Versatile clinical movement analysis using statistical parametric mapping in MovementRx

Amr Alhossary (Corresponding Author), Todd Pataky, Wei Tech Ang, Karen Sui Geok Chua, Wai Hang Kwong, Cyril John Donnelly

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Clinical gait analysis is an important biomechanics field that is often influenced by subjectivity in time-varying analysis leading to type I and II errors. Statistical Parametric Mapping can operate on all time-varying joint dynamics simultaneously, thereby overcoming subjectivity errors. We present MovementRx, the first gait analysis modelling application that correctly models the deviations of joints kinematics and kinetics both in 3 and 1 degrees of freedom; presented with easy-to-understand color maps for clinicians with limited statistical training. MovementRx is a python-based versatile GUI-enabled movement analysis decision support system, that provides a holistic view of all lower limb joints fundamental to the kinematic/kinetic chain related to functional gait. The user can cascade the view from single 3D multivariate result down to specific single joint individual 1D scalar movement component in a simple, coherent, objective, and visually intuitive manner. We highlight MovementRx benefit by presenting a case-study of a right knee osteoarthritis (OA) patient with otherwise undetected postintervention contralateral OA predisposition. MovementRx detected elevated frontal plane moments of the patient’s unaffected knee. The patient also revealed a surprising adverse compensation to the contralateral limb.

Original languageEnglish
Article number2414
JournalScientific Reports
Volume13
Issue number1
DOIs
Publication statusPublished - 10 Feb 2023

ASJC Scopus subject areas

  • General

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