Gait velocity estimation using time-interleaved between consecutive passive IR Sensor Activations

Rajib Rana, Daniel Austin, Peter G. Jacobs, Mohanraj Karunanithi, Jeffrey Kaye

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in older adults. It is often assessed clinically, but the assessments occur infrequently and do not allow optimal detection of key health changes when they occur. In this paper, we show that the time gap between activations of a pair of passive infrared motion sensors in the consecutively visited room-pair carry rich latent information about a person's gait velocity. We name this time gap transition time and modeling the relationship between transition time and gait velocity, and using a support vector regression approach, we show that gait velocity can be estimated with an average error of <2.5 cm/s. Our method is simple and cost effective and has advantages over competing approaches such as: obtaining 20-100 times more gait velocity measurements per day. It also provides a pervasive in-home method for context-aware gait velocity sensing that allows for monitoring of gait trajectories in space and time.

Original languageEnglish (US)
Article number7486123
Pages (from-to)6351-6358
Number of pages8
JournalIEEE Sensors Journal
Issue number16
StatePublished - Aug 2016


  • Gait velocity
  • passive infrared (PIR) motion sensors
  • support vector regression
  • transition time

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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