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Article

Talk, Text, Tag? Understanding Self-Annotation of Smart Home Data from a User’s Perspective

1
Faculty of Engineering, University of Bristol, Bristol BS8 1UB, UK
2
Institute of Computer Science, University of Rostock, 18059 Rostock, Germany
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2365; https://doi.org/10.3390/s18072365
Received: 7 June 2018 / Revised: 11 July 2018 / Accepted: 12 July 2018 / Published: 20 July 2018
(This article belongs to the Special Issue Annotation of User Data for Sensor-Based Systems)
Delivering effortless interactions and appropriate interventions through pervasive systems requires making sense of multiple streams of sensor data. This is particularly challenging when these concern people’s natural behaviours in the real world. This paper takes a multidisciplinary perspective of annotation and draws on an exploratory study of 12 people, who were encouraged to use a multi-modal annotation app while living in a prototype smart home. Analysis of the app usage data and of semi-structured interviews with the participants revealed strengths and limitations regarding self-annotation in a naturalistic context. Handing control of the annotation process to research participants enabled them to reason about their own data, while generating accounts that were appropriate and acceptable to them. Self-annotation provided participants an opportunity to reflect on themselves and their routines, but it was also a means to express themselves freely and sometimes even a backchannel to communicate playfully with the researchers. However, self-annotation may not be an effective way to capture accurate start and finish times for activities, or location associated with activity information. This paper offers new insights and recommendations for the design of self-annotation tools for deployment in the real world. View Full-Text
Keywords: ground-truth acquisition; self-annotation; labelling; activity logging; location; NFC; smart homes; naturalistic data ground-truth acquisition; self-annotation; labelling; activity logging; location; NFC; smart homes; naturalistic data
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MDPI and ACS Style

Tonkin, E.L.; Burrows, A.; Woznowski, P.R.; Laskowski, P.; Yordanova, K.Y.; Twomey, N.; Craddock, I.J. Talk, Text, Tag? Understanding Self-Annotation of Smart Home Data from a User’s Perspective. Sensors 2018, 18, 2365. https://doi.org/10.3390/s18072365

AMA Style

Tonkin EL, Burrows A, Woznowski PR, Laskowski P, Yordanova KY, Twomey N, Craddock IJ. Talk, Text, Tag? Understanding Self-Annotation of Smart Home Data from a User’s Perspective. Sensors. 2018; 18(7):2365. https://doi.org/10.3390/s18072365

Chicago/Turabian Style

Tonkin, Emma L., Alison Burrows, Przemysław R. Woznowski, Pawel Laskowski, Kristina Y. Yordanova, Niall Twomey, and Ian J. Craddock. 2018. "Talk, Text, Tag? Understanding Self-Annotation of Smart Home Data from a User’s Perspective" Sensors 18, no. 7: 2365. https://doi.org/10.3390/s18072365

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