Literature DB >> 30630626

A wearable solution for accurate step detection based on the direct measurement of the inter-foot distance.

Stefano Bertuletti1, Ugo Della Croce2, Andrea Cereatti3.   

Abstract

Accurate step detection is crucial for the estimation of gait spatio-temporal parameters. Although several step detection methods based on the use of inertial measurement units (IMUs) have been successfully proposed, they may not perform adequately when the foot is dragged while walking, when walking aids are used, or when walking at low speed. The aim of this study was to test an original step-detection method, the inter-foot distance step counter (IFOD), based on the direct measurement of the distance between feet. Gait data were recorded using a wearable prototype system (SWING2DS), which integrates an IMU and two time-of-flight distance sensors (DSs). The system was attached to the medial side of the right foot with one DS positioned close to the forefoot (FOREDS) and the other close to the rearfoot (REARDS). Sixteen healthy adults were asked to walk over ground for two minutes along a loop, including both rectilinear and curvilinear portions, during two experimental sessions. The accuracy of the IFOD step counter was assessed using a stereo-photogrammetric system as gold standard. The best performance was obtained for REARDS with an accuracy higher than 99.8% for the instrumented foot step and 88.8% for the non-instrumented foot step during both rectilinear and curvilinear walks. Key features of the IFOD step counter are that it is possible to detect both right and left steps by instrumenting one foot only and that it does not rely on foot impact dynamics. The IFOD step counter can be combined with existing IMU-based methods for increasing step-detection accuracy.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Distance sensor; Gait analysis; Mobility assessment; Proximity sensor; Stride; Walking cycle

Year:  2018        PMID: 30630626     DOI: 10.1016/j.jbiomech.2018.12.039

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  3 in total

1.  A Personalized Approach to Improve Walking Detection in Real-Life Settings: Application to Children with Cerebral Palsy.

Authors:  Lena Carcreff; Anisoara Paraschiv-Ionescu; Corinna N Gerber; Christopher J Newman; Stéphane Armand; Kamiar Aminian
Journal:  Sensors (Basel)       Date:  2019-12-03       Impact factor: 3.576

2.  Technical validation of real-world monitoring of gait: a multicentric observational study.

Authors:  Claudia Mazzà; Lisa Alcock; Kamiar Aminian; Clemens Becker; Stefano Bertuletti; Tecla Bonci; Philip Brown; Marina Brozgol; Ellen Buckley; Anne-Elie Carsin; Marco Caruso; Brian Caulfield; Andrea Cereatti; Lorenzo Chiari; Nikolaos Chynkiamis; Fabio Ciravegna; Silvia Del Din; Björn Eskofier; Jordi Evers; Judith Garcia Aymerich; Eran Gazit; Clint Hansen; Jeffrey M Hausdorff; Jorunn L Helbostad; Hugo Hiden; Emily Hume; Anisoara Paraschiv-Ionescu; Neil Ireson; Alison Keogh; Cameron Kirk; Felix Kluge; Sarah Koch; Arne Küderle; Vitaveska Lanfranchi; Walter Maetzler; M Encarna Micó-Amigo; Arne Mueller; Isabel Neatrour; Martijn Niessen; Luca Palmerini; Lucas Pluimgraaff; Luca Reggi; Francesca Salis; Lars Schwickert; Kirsty Scott; Basil Sharrack; Henrik Sillen; David Singleton; Abolfazi Soltani; Kristin Taraldsen; Martin Ullrich; Linda Van Gelder; Beatrix Vereijken; Ioannis Vogiatzis; Elke Warmerdam; Alison Yarnall; Lynn Rochester
Journal:  BMJ Open       Date:  2021-12-02       Impact factor: 2.692

3.  A Quality Control Check to Ensure Comparability of Stereophotogrammetric Data between Sessions and Systems.

Authors:  Kirsty Scott; Tecla Bonci; Lisa Alcock; Ellen Buckley; Clint Hansen; Eran Gazit; Lars Schwickert; Andrea Cereatti; Claudia Mazzà
Journal:  Sensors (Basel)       Date:  2021-12-09       Impact factor: 3.576

  3 in total

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