TY  - JOUR
AU  - Jeyasingh-Jacob, Julian
AU  - Crook-Rumsey, Mark
AU  - Shah, Harshvi
AU  - Joseph, Theresita
AU  - Abulikemu, Subati
AU  - Daniels, Sarah
AU  - Sharp, David J
AU  - Haar, Shlomi
PY  - 2024
DA  - 2024/8/6
TI  - Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review
JO  - JMIR Aging
SP  - e52582
VL  - 7
KW  - markerless motion capture
KW  - motion analysis
KW  - movement analysis
KW  - motion
KW  - neurodegeneration
KW  - neurodegenerative
KW  - systematic review
KW  - movement
KW  - body tracking
KW  - tracking
KW  - monitoring
KW  - clinical decision making
KW  - decision
KW  - decision making
KW  - dementia
KW  - neurodegenerative disease
KW  - mild cognitive impairment
KW  - Parkinson's disease
KW  - tool
KW  - mobility
AB  - 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. 
SN  - 2561-7605
UR  - https://aging.jmir.org/2024/1/e52582
UR  - https://doi.org/10.2196/52582
DO  - 10.2196/52582
ID  - info:doi/10.2196/52582
ER  -