SensCare: Semi-automatic Activity Summarization System for Elderly Care
The fast growing mobile sensor technology makes sensor-based lifelogging system attractive to the remote elderly care. However, existing lifelogging systems are weak at generating meaningful activity summaries from heterogeneous sensor data which significantly limits the usability of lifelogging sys...
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Published in | Mobile Computing, Applications, and Services pp. 1 - 19 |
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Main Authors | , , , |
Format | Book Chapter |
Language | English |
Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
2012
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Series | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering |
Subjects | |
Online Access | Get full text |
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Summary: | The fast growing mobile sensor technology makes sensor-based lifelogging system attractive to the remote elderly care. However, existing lifelogging systems are weak at generating meaningful activity summaries from heterogeneous sensor data which significantly limits the usability of lifelogging systems in practice. In this paper, we introduce SensCare, a semi-automatic lifelog summarization system for elderly care. From various sensor information collected from mobile phones carried by elderlies, SensCare fuses the heterogeneous sensor information and automatically segments/recognizes user’s daily activities in a hierarchical way. With a few human annotations, SensCare generates summaries of data collected from activties performed by the elderly. SensCare addresses three challenges in sensor-based elderly care systems: the rarity of activity labels, the uncertainty of activity granularities, and the difficulty of multi-dimensional sensor fusion. We conduct a set of experiments with users carrying a smart phone for multiple days and evaluate the effectiveness of the automatic summary. With proper sensor configuration, the phone can continue to monitor user’s activities for more than 24 hours without charging. SensCare also demonstrates that unsupervised hierarchical activity segmentation and semi-automatic summarization can be achieved with reasonably good accuracy (average F1 score 0.65) and the system is very useful for users to recall what has happened in their daily lives. |
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ISBN: | 3642323197 9783642323195 |
ISSN: | 1867-8211 1867-822X |
DOI: | 10.1007/978-3-642-32320-1_1 |