DocumentCode :
1601406
Title :
Multi-label Learning for Activity Recognition
Author :
Kumar, Rahul ; Qamar, Imroj ; Virdi, Jaskaran Singh ; Krishnan, Narayanan Chatapuram
Author_Institution :
Dept. of Comput. Sci. & Eng., Indian Inst. of Technol. Ropar, Ropar, India
fYear :
2015
Firstpage :
152
Lastpage :
155
Abstract :
Advances in pervasive and ubiquitous computing have resulted in the development of sensors that can be easily deployed in the natural habitat of a human to acquire activity related data. However, inferring meaningful activity information from sensor data is still a challenging problem. This paper addresses the problem of inferring activities that are simultaneously performed by multiple residents in a smart home or single resident performing multiple activities concurrently. The paper formulates this problem as learning multiple activity labels from a sequence of sensor data. It investigates the suitability of multi-label learning algorithms inspired by decision trees as a proposed solution to the problem. The results obtained from the experiments on four benchmarking multi-resident activity datasets clearly indicate the superiority of decision tree ensemble (random forests) based approaches for multi-label learning.
Keywords :
decision trees; home automation; learning (artificial intelligence); pose estimation; sensors; ubiquitous computing; activity recognition; decision tree ensemble based approaches; decision trees; multilabel learning algorithms; multiple activity label learning; multiresident activity dataset benchmarking; natural habitat; pervasive computing; random forests; sensor data; smart home; ubiquitous computing; Algorithm design and analysis; Decision trees; Feature extraction; Intelligent sensors; Measurement; Vegetation; Human Activity Recognition; Multi-label Learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Environments (IE), 2015 International Conference on
Conference_Location :
Prague
Type :
conf
DOI :
10.1109/IE.2015.32
Filename :
7194287
Link To Document :
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