IRT-SD-SLE: an improved real-time step detection and step length estimation using smartphone accelerometer

Pampa Sadhukhan, Saptadipa Mazumder, Chandreyee Chowdhury, Sara Paiva, Pradip K. Das, Keshav Dahal, Xinheng Wang

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Abstract

Smartphone sensor-based pedestrian dead reckoning (PDR) systems provide a viable solution to the problem of localization in an infrastructure-less area. Step detection (SD) and step length estimation (SLE), being two fundamental operations of the PDR-based localization technique, have drawn many researchers’ attention in the recent time. Most of the existing SD and SLE methods proposed over the years, however, provide either server-or cloud-based solution that consume additional network bandwidth and suffer from increased transmission delay. Moreover, nonavailability of the inertial sensors like gyroscope, magnetometers, etc., at every smartphone makes majority of the existing SLE methods less applicable to such devices. To address the above-said issues, in this article, we focus on devising an improved SLE method that would detect the pedestrian’s steps and subsequently estimate the step length in real-time by processing the accelerometer data at the device itself. Our proposed method transforms the measured acceleration values along the Earth coordinate system (ECS) and also applies sliding window meaning (SWM) to mitigate the negative effects of the smartphone’s orientation and gravitational bias on the accuracy of SD and SLE. The performances of our proposed method are evaluated in terms of accuracy for ten different users by taking the device in two different postures (handheld and trouser pocket) under two different walking modes (normal and fast) to demonstrate its efficacy. Moreover, our proposed method obtains more than 80% average accuracy for SD and also obtains more than 75% accuracy (median) for SLE for all participants under four different scenarios considered here.
Original languageEnglish
Pages (from-to)30858-30868
Number of pages11
JournalIEEE Sensors Journal
Volume23
Issue number24
Early online date9 Nov 2023
DOIs
Publication statusPublished - 15 Dec 2023

Keywords

  • accelerometer
  • accuracy
  • machine learning
  • sensor
  • smartphone
  • step detection (SD)
  • step length

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