Mood Perception Model for Social Robot Based on Facial and Bodily Expression Using a Hidden Markov Model

2019 ◽  
Vol 31 (4) ◽  
pp. 629-638 ◽  
Author(s):  
Jiraphan Inthiam ◽  
Abbe Mowshowitz ◽  
Eiji Hayashi ◽  
◽  
Keyword(s):  
Social Skill ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Negative Mood ◽  
Human Interaction ◽  
Perceptual Model ◽  
Bodily Expression ◽  
Perception Model ◽  
Hidden States ◽  
Physical Manifestation

In the normal course of human interaction people typically exchange more than spoken words. Emotion is conveyed at the same time in the form of nonverbal messages. In this paper, we present a new perceptual model of mood detection designed to enhance a robot’s social skill. This model assumes 1) there are only two hidden states (positive or negative mood), and 2) these states can be recognized by certain facial and bodily expressions. A Viterbi algorithm has been adopted to predict the hidden state from the visible physical manifestation. We verified the model by comparing estimated results with those produced by human observers. The comparison shows that our model performs as well as human observers, so the model could be used to enhance a robot’s social skill, thus endowing it with the flexibility to interact in a more human-oriented way.

scholarly journals Hidden Markov Model

d'CARTESIAN ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 86 ◽  
Author(s):  
Kezia Tumilaar ◽  
Yohanes Langi ◽  
Altien Rindengan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Learning Problem ◽  
Model Learning ◽  
Observation Sequence ◽  
Evaluation Problem ◽  
Hidden States

Hidden Markov Models (HMM) is a stochastic model and is essentially an extension of Markov Chain. In Hidden Markov Model (HMM)  there are two types states: the observable states and the hidden states. The purpose of this research are to understand how hidden Markov model (HMM) and to understand how the solution of three basic problems on Hidden Markov Model (HMM) which consist of evaluation problem, decoding problem and learning problem.  The result of the research is hidden Markov model can be defined as . The evaluation problem or to compute probability of the observation sequence given the model P(O|) can solved  by Forward-Backward algorithm, the decoding problem or to choose hidden state sequence which is optimal can solved by Viterbi algorithm and learning problem or to estimate hidden Markov model parameter  to maximize P(O|)  can solved by Baum – Welch algorithm. From description above Hidden Markov Model  with state 3  can describe behavior  from the case studies. Key  words: Decoding Problem, Evaluation Problem, Hidden Markov Model, Learning Problem

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

An MEWMA-based segmental multivariate hidden Markov model for degradation assessment and prediction

2021 ◽  
pp. 1748006X2199052
Author(s):  
Yaping Li ◽  
Enrico Zio ◽  
Ershun Pan
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Average Run Length ◽  
Moving Average ◽  
Model Parameters ◽  
Current Time ◽  
Multivariate Processes ◽  
Industrial Systems ◽  
Hidden States ◽  
Multivariate Signals

Degradation is an unavoidable phenomenon in industrial systems. Hidden Markov models (HMMs) have been used for degradation modeling. In particular, segmental HMMs have been developed to model the explicit relationship between degradation signals and hidden states. However, existing segmental HMMs deal only with univariate cases, whereas in real systems, signals from various sensors are collected simultaneously, which makes it necessary to adapt the segmental HMMs to deal with multivariate processes. Also, to make full use of the information from the sensors, it is important to differentiate stable signals from deteriorating ones, but there is no good way for this, especially in multivariate processes. In this paper, the multivariate exponentially weighted moving average (MEWMA) control chart is employed to identify deteriorating multivariate signals. Specifically, the MEWMA statistic is used as a comprehensive indicator for differentiating multivariate observations. Likelihood Maximization is used to estimate the model parameters. To avoid underflow, the forward and backward probabilities are normalized. In order to assess degradation, joint probabilities are defined and derived. Further, the occurrence probability of each degradation state at the current time, as well as in the future, is derived. The Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset of NASA is employed for comparative analysis. In terms of degradation assessment and prediction, the proposed model performs very well in general. By sensitivity analysis, we show that in order to improve further the performance of the method, the weight of the chart should be set relatively small, whereas the method is not sensitive to the change of the in-control average run length (ARL).

scholarly journals The Application of Baum-Welch Algorithm in Multistep Attack

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yanxue Zhang ◽  
Dongmei Zhao ◽  
Jinxing Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Forward Algorithm ◽  
Better Than

The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.

Remaining Useful Life Estimation Based on a Segmental Hidden Markov Model With Continuous Observations

2017 ◽  
Author(s):  
Zhen Chen ◽  
Tangbin Xia ◽  
Ershun Pan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Goodness Of Fit ◽  
Hidden Markov ◽  
Remaining Useful Life ◽  
Likelihood Method ◽  
Distribution Model ◽  
Unknown Parameters ◽  
Useful Life ◽  
Hidden States

In this paper, a segmental hidden Markov model (SHMM) with continuous observations, is developed to tackle the problem of remaining useful life (RUL) estimation. The proposed approach has the advantage of predicting the RUL and detecting the degradation states simultaneously. As the observation space is discretized into N segments corresponding to N hidden states, the explicit relationship between actual degradation paths and the hidden states can be depicted. The continuous observations are fitted by Gaussian, Gamma and Lognormal distribution, respectively. To select a more suitable distribution, model validation metrics are employed for evaluating the goodness-of-fit of the available models to the observed data. The unknown parameters of the SHMM can be estimated by the maximum likelihood method with the complete data. Then a recursive method is used for RUL estimation. Finally, an illustrate case is analyzed to demonstrate the accuracy and efficiency of the proposed method. The result also suggests that SHMM with observation probability distribution which is closer to the real data behavior may be more suitable for the prediction of RUL.

scholarly journals Dynamic Transactions Between News Frames and Sociopolitical Events: An Integrative, Hidden Markov Model Approach

2020 ◽  
Vol 70 (3) ◽  
pp. 335-355 ◽  
Author(s):  
Frederic R Hopp ◽  
Jacob T Fisher ◽  
René Weber
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
News Coverage ◽  
News Frames ◽  
Macro Scale ◽  
News Event ◽  
Hidden States ◽  
Research Domains ◽  
Integrated Study ◽  
Model Approach

Abstract A central goal of news research is to understand the interplay between news coverage and sociopolitical events. Although a great deal of work has elucidated how events drive news coverage, and how in turn news coverage influences societal outcomes, integrative systems-level models of the reciprocal interchanges between these two processes are sparse. Herein, we present a macro-scale investigation of the dynamic transactions between news frames and events using Hidden Markov Models (HMMs), focusing on morally charged news frames and sociopolitical events. Using 3,501,141 news records discussing 504,759 unique events, we demonstrate that sequences of frames and events can be characterized in terms of “hidden states” containing distinct moral frame and event relationships, and that these “hidden states” can forecast future news frames and events. This work serves to construct a path toward the integrated study of the news-event cycle across multiple research domains.

scholarly journals Use of Hidden Markov Models to Identify Background States Behind Risks of Cerebral Infarction and Ischemic Heart Disease

2017 ◽  
Vol 9 (1) ◽  
pp. 24
Author(s):  
Hiroshi Morimoto
Keyword(s):  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Human Health ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Meteorological Factors ◽  
Hidden Markov ◽  
Weather Patterns ◽  
Weather And Human Health ◽  
Hidden States

Cold exposure is often said to trigger the incidence of cerebral infarctions and ischemic heart disease. This association between weather and human health has attracted considerable interest, and has been explored using standard statistical techniques such as regression models. Meteorological factors, such as temperature, are controlled by background systems, notably weather patterns. Therefore, it is reasonable to posit that the incidence of diseases is similarly influenced by a background system. The aim of this paper was to identify and construct these respective background systems. Possible background states or "hidden states", behind the incidence of diseases were derived using the EM and Viterbi algorithms with in the framework of hidden Markov models (HMM). A self-organizing map (SOM) enabled identification of weather patterns, considered as background states behind meteorological factors. These background states were then compared, and the hidden states behind the incidence of diseases were identified by six weather patterns. This finding indicates new evidence of the links between weather and human health, shedding light on the association between changes in the weather and the onset of disease. 

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