Classification of Motor Impairments of Post-Stroke Patients Based on Force Applied to a Handrail
Classification of Motor Impairments of Post-Stroke Patients Based on Force Applied to a Handrail
复制标题
根据施加在扶手上的力对中风后患者运动障碍进行分类
DOI：
10.1109/tnsre.2021.3127504
复制
发表时间：
2021
影响因子：
4.9
通讯作者：
Miyai Ic
中科院分区：
文献类型：
--
作者：
An Qi;Yang Ningjia;Yamakawa Hiroshi;Kogami Hiroki;Yoshida Kazunori;Wang Ruoxi;Yamashita Atsushi;Asama Hajime;Ishiguro Shu;Shimoda Shingo;Yamasaki Hiroshi;Yokoyama Moeka;Alnajjar Fady;Hattori Noriaki;Takahashi Kouji;Fujii Takanori;Otomune Hironori;Miyai Ic
许多病人在脑部受伤后运动能力下降。为了提供适当的康复方案，鼓励运动障碍患者进一步参与康复，有必要有充分和简单的评估方法。这项研究的重点是卒中后患者的坐立运动，因为它是一项重要的日常活动。我们之前的研究利用肌肉协同作用（同步肌肉激活）对患者的运动损伤程度进行分类，并提出适当的康复方法。然而，在我们之前的研究中，患者需要将肌电传感器附着在他/她的身体上；因此，很难评估日常环境下的运动能力。在这里，我们开发了一种扶手式传感器，可以测量施加在它上面的力。利用力数据的时间特征，明确运动损伤程度与时间特征之间的关系，并利用随机森林模型建立了判定偏瘫患者运动损伤程度的分类模型。结果表明，重度运动障碍偏瘫患者对扶手的作用力较大，使用时间也较长。研究还发现，与中度运动障碍患者相比，重度运动障碍患者在站立时不能向前移动，而是更多地依靠扶手将上半身向上拉。此外，根据开发的分类模型，成功地将患者分为重度或中度损伤。所开发的分类模型还可以检测患者的长期恢复情况。扶手式传感器不需要在患者身上安装额外的传感器，并且提供了一种简单的评估方法。
Many patients suffer from declined motor abilities after a brain injury. To provide appropriate rehabilitation programs and encourage motor-impaired patients to participate further in rehabilitation, sufficient and easy evaluation methodologies are necessary. This study is focused on the sit-to-stand motion of post-stroke patients because it is an important daily activity. Our previous study utilized muscle synergies (synchronized muscle activation) to classify the degree of motor impairment in patients and proposed appropriate rehabilitation methodologies. However, in our previous study, the patient was required to attach electromyography sensors to his/her body; thus, it was difficult to evaluate motor ability in daily circumstances. Here, we developed a handrail-type sensor that can measure the force applied to it. Using temporal features of the force data, the relationship between the degree of motor impairment and temporal features was clarified, and a classification model was developed using a random forest model to determine the degree of motor impairment in hemiplegic patients. The results show that hemiplegic patients with severe motor impairments tend to apply greater force to the handrail and use the handrail for a longer period. It was also determined that patients with severe motor impairments did not move forward while standing up, but relied more on the handrail to pull their upper body upward as compared to patients with moderate impairments. Furthermore, based on the developed classification model, patients were successfully classified as having severe or moderate impairments. The developed classification model can also detect long-term patient recovery. The handrail-type sensor does not require additional sensors on the patient’s body and provides an easy evaluation methodology.