scholarly journals Real-time human identification using a pyroelectric infrared detector array and hidden Markov models

2006 ◽  
Vol 14 (15) ◽  
pp. 6643 ◽  
Author(s):  
Jian-Shuen Fang ◽  
Qi Hao ◽  
David J. Brady ◽  
Bob D. Guenther ◽  
Ken Y. Hsu
Keyword(s):  
Hidden Markov Models ◽  
Real Time ◽  
Markov Models ◽  
Hidden Markov ◽  
Infrared Detector ◽  
Human Identification ◽  
Detector Array ◽  
Infrared Detector Array

scholarly journals Hidden Markov Models for Real-Time Estimation of Corn Progress Stages Using MODIS and Meteorological Data

2013 ◽  
Vol 5 (4) ◽  
pp. 1734-1753 ◽  
Author(s):  
Yonglin Shen ◽  
Lixin Wu ◽  
Liping Di ◽  
Genong Yu ◽  
Hong Tang ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Real Time ◽  
Markov Models ◽  
Hidden Markov ◽  
Meteorological Data ◽  
Time Estimation ◽  
Real Time Estimation

scholarly journals Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent’s Location Using Hidden Markov Models

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Cédric Beaulac ◽  
Fabrice Larribe
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Hidden Markov Models ◽  
Real Time ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Estimation ◽  
Supervised Machine Learning ◽  
Real Time Estimation ◽  
Mobile Target

We propose to use a supervised machine learning technique to track the location of a mobile agent in real time. Hidden Markov Models are used to build artificial intelligence that estimates the unknown position of a mobile target moving in a defined environment. This narrow artificial intelligence performs two distinct tasks. First, it provides real-time estimation of the mobile agent’s position using the forward algorithm. Second, it uses the Baum–Welch algorithm as a statistical learning tool to gain knowledge of the mobile target. Finally, an experimental environment is proposed, namely, a video game that we use to test our artificial intelligence. We present statistical and graphical results to illustrate the efficiency of our method.

Real-Time Multistep Attack Prediction Based on Hidden Markov Models

2020 ◽  
Vol 17 (1) ◽  
pp. 134-147 ◽  
Author(s):  
Pilar Holgado ◽  
Victor A. Villagra ◽  
Luis Vazquez
Keyword(s):  
Hidden Markov Models ◽  
Real Time ◽  
Markov Models ◽  
Hidden Markov

scholarly journals Continuous Operator Authentication for Teleoperated Systems Using Hidden Markov Models

2022 ◽  
Vol 6 (1) ◽  
pp. 1-25
Author(s):  
Junjie Yan ◽  
Kevin Huang ◽  
Kyle Lindgren ◽  
Tamara Bonaci ◽  
Howard J. Chizeck
Keyword(s):  
Hidden Markov Models ◽  
Real Time ◽  
Haptic Feedback ◽  
Markov Models ◽  
Hidden Markov ◽  
Continuous Operator ◽  
Human Motion ◽  
Skill Assessment ◽  
Impersonation Attack ◽  
Working Set

In this article, we present a novel approach for continuous operator authentication in teleoperated robotic processes based on Hidden Markov Models (HMM). While HMMs were originally developed and widely used in speech recognition, they have shown great performance in human motion and activity modeling. We make an analogy between human language and teleoperated robotic processes (i.e., words are analogous to a teleoperator’s gestures, sentences are analogous to the entire teleoperated task or process) and implement HMMs to model the teleoperated task. To test the continuous authentication performance of the proposed method, we conducted two sets of analyses. We built a virtual reality (VR) experimental environment using a commodity VR headset (HTC Vive) and haptic feedback enabled controller (Sensable PHANToM Omni) to simulate a real teleoperated task. An experimental study with 10 subjects was then conducted. We also performed simulated continuous operator authentication by using the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). The performance of the model was evaluated based on the continuous (real-time) operator authentication accuracy as well as resistance to a simulated impersonation attack. The results suggest that the proposed method is able to achieve 70% (VR experiment) and 81% (JIGSAWS dataset) continuous classification accuracy with as short as a 1-second sample window. It is also capable of detecting an impersonation attack in real-time.

HUMAN IDENTIFICATION BASED ON GAIT MODELING

2007 ◽  
Vol 04 (01) ◽  
pp. 27-38 ◽  
Author(s):  
BUFU HUANG ◽  
MENG CHEN ◽  
KA KEUNG LEE ◽  
YANGSHENG XU
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Human Identification ◽  
Performance Criterion ◽  
Human Gait ◽  
Facial Features ◽  
Machine Learning Method ◽  
Gait Performance ◽  
Dynamic Gait

Human gait is a dynamic biometrical feature which is complex and difficult to imitate. It is unique and more secure than static features such as passwords, fingerprints and facial features. In this paper, we present intelligent shoes for human identification based on human gait modeling and similarity evaluation with hidden Markov models (HMMs). Firstly we describe the intelligent shoe system for collecting human dynamic gait performance. Using the proposed machine learning method hidden Markov models, an individual wearer's gait model is derived and we then demonstrate the procedure for recognizing different wearers by analyzing the corresponding models. Next, we define a hidden-Markov-model-based similarity measure which allows us to evaluate resultant learning models. With the most likely performance criterion, it will help us to derive the similarity of individual behavior and its corresponding model. By utilizing human gait modeling and similarity evaluation based on hidden Markov models, the proposed method has produced satisfactory results for human identification during testing.

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