Judah, G, de Witt Huberts, J, Drassal, A and Aunger, R. 2017. The development and validation of a Real Time Location System to reliably monitor everyday activities in natural contexts. [Online]. Figshare. Available from: https://doi.org/10.1371/journal.pone.0171610
Judah, G, de Witt Huberts, J, Drassal, A and Aunger, R. The development and validation of a Real Time Location System to reliably monitor everyday activities in natural contexts [Internet]. Figshare; 2017. Available from: https://doi.org/10.1371/journal.pone.0171610
Judah, G, de Witt Huberts, J, Drassal, A and Aunger, R (2017). The development and validation of a Real Time Location System to reliably monitor everyday activities in natural contexts. [Data Collection]. Figshare. https://doi.org/10.1371/journal.pone.0171610
Description
The accurate measurement of behaviour is vitally important to many disciplines and practitioners of various kinds. While different methods have been used (such as observation, diaries, questionnaire), none are able to accurately monitor behaviour over the long term in the natural context of people's own lives. The aim of this work was therefore to develop and test a reliable system for unobtrusively monitoring various behaviours of multiple individuals within the same household over a period of several months. A commercial Real Time Location System was adapted to meet these requirements and subsequently validated in three households by monitoring various bathroom behaviours. The results indicate that the system is robust, can monitor behaviours over the long-term in different households and can reliably distinguish between individuals. Precision rates were high and consistent. Recall rates were less consistent across households and behaviours, although recall rates improved considerably with practice at set-up of the system. The achieved precision and recall rates were comparable to the rates observed in more controlled environments using more valid methods of ground truthing. These initial findings indicate that the system is a valuable, flexible and robust system for monitoring behaviour in its natural environment that would allow new research questions to be addressed.
Keywords
Data capture method | Other |
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Date (Date published in a 3rd party system) | 14 February 2017 |
Language(s) of written materials | English |
Data Creators | Judah, G, de Witt Huberts, J, Drassal, A and Aunger, R |
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Participating Institutions | London School of Hygiene & Tropical Medicine, London, United Kingdom, Imperial College, London, United Kingdom, Potsdam University, Potsdam, Germany, Washington State University, Pullman, Washington, United States of America |
Funders |
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Date Deposited | 24 Feb 2017 16:39 |
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Last Modified | 15 Feb 2022 19:12 |
Publisher | Figshare |
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Data / Code
Filename: S1_Dataset.xlsx
Description: Data household 1 (MS Excel). doi:10.1371/journal.pone.0171610.s001
Content type: Dataset
File size: 56kB
Mime-Type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
Filename: S1_Table.docx
Description: Recognition rates for each household (MS Word). doi:10.1371/journal.pone.0171610.s004
Content type: Textual content
File size: 83kB
Mime-Type: application/vnd.openxmlformats-officedocument.wordprocessingml.document
Filename: S2_Dataset.xlsx
Description: Data household 2 (MS Excel). doi:10.1371/journal.pone.0171610.s002
Content type: Dataset
File size: 30kB
Mime-Type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
Filename: S3_Dataset.xlsx
Description: Data household 3 (MS Excel). doi:10.1371/journal.pone.0171610.s003
Content type: Dataset
File size: 46kB
Mime-Type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet