Artificial Intelligence (AI) based data analysis methods in biomechanical movement assessments of musculoskeletal conditions discriminating between groups: A Scoping Review Protocol

Abstract

eview question / Objective Objective: The primary objective of this scoping review is to identify Artificial Intelligence (AI) based analysis methods for biomechanical time series data that can effectively distinguishing between groups of conditions.The secondary objective is to identify relevant parameters extracted from data captured through biomechanical measurement methods, such as Motion Capture or EMG.The re v i e w f o c u s e s o n s t u d i e s t h a t p ro v i d e performance measures of the methods applied and allow the distinction between groups.Research question: The primary research question guiding this study, based on the PCC framework, is: "What AI analysis methods are used to discriminate groups in studies assessing human movement in individuals with musculoskeletal (MSK) conditions, using biomechanical measuring methods, such as Motion Capture or EMG?" with the following definitions: Population: Humans, any age, with MSK conditions Concept: AI analysis methods capable of discriminating between groups Context: Studies assessing human movement using biomechanical measuring methods such as Motion capture or EMG To address this question, several sub-questions are posed: 1. "Which AI-based analysis methods show proven capabilities to distinguish or prepare the distinction between different groups in humans with musculoskeletal conditions?" 2. "What biomechanical parameters are relevant when assessing differences in movements between groups in humans with musculoskeletal conditions?" Background This Scoping Review is part of a l a rg e r p ro j e c t , f u n d e d b y t h e D e u t s c h e Forschungsgesellschaft (DFG) investigating the development and aetiology of movement-related musculoskeletal (MSK) conditions in performing INPLASY

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