scholarly journals An empirical mode decomposition based hidden Markov model approach for detection of Bryde's whale pulse calls

2020 ◽  
Vol 147 (2) ◽  
pp. EL125-EL131
Author(s):  
Olayinka O. Ogundile ◽  
Ayinde M. Usman ◽  
Daniel J. J. Versfeld
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Empirical Mode Decomposition ◽  
Hidden Markov ◽  
Mode Decomposition ◽  
Model Approach

scholarly journals Intelligent health monitoring system based on smart clothing

2018 ◽  
Vol 14 (8) ◽  
pp. 155014771879431 ◽  
Author(s):  
Chung-Chih Lin ◽  
Chih-Yu Yang ◽  
Zhuhuang Zhou ◽  
Shuicai Wu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Empirical Mode Decomposition ◽  
Monitoring System ◽  
Health Monitoring ◽  
Hidden Markov ◽  
Fall Detection ◽  
Health Monitoring System ◽  
Smart Clothing ◽  
Mode Decomposition

In this study, we proposed an intelligent health monitoring system based on smart clothing. The system consisted of smart clothing and sensing component, care institution control platform, and mobile device. The smart clothing is a wearable device for electrocardiography signal collection and heart rate monitoring. The system integrated our proposed fast empirical mode decomposition algorithm for electrocardiography denoising and hidden Markov model–based algorithm for fall detection. Eight kinds of services were provided by the system, including surveillance of signs of life, tracking of physiological functions, monitoring of the activity field, anti-lost, fall detection, emergency call for help, device wearing detection, and device low battery warning. The performance of fast empirical mode decomposition and hidden Markov model were evaluated by experiment I (fast empirical mode decomposition evaluation) and experiment II (fall detection), respectively. The accuracy and sensitivity of R-peak detection using fast empirical mode decomposition were 96.46% and 98.75%, respectively. The accuracy, sensitivity, and specificity of fall detection using hidden Markov model were 97.92%, 90.00%, and 99.50%, respectively. The system was evaluated in an elderly long-term care institution in Taiwan. The results of the satisfaction survey showed that both the caregivers and the elders are willing to use the proposed intelligent health monitoring system. The proposed system may be used for long-term health monitoring.

scholarly journals Análisis estocástico de señales vibratorias de motores de inducción para la detección de fallas usando descomposición de modo empírico

Tecnura ◽  
2015 ◽  
Vol 19 (44) ◽  
pp. 83
Author(s):  
Alejandro Rivera Roldán ◽  
Miguel Alberto Becerra Botero ◽  
Jaime Alberto Guzmán Luna
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Empirical Mode Decomposition ◽  
Hidden Markov ◽  
Mode Decomposition

En este artículo se presenta un análisis de vibraciones en motores de inducción por medio de Modelos Ocultos de Markov (Hidden Markov Model - HMM) aplicado a características obtenidas de la Descomposición de Modo Empírico (Empirical Mode Decomposition - EMD) y transformada de Hilbert-Huang de señales de vibración obtenidas en las coordenadas x y y, con el fin de detectar fallas de funcionamiento en rodamientos y barras.  Además se presenta un análisis comparativo de la capacidad de las señales de vibración en dirección x y en dirección y, para aportar información en la detección de fallas. Así, un HMM ergódico inicializado y entrenado por medio del algoritmo de máxima esperanza, con convergencia en 10e-7 y un máximo de iteraciones de 100, se aplicó sobre el espacio de características y su desempeño fue determinado mediante validación cruzada 80-20 con 30 fold, obteniendo un alto desempeño para la detección de fallas en términos de exactitud.

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