StepNet - Deep Learning Approaches for Step Length Estimation

Itzik Klein, Omri Asraf

Research output: Contribution to journalArticlepeer-review


The case of a user walking with a smartphone in an indoor environment is considered. Instead of using traditional pedestrian dead reckoning approaches to estimate the user step-length, we define a deep learning based framework with an activity recognition model to regress the user change in distance and step-length. We propose StepNet - a family of deep-learning based approaches to regress the step-length or change in distance. In addition, we propose regressing a time-varying gain instead of a constant one used for traditional step-length estimation. A comparison is made between the proposed approaches and different network architectures. Experimental results show that the proposed deep-learning approaches outperform traditional ones for the examined trajectories.

Original languageEnglish
Article number9090160
Pages (from-to)85706-85713
Number of pages8
JournalIEEE Access
StatePublished - 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Deep Learning
  • indoor navigation
  • pedestrian dead reckoning

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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