On-Line Inference Comparison with Markov Logic Network Engines for Activity Recognition in AAL Environments

2016 ◽  
Author(s):  
Manuel Fernandez-Carmona ◽  
Nicola Bellotto
Keyword(s):  
Activity Recognition ◽  
Markov Logic ◽  
Logic Network ◽  
On Line ◽  
Markov Logic Network

Hierarchical activity recognition for dementia care using Markov Logic Network

2014 ◽  
Vol 19 (2) ◽  
pp. 271-285 ◽  
Author(s):  
K. S. Gayathri ◽  
Susan Elias ◽  
Balaraman Ravindran
Keyword(s):  
Activity Recognition ◽  
Dementia Care ◽  
Markov Logic ◽  
Logic Network ◽  
Markov Logic Network

scholarly journals A hybrid approach to inferring the Internet of Things for complex activity recognition

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Qingjuan Li ◽  
Huansheng Ning ◽  
Tao Zhu ◽  
Shan Cui ◽  
Liming Chen
Keyword(s):  
Missing Data ◽  
Internet Of Things ◽  
Activity Recognition ◽  
Daily Activity ◽  
Hybrid Approach ◽  
The Internet ◽  
Markov Logic ◽  
Logic Network ◽  
Markov Logic Network ◽  
The Internet Of Things

AbstractWith the rapid development and large-scale uptake of the Internet of Things, smart home is evolving from a vision towards a realistically viable solution for assisted living. Activity recognition is one of the fundamental tasks in order to provide accurate and timely assistance and service. As daily living scenarios are full of similar activities, missing data, and noise, inferring complex activities using knowledge-driven reasoning algorithms suffers from several drawbacks, e.g., real-time raw sensor data segmentation, poor generalization, higher computational complexity, and scalability. To address these problems, this paper proposes a hybrid approach to complex daily activity recognition by merging the first-order logic and probability graphic modeling. Specifically, we develop a novel “Markov logic network” combining data-driven multi-feature and simplified rule-based modeling and inference, thus enabling and supporting the applicability and robustness of daily activity recognition. To evaluate the approach and associated methods, we design a testing scenario with a number of similar activity groups, missing data, or disturbance test datasets in a multi-modeling sensor scene. Initial results show our approach outperforms the traditional approach with a better accuracy in the situations of similar activities with missing data and noise disturbance. Experiments are also conducted to compare the Gibbs sampling and MC-SAT sampling algorithms for Markov logic network, and the results show that the Gibbs is better in our experimental settings.

scholarly journals Learning a Markov Logic network for supervised gene regulatory network inference

2013 ◽  
Vol 14 (1) ◽  
Author(s):  
Céline Brouard ◽  
Christel Vrain ◽  
Julie Dubois ◽  
David Castel ◽  
Marie-Anne Debily ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Inference ◽  
Gene Regulatory Network Inference ◽  
Markov Logic ◽  
Logic Network ◽  
Gene Regulatory ◽  
Markov Logic Network
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