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

Iram Fatima, Muhammad Fahim, Young Koo Lee*, Sungyoung Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)760-780
Number of pages21
JournalJournal of Supercomputing
Volume66
Issue number2
Early online date11 Jul 2013
DOIs
Publication statusPublished - Nov 2013
Externally publishedYes

Bibliographical note

Funding Information:
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)).

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Activity recognition
  • Classifiers performance
  • Dataset analysis
  • Pervasive sensing
  • Smart home

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture

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