The increasing prevalence of the aging population, and inadequate and uneven distribution of medical resources, have led to a growing demand for telemedicine services. Gait disturbance is a primary symptom of neurological disorders such as Parkinson's disease (PD). This study proposed a novel approach for the quantitative assessment and analysis of gait disturbance from two-dimensional (2D) videos captured using smartphones. The approach used a convolutional pose machine to extract human body joints and a gait phase segmentation algorithm based on node motion characteristics to identify the gait phase. Moreover, it extracted features of the upper and lower limbs. A height ratio-based spatial feature extraction method was proposed that effectively captures spatial information. The proposed method underwent validation via error analysis, correction compensation, and accuracy verification using the motion capture system. Specifically, the proposed method achieved an extracted step length error of less than 3 cm. The proposed method underwent clinical validation, recruiting 64 patients with Parkinson's disease and 46 healthy controls of the same age group. Various gait indicators were statistically analyzed using three classic classification methods, with the random forest method achieving a classification accuracy of 91%. This method provides an objective, convenient, and intelligent solution for telemedicine focused on movement disorders in neurological diseases.
面对人口老龄化加剧、医疗资源不足与分布不均衡的挑战，远程诊疗越来越重要。运动障碍特别是步态障碍，是帕金森病（PD）等神经系统疾病的主要症状。本文提出了一种基于二维（2D）视频的步态障碍量化评估与分析方法，以智能手机为视频采集设备。采用卷积姿态机提取人体关节点，设计基于节点运动特征的步相划分算法，提取上肢和下肢相关特征。并设计了基于身高比例的空间特征提取方法，可有效提取空间特征。使用运动捕捉系统对所提方法进行误差分析、校正补偿以及精度验证，校正后提取的步长误差小于3 cm。开展临床验证，招募64名帕金森病患者和46名同年龄组健康受试者，对各项步态指标进行统计学分析，并采用三种经典分类方法进行实验，其中使用随机森林方法可以获得91%的分类准确率。本文所提方法为神经系统疾病导致运动障碍的非接触和远程诊疗提供了一种客观、方便、智能化的解决方案。.
Keywords: Gait features; Machine learning; Motion disorders; Telemedicine; Two-dimensional video.