Wu Junlang, Guo Wei, Luo Kexin, He Ling, Yang Guanci
Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, P.R. China.
Guizhou Provincial Staff and Workers Hospital, Guiyang 550025, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2025 Apr 25;42(2):396-403. doi: 10.7507/1001-5515.202403004.
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.
神经退行性疾病（NDDs）是一组异质性神经系统疾病，可导致中枢神经系统或周围神经系统中的神经元逐渐丧失，从而导致运动功能下降。动作捕捉作为一种用于捕捉人体运动数据的高精度、高分辨率技术，推动了神经退行性疾病的运动评估，以有效提取运动学特征，从而评估患者的运动能力或疾病严重程度。本文重点关注由动作捕捉数据驱动的神经退行性疾病运动评估的最新研究进展。在简要介绍神经退行性疾病运动评估数据集的基础上，我们根据特征提取和处理方式将评估方法分为三类：基于统计分析、机器学习和深度学习的神经退行性疾病运动评估方法。然后，我们从数据组成、数据预处理、评估方法、评估目的和效果等方面对这三类方法的技术要点和特点进行了比较分析。最后，我们对神经退行性疾病运动评估的发展趋势进行了讨论和展望。
