Detecting short-duration ambulatory episodes in Fitbit data

Research output: Contribution to conferencePaperpeer-review

Abstract

Wearable consumer physical activity tracking devices1 ssuch as Fitbit®) which generate logs of step count data for users offer a simple and relatively inexpensive way to monitor ambulatory behaviour. However, for researchers, the time granularity with which incremental step count data can typically be obtained using these devices, limits their usefulness particularly for short duration events. This prevents wider use of consumer wearables for non-fitness related purposes, such as using step counts to describe specific activities of daily living. This paper describes a method to overcome this limitation by classifying movements based on patterns in the data. We propose a simple model for analysing adjacent step count data values to achieve this using pilot data on physical activity in an office setting.

Original languageEnglish
DOIs
Publication statusPublished - 29 Jan 2018
Event2018 Australasian Computer Science Week Multiconference, ACSW 2018 - Brisbane, Australia
Duration: 30 Jan 20182 Feb 2018

Conference

Conference2018 Australasian Computer Science Week Multiconference, ACSW 2018
CountryAustralia
CityBrisbane
Period30/01/182/02/18

Keywords

  • ACM proceedings
  • Text tagging

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