Assessing Sensorimotor Problems Via Bayesian Theory and Hidden Markov Models

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
José Baca ◽  
Juan Martinez ◽  
Scott A. King
Keyword(s):  
Motor Control ◽  
Bayesian Statistics ◽  
Motor Skills ◽  
Graphical Model ◽  
Mixed Reality ◽  
Markov Models ◽  
Hidden Markov ◽  
Early Stage ◽  
Continuous Assessment ◽  
Main Components

Abstract This work introduces a novel framework that combines Bayesian Statistics for motor control with a probabilistic graphical model to estimate sensorimotor problems. This problem is relevant because as we age, our motor skills tend to decay. A person with this type of problems finds difficult to perform even simple tasks such as walking, cooking, and driving. They become challenging activities due to the alterations to the motor control, which might lead to accidents or injuries. Therefore, the continuous assessment of the sensorimotor functions of a person could help in identifying potential problems at an early stage. This framework aims to provide a substantial estimation of the presence, or absence, of a sensorimotor problem over time. Our strategy is based on the integration of three main components, i.e., data collection during the execution of basic activities via mixed reality, estimation of coordination under uncertainty via Bayesian statistics, and the probability estimation of a sensorimotor problem at different instances of time via hidden Markov model (HMM).

scholarly journals Probabilistic Independence Networks for Hidden Markov Probability Models

1997 ◽  
Vol 9 (2) ◽  
pp. 227-269 ◽  
Author(s):  
Padhraic Smyth ◽  
David Heckerman ◽  
Michael I. Jordan
Keyword(s):  
Speech Recognition ◽  
Graphical Models ◽  
Statistical Physics ◽  
Graphical Model ◽  
Markov Models ◽  
Hidden Markov ◽  
Probability Models ◽  
Model Framework ◽  
Estimation Algorithms ◽  
Special Cases

Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas, including statistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been developed relatively independently in these research communities. In this paper we explore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper presents a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general inference algorithms for arbitrary PINs. Furthermore, the existence of inference and estimation algorithms for more general graphical models provides a set of analysis tools for HMM practitioners who wish to explore a richer class of HMM structures. Examples of relatively complex models to handle sensor fusion and coarticulation in speech recognition are introduced and treated within the graphical model framework to illustrate the advantages of the general approach.

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

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.

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