Li Dongdong, Kaminishi Kohei, Chiba Ryosuke, Takakusaki Kaoru, Mukaino Masahiko, Ota Jun
Department of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
Research Into Artifacts, Center for Engineering (RACE), School of Engineering, The University of Tokyo, Tokyo, Japan.
Front Hum Neurosci. 2021 Dec 3;15:731677. doi: 10.3389/fnhum.2021.731677. eCollection 2021.
Post-stroke complications are the second most frequent cause of death and the third leading cause of disability worldwide. The motor function of post-stroke patients is often assessed by measuring the postural sway in the patients during quiet standing, based on sway measures, such as sway area and velocity, which are obtained from temporal variations of the center of pressure. However, such approaches to establish a relationship between the sway measures and patients' demographic factors have hardly been successful (e.g., days after onset). This study instead evaluates the postural sway features of post-stroke patients using the clustering method of machine learning. First, we collected the stroke patients' multi-variable motion-capture standing-posture data and processed them into s long data slots. Then, we clustered the -s data slots into cluster groups using the dynamic-time-warping partition-around-medoid (DTW-PAM) method. The DTW measures the similarity between two temporal sequences that may vary in speed, whereas PAM identifies the centroids for the DTW clustering method. Finally, we used a test and found that the sway amplitudes of markers in the shoulder, hip, knee, and center-of-mass are more important than their sway frequencies. We separately plotted the marker amplitudes and frequencies in the medial-lateral direction during a 5-s data slot and found that the post-stroke patients' postural sway frequency lay within the bandwidth of 0.5-1.5 Hz. Additionally, with an increase in the onset days, the cluster index of cerebral hemorrhage patients gradually transits in a four-cluster solution. However, the cerebral infarction patients did not exhibit such pronounced transitions over time. Moreover, we found that the postural-sway amplitude increased in clusters 1, 3, and 4. However, the amplitude of cluster 2 did not follow this pattern, owing to age effects related to the postural sway changes with age. A rehabilitation doctor can utilize these findings as guidelines to direct the post-stroke patient training.
中风后并发症是全球第二大常见死因和第三大致残原因。中风后患者的运动功能通常通过测量患者安静站立时的姿势摇摆来评估，基于从压力中心的时间变化获得的摇摆测量值，如摇摆面积和速度。然而，建立摇摆测量值与患者人口统计学因素（如发病后天数）之间关系的此类方法几乎没有成功过。相反，本研究使用机器学习的聚类方法评估中风后患者的姿势摇摆特征。首先，我们收集了中风患者的多变量运动捕捉站立姿势数据，并将其处理成s个长数据时隙。然后，我们使用动态时间规整围绕中心点划分（DTW-PAM）方法将-s个数据时隙聚类为簇组。DTW测量两个可能速度不同的时间序列之间的相似性，而PAM为DTW聚类方法识别中心点。最后，我们使用t检验，发现肩部、髋部、膝盖和质心处标记物的摇摆幅度比其摇摆频率更重要。我们在一个5秒的数据时隙内分别绘制了标记物在内侧-外侧方向上的幅度和频率，发现中风后患者的姿势摇摆频率在0.5-1.5赫兹的带宽内。此外，随着发病天数的增加，脑出血患者的聚类指数在四簇解决方案中逐渐转变。然而，脑梗死患者并未随时间表现出如此明显的转变。而且，我们发现第1、3和4簇中的姿势摇摆幅度增加。然而，第2簇的幅度没有遵循这种模式，这是由于与姿势摇摆随年龄变化相关的年龄效应。康复医生可以将这些发现用作指导中风后患者训练的指南。
