Personalization without user interruption: Boosting activity recognition in new subjects using unlabeled data

Ramin Fallahzadeh, Hassan Ghasemzadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations

Abstract

Activity recognition systems are widely used in monitoring physical and physiological conditions as well as observing the short/long term behavioral patterns for the purpose of improving the health and wellbeing of the users. The major obstacle in widespread use of these systems is the need for collecting labeled data to train the activity recognition model. While a personalized model outperforms a user-independent model, collecting labels from every single user is burdensome and in some cases impractical. In this paper, we propose an uninformed cross-subject transfer learning algorithm that leverages the cross-user similarities by constructing a networkbased feature-level representation of the data in source and target users and perform a best effort community detection to extract the core observations in target data. Our algorithm uses a heuristic classifier-based mapping to assign activity labels to the core observations. Finally, the output of labeling is conditionally fused with the prediction of the source-model to develop a boosted and personalized activity recognition algorithm. Our analysis on real-world data demonstrates the superiority of our approach. Our algorithm achieves over 87% accuracy on average which is 7% higher than the state-of-the art approach.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week)
PublisherAssociation for Computing Machinery, Inc
Pages293-302
Number of pages10
ISBN (Electronic)9781450349659
DOIs
StatePublished - Apr 18 2017
Externally publishedYes
Event8th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2017 - Pittsburgh, United States
Duration: Apr 18 2017Apr 20 2017

Publication series

NameProceedings - 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems, ICCPS 2017 (part of CPS Week)

Conference

Conference8th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2017
Country/TerritoryUnited States
CityPittsburgh
Period4/18/174/20/17

Keywords

  • Activity recognition
  • Cross-subject boosting
  • Uninformed transfer learning

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications

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