IEEE Trans Neural Syst Rehabil Eng. 2018 Apr;26(4):856-864. doi: 10.1109/TNSRE.2018.2811415.
Gait analysis for the patients with lower limb motor dysfunction is a useful tool in assisting clinicians for diagnosis, assessment, and rehabilitation strategy making. Implementing accurate automatic gait analysis for the hemiparetic patients after stroke is a great challenge in clinical practice. This study is to develop a new automatic gait analysis system for qualitatively recognizing and quantitatively assessing the gait abnormality of the post-stroke hemiparetic patients. Twenty-one post-stroke patients and twenty-one healthy volunteers participated in the walking trials. Three of the most representative gait data, i.e., marker trajectory (MT), ground reaction force (GRF), and electromyogram, were simultaneously acquired from these subjects during their walking. A multimodal fusion architecture is established by using these different modal data to qualitatively distinguish the hemiparetic gait from normal gait by different pattern recognition techniques and to quantitatively estimate the patient's lower limb motor function by a novel probability-based gait score. Seven decision fusion algorithms have been tested in this architecture, and extensive data analysis experiments have been conducted. The results indicate that the recognition performance and estimation performance of the system become better when more modal gait data are fused. For the recognition performance, the random forest classifier based on the GRF data achieves an accuracy of 92.26% outperformed other single-modal schemes. When combining two modal data, the accuracy can be enhanced to 95.83% by using the support vector machine (SVM) fusion algorithm to fuse the MT and GRF data. When integrating all the three modal data, the accuracy can be further improved to 98.21% by using the SVM fusion algorithm. For the estimation performance, the absolute values of the correlation coefficients between the estimation results of the above three schemes and the Wisconsin gait scale scores for the post-stroke patients are 0.63, 0.75, and 0.84, respectively, which means the clinical relevance becomes more obvious when using more modalities. These promising results demonstrate that the proposed method has considerable potential to promote the future design of automatic gait analysis systems for clinical practice.
下肢运动功能障碍患者的步态分析是辅助临床医生进行诊断、评估和制定康复策略的有用工具。对脑卒中后偏瘫患者进行准确的自动步态分析是临床实践中的一大挑战。本研究旨在开发一种新的自动步态分析系统，用于定性识别和定量评估脑卒中后偏瘫患者的步态异常。21 例脑卒中后患者和 21 例健康志愿者参与了行走试验。从这些受试者在行走过程中同步获取了三个最具代表性的步态数据，即标记轨迹（MT）、地面反力（GRF）和肌电图。通过不同的模式识别技术，使用这些不同模态的数据建立了一种多模态融合架构，以定性区分偏瘫步态与正常步态，并通过一种新的基于概率的步态评分来定量估计患者的下肢运动功能。在该架构中测试了七种决策融合算法，并进行了广泛的数据分析实验。结果表明，融合更多模态的步态数据可以提高系统的识别性能和估计性能。对于识别性能，基于 GRF 数据的随机森林分类器的准确率为 92.26%，优于其他单模态方案。当融合两种模态数据时，通过使用支持向量机（SVM）融合算法融合 MT 和 GRF 数据，准确率可提高到 95.83%。当整合所有三种模态数据时，通过使用 SVM 融合算法，准确率可进一步提高到 98.21%。对于估计性能，上述三种方案的估计结果与脑卒中患者的威斯康星步态量表评分之间的相关系数绝对值分别为 0.63、0.75 和 0.84，这意味着使用更多模态时临床相关性变得更加明显。这些有希望的结果表明，所提出的方法具有相当的潜力，可以促进未来自动步态分析系统在临床实践中的设计。
