scholarly journals An Activity Classification for Vibrotactile Phenomena

2006 ◽  
pp. 145-156 ◽  
Author(s):  
Conor O’Sullivan ◽  
Angela Chang
Keyword(s):  
Activity Classification

Human activity classification using Decision Tree and Naïve Bayes classifiers

2021 ◽  
Author(s):  
Kholoud Maswadi ◽  
Norjihan Abdul Ghani ◽  
Suraya Hamid ◽  
Muhammads Babar Rasheed
Keyword(s):  
Decision Tree ◽  
Human Activity ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Activity Classification

scholarly journals Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 884
Author(s):  
Antonio García-Domínguez ◽  
Carlos E. Galván-Tejada ◽  
Ramón F. Brena ◽  
Antonio A. Aguileta ◽  
Jorge I. Galván-Tejada ◽  
...  
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Activity Classification ◽  
Environmental Sound ◽  
Non Invasive ◽  
Akaike Criterion ◽  
Data Source ◽  
Feature Selection Techniques

Children’s healthcare is a relevant issue, especially the prevention of domestic accidents, since it has even been defined as a global health problem. Children’s activity classification generally uses sensors embedded in children’s clothing, which can lead to erroneous measurements for possible damage or mishandling. Having a non-invasive data source for a children’s activity classification model provides reliability to the monitoring system where it is applied. This work proposes the use of environmental sound as a data source for the generation of children’s activity classification models, implementing feature selection methods and classification techniques based on Bayesian networks, focused on the recognition of potentially triggering activities of domestic accidents, applicable in child monitoring systems. Two feature selection techniques were used: the Akaike criterion and genetic algorithms. Likewise, models were generated using three classifiers: naive Bayes, semi-naive Bayes and tree-augmented naive Bayes. The generated models, combining the methods of feature selection and the classifiers used, present accuracy of greater than 97% for most of them, with which we can conclude the efficiency of the proposal of the present work in the recognition of potentially detonating activities of domestic accidents.

scholarly journals Energy-aware activity classification using wearable sensor networks

2013 ◽  
Author(s):  
Bo Dong ◽  
Alexander Montoye ◽  
Rebecca Moore ◽  
Karin Pfeiffer ◽  
Subir Biswas
Keyword(s):  
Sensor Networks ◽  
Wearable Sensor ◽  
Activity Classification ◽  
Energy Aware
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