Effects of Smart Home Dataset Characteristics on Classifiers Performance for Human Activity Recognition
Over the last few years, activity recognition in the smart home has become an active research area due to the wide range of human centric-applications. A list of machine learning algorithms is available for activity classification. Datasets collected in s
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Abstract Over the last few years, activity recognition in the smart home has become an active research area due to the wide range of human centric-applications. A list of machine learning algorithms is available for activity classification. Datasets collected in smart homes poses unique challenges to these methods for classification because of their high dimensionality, multi-class activities and various deployed sensors. In fact the nature of dataset plays considerable role in recognizing the activities accurately for a particular classifier. In this paper, we evaluated the effects of smart home datasets characteristics on state-of-the-art activity recognition techniques. We applied probabilistic and statistical methods such as the Artificial Neural network, Hidden Markov Model, Conditional Random Field, and Support Vector Machines. The four real world datasets are selected from three most recent and publically available smart home projects. Our experimental results show that how the performance of activity classifiers are influenced by the dataset characteristics. The outcome of our study will be helpful for upcoming researchers to develop a better understanding about the smart home datasets characteristics in combination with classifier’s performance.
I. Fatima (&) M. Fahim Y.-K. Lee S. Lee Department of Computer Engineering, Kyung Hee University, Yongin, Korea e-mail: [email protected] M. Fahim e-mail: [email protected] Y.-K. Lee e-mail: [email protected] S. Lee e-mail: [email protected]
S.-S. Yeo et al. (eds.), Computer Science and its Applications, Lecture Notes in Electrical Engineering 203, DOI: 10.1007/978-94-007-5699-1_28, Ó Springer Science+Business Media Dordrecht 2012
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1 Introduction A smart home is an intelligent agent that perceives state of resident and the physical environments using sensors. Recent advancements in the field of machine learning and data mining have enabled activity recognition research using smart homes sensing data to play a direct role in improving the general quality of health care. It is one of the best solutions to provide a level of independence and comfort in the homes of elderly people rather than requiring them to reside at health care centers [1]. The advancement of sensor technology has proven itself to be robust, cost-effective, easy to install and less intrusive for inhabitants. This fact is supported by a large number of applications developed using activity recognition to provide solutions to a number of real-world problems such as remote health monitoring, life style analysis, interaction monitoring, and behavior mining [2, 3]. A diverse set of machine learning and data mining algorithms have been previously used to identify the performed activities from the smart home datasets. The quest to optimize the performance of classifiers has a long and varied history. The diverse characterized data of smart homes require intelligent machine learning and data mining algorithms for automated analysis in order to make
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