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Analysis and effects of smart home dataset characteristics for daily life activity recognition

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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. With the development of machine learning algorithms for activity classification, dataset is significantly important for algorithms testing and validation. Collection of real data is a challenging process due to involved budget, human resources, and annotation cost that’s why mostly researchers prefer to utilize existing datasets for evaluation purposes. However, openly available smart home datasets indicate variation in terms of performed activities, deployed sensors, and environment settings. Unfortunately, the analysis of existing datasets characteristic is a bottleneck for researchers while selecting datasets of their intent. In this paper, we develop a Framework for Smart Homes Dataset Analysis (FSHDA) to reflect their diverse dimensions in predefined format. It analyzes a list of data dimensions that covers the variations in time, activities, sensors, and inhabitants. For validation, we examine the effects of proposed data dimension on state-of-the-art activity recognition techniques. The results show that dataset dimensions highly affect the classifiers’ individual activity label assignments and their overall performances. The outcome of our study is helpful for upcoming researchers to develop a better understanding about the smart home datasets characteristics with classifier’s performance.

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Notes

  1. http://uclab.khu.ac.kr/ext/iram/FSHDA.

  2. https://sites.google.com/site/tim0306/datasets.

  3. http://courses.media.mit.edu/2004fall/mas622j/04.projects/home/.

  4. The scope of variables used to describe the functionality of the classifiers is limited to this section.

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Acknowledgements

This research was supported by the MSIP (Ministry of Science, ICT&Future Planning), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2013-(H0301-13-2001)).

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Authors and Affiliations

  1. Department of Computer Engineering, Kyung Hee University, Seoul, South Korea

    Iram Fatima, Muhammad Fahim, Young-Koo Lee & Sungyoung Lee

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  1. Iram Fatima
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  2. Muhammad Fahim
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  3. Young-Koo Lee
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  4. Sungyoung Lee
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Correspondence to Young-Koo Lee.

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Fatima, I., Fahim, M., Lee, YK. et al. Analysis and effects of smart home dataset characteristics for daily life activity recognition. J Supercomput 66, 760–780 (2013). https://doi.org/10.1007/s11227-013-0978-8

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