Li Jiuwei, Pan Bingyu, Jin Tingting, Huang Zhipei, Ye Shiwei, Wu Jiankang, Huang Zhen, Xie Bin, Luo Chun, Wang Cui
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Science, Beijing, China.
Rehabilitation Department, Peking University 1st Hospital, Beijing, China.
Technol Health Care. 2016 Apr 29;24 Suppl 2:S707-15. doi: 10.3233/THC-161199.
Nowadays, stroke is a leading cause of disability in adults. Assessment of motor performance has played an important role in rehabilitation for post stroke patients. Therefore, it is quite important to develop an automatic assessment system of motor function.
The purpose of this study is to assess the performance of the single task upper-limb movements quantitatively among stroke survivors.
Eleven normal subjects and thirty-five subjects with stroke were involved in this study. The subjects, who were wearing the micro-sensor motion capture system, performed shoulder flexion in a sitting position. The system recorded three-dimensional kinematics data of limb movements in quaternions. By extracting the significant features from these data, we built a linear model to acquire the functional assessment score (FAS).
All of the kinematics features have a significant statistical difference (P < 0.05) between patients and healthy people, while the feature values have a high correlation with Fugl-Meyer (FM) scores (r > 0.5, p < 0.05), indicating that these features are able to reflect the level of motion impairment. Furthermore, most samples of the linear model locate in the confidence interval after regression, with the residual approaching a normal distribution. These results show that the FAS is capable of motor function assessment for stroke survivors.
These findings represent an important step towards a system that can be utilized for precise single task motor evaluation after stroke, applicable to clinical research and as a tool for rehabilitation.
如今，中风是成年人残疾的主要原因。运动功能评估在中风后患者的康复中发挥着重要作用。因此，开发一种运动功能自动评估系统非常重要。
本研究的目的是定量评估中风幸存者单任务上肢运动的表现。
11名正常受试者和35名中风受试者参与了本研究。受试者佩戴微传感器运动捕捉系统，在坐姿下进行肩部屈曲。该系统以四元数记录肢体运动的三维运动学数据。通过从这些数据中提取显著特征，我们建立了一个线性模型来获取功能评估分数（FAS）。
患者和健康人之间所有运动学特征均有显著统计学差异（P < 0.05），而特征值与Fugl-Meyer（FM）评分高度相关（r > 0.5，p < 0.05），表明这些特征能够反映运动损伤程度。此外，线性模型的大多数样本在回归后位于置信区间内，残差接近正态分布。这些结果表明，FAS能够对中风幸存者进行运动功能评估。
这些发现代表了朝着一个可用于中风后精确单任务运动评估的系统迈出的重要一步，适用于临床研究并作为康复工具。
