An investigation into smartphone based weakly supervised activity recognition systems

Research output: Contribution to journalArticle

Abstract

With smart-devices becoming increasingly more commonplace, methods of capturing an individual’s activities are becoming feasible. This is more generally performed through questionnaires or within unnatural environments bringing drawbacks in accuracy or requiring impractical conditions. This paper presents a simpler method of data collection which reduces the complications of typical activity data collection by collecting labels directly from a user. Instead of capturing activity beginning and end times, user requests are made at time intervals and labels are populated to feature vectors. These methods can provide a simpler method of data collection and could provide a solution to the annotation problem within activity recognition
Original languageEnglish
Article number56
Pages (from-to)45-56
Number of pages12
JournalPervasive and Mobile Computing
Volume56
Early online date29 Mar 2019
DOIs
Publication statusPublished - 1 May 2019

Keywords

  • Gaussian means
  • Activity recognition
  • Multiple-instance learning
  • Random forest
  • Support vector machines

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