Activity Recognition from Call Detail Record: Relation Between Mobile Behavior Pattern and Social Attribute Using Hierarchical Conditional Random Fields

2010 ◽  
Author(s):  
Chen Zhou ◽  
Zhengguang Xu ◽  
Benxiong Huang
Keyword(s):  
Activity Recognition ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Behavior Pattern

Semi-Markov conditional random fields for accelerometer-based activity recognition

2010 ◽  
Vol 35 (2) ◽  
pp. 226-241 ◽  
Author(s):  
La The Vinh ◽  
Sungyoung Lee ◽  
Hung Xuan Le ◽  
Hung Quoc Ngo ◽  
Hyoung Il Kim ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Random Fields ◽  
Conditional Random Fields

scholarly journals Human activity recognition using conditional random fields and privileged information

2016 ◽  
Author(s):  
Μιχαήλ Βρίγκας
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Human Activity Recognition ◽  
Privileged Information

Η διατριβή ασχολείται με το πρόβλημα της αναγνώρισης της ανθρώπινης δραστηριότητας από εικονοσειρές και απλές εικόνες, το οποίο ανήκει στην ευρύτερη περιοχή της υπολογιστικής όρασης. Για την επίλυση του προβλήματος χρησιμοποιήθηκαν τα υπό συνθήκη τυχαία πεδία συνδυάζοντας δεδομένα από πολλαπλές πηγές. Επιπλέον, προτάθηκε μια καινούρια μέθοδος ταξινόμησης που βασίζεται στην προνομιακή πληροφορία, η oποία δίδεται ως επιπλέων είσοδος στο μοντέλο και είναι διαθέσιμη μόνο στην φάση της εκπαίδευσης αλλά όχι στην φάση του ελέγχου. Τα πειραματικά αποτελέσματα δείχνουν ότι αυτού του είδους η πληροφορία βοηθά στο να δημιουργήσουμε έναν ισχυρότερο ταξινομητή απ' ότι κάποιος θα μάθαινε χωρίς αυτήν, ενώ αυξάνει σημαντικά την ακρίβεια της αναγνώρισης του μοντέλου.

scholarly journals Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Muhammad Hameed Siddiqi ◽  
Madallah Alruwaili ◽  
Amjad Ali ◽  
Saad Alanazi ◽  
Furkh Zeshan
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Random Fields ◽  
Classification Accuracy ◽  
Conditional Random Fields ◽  
Gaussian Mixture ◽  
Human Activity Recognition ◽  
Substantial Improvement ◽  
Learning Method ◽  
Proposed Model

In healthcare, the analysis of patients’ activities is one of the important factors that offer adequate information to provide better services for managing their illnesses well. Most of the human activity recognition (HAR) systems are completely reliant on recognition module/stage. The inspiration behind the recognition stage is the lack of enhancement in the learning method. In this study, we have proposed the usage of the hidden conditional random fields (HCRFs) for the human activity recognition problem. Moreover, we contend that the existing HCRF model is inadequate by independence assumptions, which may reduce classification accuracy. Therefore, we utilized a new algorithm to relax the assumption, allowing our model to use full-covariance distribution. Also, in this work, we proved that computation wise our method has very much lower complexity against the existing methods. For the experiments, we used four publicly available standard datasets to show the performance. We utilized a 10-fold cross-validation scheme to train, assess, and compare the proposed model with the conditional learning method, hidden Markov model (HMM), and existing HCRF model which can only use diagonal-covariance Gaussian distributions. From the experiments, it is obvious that the proposed model showed a substantial improvement with p value ≤0.2 regarding the classification accuracy.

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