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.
Alzheimer’s disease (AD) is a common elderly illness, and the hand movement abilities of patients differ from those of normal individuals. Focusing on the utilization of RGB, optical flow, and hand skeleton as tri-modal image information for early AD recognition, a method for early AD recognition via multi-modal hand motion quality assessment (EADR) is proposed. First, a hybrid modality feature encoder incorporating global contextual information was designed to integrate the global contextual information of features from three specific modality branches. Subsequently, a fusion modality feature decoder network incorporating specific modality features was proposed to decode the overlooked information in the fusion modality branch from specific modality features, thereby enhancing feature fusion. Experiments demonstrated that EADR effectively could capture high-quality hand motion features and excelled in hand motion quality assessment tasks, outperforming existing models. Based on this, the action quality scoring regression model trained using the k-nearest neighbors algorithm demonstrated the best recognition performance for AD patients, with Spearman’s rank correlation coefficient and Kendall’s rank correlation coefficient reaching 90.98% and 83.44%, respectively. This indicates that the assessment of hand motor ability may serve as a potential auxiliary tool for early AD identification.
