Step Length Estimation Using Handheld Inertial Sensors
type: Dataset
description: In this paper a novel step length model using a handheld Micro Electrical Mechanical System (MEMS) is presented. It combines the user’s step frequency and height with a set of three parameters for estimating step length. The model has been developed and trained using 12 different subjects: six men and six women. For reliable estimation of the step frequency with a handheld device, the frequency content of the handheld sensor’s signal is extracted by applying the Short Time Fourier Transform (STFT) independently from the step detection process. The relationship between step and hand frequencies is analyzed for different hand’s motions and sensor carrying modes. For this purpose, the frequency content of synchronized signals collected with two sensors placed in the hand and on the foot of a pedestrian has been extracted. Performance of the proposed step length model is assessed with several field tests involving 10 test subjects different from the above 12. The percentages of error over the travelled distance using universal parameters and a set of parameters calibrated for each subject are compared. The fitted solutions show an error between 2.5 and 5% of the travelled distance, which is comparable with that achieved by models proposed in the literature for body fixed sensors only.
issued: 2026-05-09T04:53:07.443421
language: en
modified: 2026-05-09T04:53:07.443425
contact point: Organization
  fn: CCSD
keyword: biomecanique
keyword: infoeu-reposemanticsarticle
keyword: infoinfo-tscomputer-science-cssignal-and-image-processing
keyword: journal-articles
keyword: localisation
keyword: signal
keyword: spisignalengineering-sciences-physicssignal-and-image-processing
landing page: 1424-8220

Step Length Estimation Using Handheld Inertial Sensors
type: Distribution
format: HTML
issued: 2026-05-09T04:53:07.452949
modified: 2026-05-09T04:53:07.435271
access URL: https://hal.science/hal-00878552

test_moissonnage_selune
type: Agent