%0 Journal Article
%@ 2561-7605
%I JMIR Publications
%V 7
%N 
%P e52582
%T Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review
%A Jeyasingh-Jacob,Julian
%A Crook-Rumsey,Mark
%A Shah,Harshvi
%A Joseph,Theresita
%A Abulikemu,Subati
%A Daniels,Sarah
%A Sharp,David J
%A Haar,Shlomi
%+ Department of Brain Sciences, Imperial College London, Sir Michael Uren Research Hub, London, W12 0BZ, United Kingdom, 44 20 759 48064, s.haar@imperial.ac.uk
%K markerless motion capture
%K motion analysis
%K movement analysis
%K motion
%K neurodegeneration
%K neurodegenerative
%K systematic review
%K movement
%K body tracking
%K tracking
%K monitoring
%K clinical decision making
%K decision
%K decision making
%K dementia
%K neurodegenerative disease
%K mild cognitive impairment
%K Parkinson's disease
%K tool
%K mobility
%D 2024
%7 6.8.2024
%9 Review
%J JMIR Aging
%G English
%X Background: Markerless motion capture (MMC) uses video cameras or depth sensors for full body tracking and presents a promising approach for objectively and unobtrusively monitoring functional performance within community settings, to aid clinical decision-making in neurodegenerative diseases such as dementia. Objective: The primary objective of this systematic review was to investigate the application of MMC using full-body tracking, to quantify functional performance in people with dementia, mild cognitive impairment, and Parkinson disease. Methods: A systematic search of the Embase, MEDLINE, CINAHL, and Scopus databases was conducted between November 2022 and February 2023, which yielded a total of 1595 results. The inclusion criteria were MMC and full-body tracking. A total of 157 studies were included for full-text screening, out of which 26 eligible studies that met the selection criteria were included in the review.  Results: Primarily, the selected studies focused on gait analysis (n=24), while other functional tasks, such as sit to stand (n=5) and stepping in place (n=1), were also explored. However, activities of daily living were not evaluated in any of the included studies. MMC models varied across the studies, encompassing depth cameras (n=18) versus standard video cameras (n=5) or mobile phone cameras (n=2) with postprocessing using deep learning models. However, only 6 studies conducted rigorous comparisons with established gold-standard motion capture models. Conclusions: Despite its potential as an effective tool for analyzing movement and posture in individuals with dementia, mild cognitive impairment, and Parkinson disease, further research is required to establish the clinical usefulness of MMC in quantifying mobility and functional performance in the real world. 
%R 10.2196/52582
%U https://aging.jmir.org/2024/1/e52582
%U https://doi.org/10.2196/52582