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Tracking and Separation of Smart Home Residents through Ambient Activity Sensors

1
OFFIS Institute for Information Technology, 26122 Oldenburg, Germany
2
School of Medicine and Health Sciences, University of Oldenburg, 26129 Oldenburg, Germany
*
Author to whom correspondence should be addressed.
Presented at the 13th International Conference on Ubiquitous Computing and Ambient Intelligence UCAmI 2019, Toledo, Spain, 2–5 December 2019.
Proceedings 2019, 31(1), 29; https://doi.org/10.3390/proceedings2019031029
Published: 20 November 2019
To enable independent living for people in need of care and to accommodate the increasing demand of ambulant care due to demographic changes, a multitude of systems and applications that monitor activities and health-related data based on ambient sensors commonly found in smart homes have been developed. When such a system is used in a multi-person household, some form of identification or separation of residents is required. Most of these systems require permanent participation in the form of body-worn sensors or a complicated supervised learning procedure which may take hours or days to set up. To resolve this, we study several unsupervised learning approaches for the separation of activity data of multiple residents recorded with ambient, binary sensors such as light barriers and contact switches. We show how various clustering methods on data from a tracking system can, under optimal conditions, separate the activity of two residents with low error rates (<2%, Rand Index of 0 . 959 ). We also show that imprecisions in the underlying tracking algorithm have a significant impact on the clustering performance and that most of these errors can be corrected by adding a single “identifying sensor area” into the environment. As a consequence, activity monitoring applications need to rely less on body-worn sensors, which may be forgotten or biometric sensors, which may be perceived as a violation of privacy.
Keywords: ambient sensing; identification; ambient assisted living; multi-target tracking; smart healthcare; smart monitoring ambient sensing; identification; ambient assisted living; multi-target tracking; smart healthcare; smart monitoring
MDPI and ACS Style

Müller, S.M.; Hein, A. Tracking and Separation of Smart Home Residents through Ambient Activity Sensors. Proceedings 2019, 31, 29.

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