingly, features are extracted using wavelet transform and spectral analysis of input signals. The proposed classification scheme is the fusion of adaptive-neuro fuzzy inference system (ANFIS) and HMM. Both classifiers are trained using the extracted features to correctly identify normal and abnormal heart murmurs. Experimental results thus achieved exhibit that proposed system furnishes promising classification accuracy with excellent specificity and sensitivity. However, the proposed system has fewer classification errors, fewer computations, and lower dimensional feature set to build an intelligent system for detection and classification of heart murmurs.

Functional Uncertainty Analysis of Phasor Measurement Unit Using Fuzzy Hidden Markov Model

ABSTRACTThe phasor measurement units (PMUs) are new age measurement devices that provide frequency measurements and time-aligned phasors (synchrophasors) of current and voltage at selected locations of the electric power grid. These devices are continuously evolving to cater for the growing need of smart grids. In general, the PMU failures are hidden which require appropriate modelling. As they are recent devices with sparse database of stochastic failures, the functional integration of different modules has aleatory and epistemic uncertainties. However, PMUs are descendants of digital recorders and thus reliability parameters of different functional modules are already known due to prior applications. Therefore, performing functional analysis with uncertainties enables us for detailed understanding of their functions and sub-functions. This paper embarks on functional uncertainty analysis using fuzzy hidden Markov model. The methodology provides with optimal path for hidden failures to mitigate propagation of uncertainty amongst the functional blocks.

Human action recognition by fuzzy hidden Markov model

Hidden Markov model (HMM) has been widely applied in human action recognition. In this paper an extension of HMM called fuzzy hidden Markov model (fuzzy HMM) is used for action recognition. It tries to increase the classification performance and decrease the information loss due to feature vector quantisation. Using fuzzy concepts with HMM leads to better recognition of similar actions such as walking, jogging and running. Two feature extraction methods including skeleton and space-time approaches are used for action representation. Actions could be represented efficiently using skeleton features where scene background is plain. Space-time features are extracted directly from video, and therefore avoid possible failures of other pre-processing methods. We propose space-time-based features by considering temporal relation between them. Experimental results show the effectiveness of fuzzy HMM in human action recognition. Moreover, it is shown that fuzzy HMM leads to significant improvement in recognition of similar actions. The accuracy rates of fuzzy HMM in comparison to HMM are incremented 3.33% and 5.59% in Weizmann and KTH datasets respectively.

Human action recognition by fuzzy hidden Markov model

Human action recognition by fuzzy hidden Markov model

Granular transfer learning using type-2 fuzzy HMM for text sequence recognition

Context information plays an important role in text sequence recognition, but it is difficult to harness the uncertainty caused by conflicting implications. In this paper, we propose a novel Granular Transfer (GT) learning with type-2 fuzzy Hidden Markov Model (HMM) called GT2HMM, in which interpretable granules’ representation is introduced to describe the contextual uncertainty for its transfer learning. The correspondences among words are transformed into information granules using fuzzy c-means. To fulfill the utilization of granular information in sequence recognition, we construct a type-2 fuzzy HMM which fuses labeled data and unlabeled observations. With a tunable granularity, correspondence information is refined in a coarse-to-fine manner in GT2HMM. Experiments on transductive and inductive transfer learning in part-of-speech (POS) tagging tasks verify the effectiveness of our proposed GT2HMM.

Hand Gesture Recognition for Sign Language: A New Higher Order Fuzzy HMM Approach

Hand Gesture Recognition for Sign Language: A New Higher Order Fuzzy HMM Approach

Hand Gesture Recognition for Sign Language: A New Higher Order Fuzzy HMM ApproachHand Gesture Recognition for Sign Language: A New Higher Order Fuzzy HMM Approach

Hand Gesture Recognition for Sign Language: A New Higher Order Fuzzy HMM ApproachHand Gesture Recognition for Sign Language: A New Higher Order Fuzzy HMM Approach

Approach to Forecasting Multi-step Attack Based on Fuzzy Hidden Markov Model

Approach to Forecasting Multi-step Attack Based on Fuzzy Hidden Markov Model

Fuzzy Hidden Markov Models for Indonesian Speech Classification

Indonesia has many tribes, so that there are many dialects. Speech classification is difficult if the database uses speech signals from various people who have different characteristics because of gender and dialect. The different characteristics will influence frequency, intonation, amplitude, and period of the speech. It makes the system must be trained for the various templates reference of speech signal. Therefore, this study has been developed for Indonesian speech classification. The solution is a new combination of fuzzy on hidden Markov models. The result shows a new version of fuzzy hiddenMarkovmodels is better than hidden Markov model.

Additive and Nonadditive Fuzzy Hidden Markov Models

We present a novel approach for the development of fuzzy hidden Markov models (FHMMs) by exploiting both additive and nonadditive properties of input fuzzy sets in the fuzzy rules of generalized fuzzy model (GFM). This development utilizes 1) Gaussian mixture model (GMM) to manipulate the mixture parameters for the input fuzzy sets and 2) GFM rules for the inclusion of states in the consequent part to be able to use HMM. Taking the components of Gaussian mixture density conditioned on the past system states and making use of equivalence of GMM with GFM, parameters of the additive and nonadditive FHMMs are estimated using the forward-backward procedure of the Baum-Welch algorithm. The additive and nonadditive FHMMs are validated on three benchmark applications involving time-series prediction, and the results are compared and found to be better than or equal to those of the existing recent fuzzy models.

Fuzzy Qualitative Human Motion Analysis

This paper proposes a fuzzy qualitative approach to vision-based human motion analysis with an emphasis on human motion recognition. It achieves feasible computational cost for human motion recognition by combining fuzzy qualitative robot kinematics with human motion tracking and recognition algorithms. First, a data-quantization process is proposed to relax the computational complexity suffered from visual tracking algorithms. Second, a novel human motion representation, i.e., qualitative normalized template, is developed in terms of the fuzzy qualitative robot kinematics framework to effectively represent human motion. The human skeleton is modeled as a complex kinematic chain, and its motion is represented by a series of such models in terms of time. Finally, experiment results are provided to demonstrate the effectiveness of the proposed method. An empirical comparison with conventional hidden Markov model (HMM) and fuzzy HMM (FHMM) shows that the proposed approach consistently outperforms both HMMs in human motion recognition.

Type-2 fuzzy Gaussian mixture models

This paper presents a new extension of Gaussian mixture models (GMMs) based on type-2 fuzzy sets (T2 FSs) referred to as T2 FGMMs. The estimated parameters of the GMM may not accurately reflect the underlying distributions of the observations because of insufficient and noisy data in real-world problems. By three-dimensional membership functions of T2 FSs, T2 FGMMs use footprint of uncertainty (FOU) as well as interval secondary membership functions to handle GMMs uncertain mean vector or uncertain covariance matrix, and thus GMMs parameters vary anywhere in an interval with uniform possibilities. As a result, the likelihood of the T2 FGMM becomes an interval rather than a precise real number to account for GMMs uncertainty. These interval likelihoods are then processed by the generalized linear model (GLM) for classification decision-making. In this paper we focus on the role of the FOU in pattern classification. Multi-category classification on different data sets from UCI repository shows that T2 FGMMs are consistently as good as or better than GMMs in case of insufficient training data, and are also insensitive to different areas of the FOU. Based on T2 FGMMs, we extend hidden Markov models (HMMs) to type-2 fuzzy HMMs (T2 FHMMs). Phoneme classification in the babble noise shows that T2 FHMMs outperform classical HMMs in terms of the robustness and classification rate. We also find that the larger area of the FOU in T2 FHMMs with uncertain mean vectors performs better in classification when the signal-to-noise ratio is lower.