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ContextSense: unobtrusive discovery of incremental social context using dynamic bluetooth data

Published: 13 September 2014 Publication History

Abstract

User-centric ambient social contexts can be effectively captured by dynamic bluetooth data. However, conventional approaches for training classifiers struggle with social contexts that are incrementally constructed and continuously discovered in everyday environments. Incremental social contexts can confuse a classifier because it assumes that the number and composition of context classes is fixed throughout training and inference phases. To address this challenge we propose ContextSense, an ELM-based learning method for continuous and unobtrusive discovery of new social contexts incrementally from dynamic bluetooth data. Experimental results show that ContextSense can automatically cope with "incremental social context" classes that appear unpredictably in the real-world.

References

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Azizyan, M., et al. SurroundSense: Mobile Phone Localization via Ambience Fingerprinting. In Proc. MobiCom'09, ACM (2009), 261--272.
[2]
Chen, Y. Q., et al. Surrounding Context and Episode Awareness using Dynamic Bluetooth Data. In Proc. UbiComp'12, ACM (2012), 629--630.
[3]
Chen, Z. Y., et al. Inferring Social Contextual Behavior from Bluetooth Traces. In Proc. UbiComp'13, ACM (2013), 267--270.
[4]
Huang, G.-B., et al. Extreme Learning Machine: Theory and Applications. Neurocomputing 70 (2006), 489--501.
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Kim, D. H., et al. Discovering Semantically Meaningful Places from Pervasive RF-Beacons. In Proc. UbiComp'08, ACM (2008), 21--30.
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Kim, D. H., et al. SensLoc: Sensing Everyday Places and Paths using Less Energy. In Proc. SenSys'10, ACM (2010), 43--56.
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Lu, H., et al. Soundsense: Scalable Sound Sensing for People-Centric Applications on Mobile Phones. In Proc. MobiSys'09, ACM (2009), 165--178.
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Zhao., Z. T., et al. A Class Incremental Extreme Learning Machine for Activity Recognition. Cognitive Computation (2014), 1--9.

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  1. ContextSense: unobtrusive discovery of incremental social context using dynamic bluetooth data

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      cover image ACM Conferences
      UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication
      September 2014
      1409 pages
      ISBN:9781450330473
      DOI:10.1145/2638728
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Published: 13 September 2014

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      Author Tags

      1. bluetooth
      2. class-incremental learning
      3. extreme learning machine
      4. fuzzy clustering
      5. incremental social context

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      UbiComp '14
      UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing
      September 13 - 17, 2014
      Washington, Seattle

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      Overall Acceptance Rate 764 of 2,912 submissions, 26%

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      • (2024)Understanding User Behavior in the Wild Using SmartphonesHandbook of Human Computer Interaction10.1007/978-3-319-27648-9_109-1(1-26)Online publication date: 31-Dec-2024
      • (2023)Understanding behaviours in context using mobile sensingNature Reviews Psychology10.1038/s44159-023-00235-32:12(767-779)Online publication date: 23-Oct-2023
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