scholarly journals Multi-label classification based ensemble learning for human activity recognition in smart home

2020 ◽  
Vol 12 ◽  
pp. 100324
Author(s):  
Manan Jethanandani ◽  
Abhishek Sharma ◽  
Thinagaran Perumal ◽  
Jieh-Ren Chang
Keyword(s):  
Ensemble Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Smart Home ◽  
Human Activity Recognition

Human activity recognition in big data smart home context

2014 ◽  
Author(s):  
Sabrina Azzi ◽  
Abdenour Bouzouane ◽  
Sylvain Giroux ◽  
Cindy Dallaire ◽  
Bruno Bouchard
Keyword(s):  
Big Data ◽  
Activity Recognition ◽  
Human Activity ◽  
Smart Home ◽  
Human Activity Recognition ◽  
Home Context

Head-AR: Human Activity Recognition with Head-Mounted IMU Using Weighted Ensemble Learning

2020 ◽  
pp. 153-167
Author(s):  
Hristijan Gjoreski ◽  
Ivana Kiprijanovska ◽  
Simon Stankoski ◽  
Stefan Kalabakov ◽  
John Broulidakis ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition

scholarly journals Efficient Human Activity Recognition Solving the Confusing Activities Via Deep Ensemble Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 75490-75499 ◽  
Author(s):  
Ran Zhu ◽  
Zhuoling Xiao ◽  
Ying Li ◽  
Mingkun Yang ◽  
Yawen Tan ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition

scholarly journals Feature Selections Using Minimal Redundancy Maximal Relevance Algorithm for Human Activity Recognition in Smart Home Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Hongqing Fang ◽  
Pei Tang ◽  
Hao Si
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Smart Home ◽  
Recognition Accuracy ◽  
Human Activity Recognition ◽  
Minimal Redundancy ◽  
Maximal Relevance ◽  
Relevance Measure ◽  
Accuracy Rates ◽  
Smart Home Environments

In this paper, maximal relevance measure and minimal redundancy maximal relevance (mRMR) algorithm (under D-R and D/R criteria) have been applied to select features and to compose different features subsets based on observed motion sensor events for human activity recognition in smart home environments. And then, the selected features subsets have been evaluated and the activity recognition accuracy rates have been compared with two probabilistic algorithms: naïve Bayes (NB) classifier and hidden Markov model (HMM). The experimental results show that not all features are beneficial to human activity recognition and different features subsets yield different human activity recognition accuracy rates. Furthermore, even the same features subset has different effect on human activity recognition accuracy rate for different activity classifiers. It is significant for researchers performing human activity recognition to consider both relevance between features and activities and redundancy among features. Generally, both maximal relevance measure and mRMR algorithm are feasible for feature selection and positive to activity recognition.

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