scholarly journals A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

2017 ◽  
Vol 4 (4) ◽  
pp. 54 ◽  
Author(s):  
Ahmad Jalal ◽  
Shaharyar Kamal ◽  
Daijin Kim
Keyword(s):  
Health Care ◽  
Hidden Markov Models ◽  
Activity Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Human Detection ◽  
Monitoring Systems ◽  
Depth Video ◽  
Health Care Monitoring

Better Knowledge for Better Health Services

2011 ◽  
pp. 233-255
Author(s):  
Stefano De Luca ◽  
Enrico Memo
Keyword(s):  
Data Mining ◽  
Health Care ◽  
Clinical Practice ◽  
Disease Management ◽  
Hidden Markov Models ◽  
Practice Guidelines ◽  
Markov Models ◽  
Hidden Markov ◽  
The Right ◽  
The Given

The expenses in Health Care are an important portion of the overall expenses of every country, so it is very important to determine if the given cares are the right ones. This work is about a methodology, Health Discoverer, and a consequent software, aimed to disease management and to the measure of appropriateness of cares, and in particular is about the data mining techniques used to verify Clinical Practice Guidelines (CPGs) compliance and the discovery of new, better guidelines. The work is based on Quality Records, episode parsing using Ontologies and Hidden Markov Models.

scholarly journals Activity Recognition for Incomplete Spinal Cord Injury Subjects Using Hidden Markov Models

2018 ◽  
Vol 18 (15) ◽  
pp. 6369-6374 ◽  
Author(s):  
Pichleap Sok ◽  
Ting Xiao ◽  
Yohannes Azeze ◽  
Arun Jayaraman ◽  
Mark V. Albert
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Hidden Markov Models ◽  
Activity Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Incomplete Spinal Cord Injury ◽  
Cord Injury

Using hidden Markov models to infer vessel activities in the snow crab (Chionoecetes opilio) fixed gear fishery and their application to catch standardization

2014 ◽  
Vol 71 (12) ◽  
pp. 1817-1829 ◽  
Author(s):  
Colin Charles ◽  
Darren Gillis ◽  
Elmer Wade
Keyword(s):  
Hidden Markov Models ◽  
Generalized Linear Model ◽  
Markov Models ◽  
Hidden Markov ◽  
Snow Crab ◽  
Monitoring Systems ◽  
Chionoecetes Opilio ◽  
Automated Tracking ◽  
Abundance Estimates ◽  
Catch Rates

Tracking vessel movements has become increasingly important in fisheries research to identify fishing grounds, monitor responses to area closures, and other actions for fishery managers. Vessel monitoring systems (VMS) have given fishery managers and researchers the ability to study vessel interactions by automated tracking of vessels throughout fishing seasons. The high spatial and temporal resolution obtained from VMS records in the Gulf of St. Lawrence snow crab (Chionoecetes opilio) fishery provides information on movement patterns and fishing locations. With the use of hidden Markov models (HMM), we inferred behaviours exhibited by the fishermen during the course of fishing trips and related these behaviours to catch rates across years with varying abundance estimates. The HMM classified three behavioural states in the VMS data that were identified with travelling, setting traps in novel locations (new sets), and retrieving previously set traps (resets). Catches within a trip were modeled by combining VMS-based estimates of these behaviours with logbook information in a generalized linear model. Our model demonstrates that behavioural variables can contribute to the standardization of catch similar to classical trip and vessel variables used in constructing abundance indices.

Sign in / Sign up

Export Citation Format

Share Document