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

Lookup NU author(s): Dr Kirsty ScottORCiD, Dr Lisa AlcockORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2021 by the authors. Licensee MDPI, Basel, Switzerland.Optoelectronic stereophotogrammetric (SP) systems are widely used in human movement research for clinical diagnostics, interventional applications, and as a reference system for validating alternative technologies. Regardless of the application, SP systems exhibit different random and systematic errors depending on camera specifications, system setup and laboratory environment, which hinders comparing SP data between sessions and across different systems. While many methods have been proposed to quantify and report the errors of SP systems, they are rarely utilized due to their complexity and need for additional equipment. In response, an easy-to-use quality control (QC) check has been designed that can be completed immediately prior to a data collection. This QC check requires minimal training for the operator and no additional equipment. In addition, a custom graphical user interface ensures automatic processing of the errors in an easy-to-read format for immediate interpretation. On initial deployment in a multicentric study, the check (i) proved to be feasible to perform in a short timeframe with minimal burden to the operator, and (ii) quantified the level of random and systematic errors between sessions and systems, ensuring comparability of data in a variety of protocol setups, including repeated measures, longitudinal studies and multicentric studies.


Publication metadata

Author(s): Scott K, Bonci T, Alcock L, Buckley E, Hansen C, Gazit E, Schwickert L, Cereatti A, Mazza C

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2021

Volume: 21

Issue: 24

Online publication date: 09/12/2021

Acceptance date: 07/12/2021

Date deposited: 13/01/2022

ISSN (electronic): 1424-8220

Publisher: MDPI

URL: https://doi.org/10.3390/s21248223

DOI: 10.3390/s21248223


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