Research progress in motor assessment of neurodegenerative diseases driven by motion capture data.
10.7507/1001-5515.202403004
- Author:
Junlang WU
1
;
Wei GUO
2
;
Kexin LUO
1
;
Ling HE
1
;
Guanci YANG
1
Author Information
1. Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, P.R. China.
2. Guizhou Provincial Staff and Workers Hospital, Guiyang 550025, P. R. China.
- Publication Type:English Abstract
- Keywords:
Kinematic features;
Motion capture;
Motor assessment;
Neurodegenerative diseases
- MeSH:
Humans;
Neurodegenerative Diseases/diagnosis*;
Machine Learning;
Biomechanical Phenomena;
Deep Learning;
Motion;
Motion Capture
- From:
Journal of Biomedical Engineering
2025;42(2):396-403
- CountryChina
- Language:Chinese
-
Abstract:
Neurodegenerative diseases (NDDs) are a group of heterogeneous neurological disorders that can cause progressive loss of neurons in the central nervous system or peripheral nervous system, resulting in a decline in motor function. Motion capture, as a high-precision and high-resolution technology for capturing human motion data, drives NDDs motor assessment to effectively extract kinematic features and thus assess the patient's motor ability or disease severity. This paper focuses on the recent research progress in motor assessment of NDDs driven by motion capture data. Based on a brief introduction of NDDs motor assessment datasets, we categorized the assessment methods into three types according to the way of feature extraction and processing: NDDs motor assessment methods based on statistical analysis, machine learning and deep learning. Then, we comparatively analyzed the technical points and characteristics of the three types of methods from the aspects of data composition, data preprocessing, assessment methods, assessment purposes and effects. Finally, we discussed and prospected the development trends of NDDs motor assessment.
