Classification of Motor Impairments of Post-stroke Patients based on Force Applied to a Handrail

Qi An, Ningjia Yang, Hiroshi Yamakawa, Hiroki Kogami, Kazunori Yoshida, Ruoxi Wang, Atsushi Yamashita, Hajime Asama, Shu Ishiguro, Shingo Shimoda, Hiroshi Yamasaki, Hiroshi Yamasaki, Moeka Yokoyama, Fady Alnajjar, Noriaki Hattori, Kouji Takahashi, Takanori Fujii, Hironori Otomune, Ichiro Miyai, Ryo Kurazume

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
VolumePP
DOIs
Publication statusE-pub ahead of print - Nov 11 2021

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