Quantitative Evaluation System of Upper Limb Motor Function of Stroke Patients Based on Desktop Rehabilitation Robot

Abstract

Rehabilitation training and movement evaluation after stroke have become a research hotspot as stroke has become a very common and harmful disease. However, traditional rehabilitation training and evaluation are mainly conducted under the guidance of rehabilitation doctors. The evaluation process is time-consuming and the evaluation results are greatly influenced by doctors. In this study, a desktop upper limb rehabilitation robot was designed and a quantitative evaluation system of upper limb motor function for stroke patients was proposed. The kinematics and dynamics data of stroke patients during active training were collected by sensors. Combined with the scores of patients’ upper limb motor function by rehabilitation doctors using the Wolf Motor Function Test (WMFT) scale, three different quantitative evaluation models of upper limb motor function based on Back Propagation Neural Network (BPNN), K-Nearest Neighbors (KNN), and Support Vector Regression (SVR) algorithms were established. To verify the effectiveness of the quantitative evaluation system, 10 healthy subjects and 21 stroke patients were recruited for experiments. The experimental results show that the BPNN model has the best evaluation performance among the three quantitative evaluation models. The scoring accuracy of the BPNN model reached up to 87.1%. Moreover, there was a significant correlation between the models′ scores and the doctors′ scores. The proposed system can help doctors to quantitatively evaluate the upper limb motor function of stroke patients and accurately master the rehabilitation progress of patients.

Effects of Early Acupuncture Combined with Rehabilitation Training on Limb Function and Nerve Injury Rehabilitation in Elderly Patients with Stroke: Based on a Retrospective Cohort Study

“…Stroke includes ischemic stroke and hemorrhagic stroke and is a kind of disease caused by cerebral vascular obstruction or hemorrhage and interruption of cerebral vascular blood flow, which has the characteristics of high incidence, high mortality, high disability rate, high recurrence rate, and high medical cost [ 20 ]. The China Stroke Prevention and Treatment Report 2019 points out that stroke has become the leading cause of death among Chinese residents due to disease [ 21 ].…”

“…The upper limb functional training of patients was evaluated by the WMFT with a total of 15 items, all of which were scored using a 6-level scoring method [ 20 ]. The higher the score, the better the functional training effect of the patients.…”

Data-Driven Quantitation of Movement Abnormality after Stroke

“…To date, studies have largely focused on training ML models to directly predict clinical assessment scores (e.g., FMA, WMFT) or athletic level from kinematic data. These data are often acquired from the administration of the clinical assessments themselves [ 62 , 63 , 64 , 65 , 66 ], robotic platforms [ 67 ], or movement batteries [ 68 , 69 ], which require expert assessors, extra time, and/or specialized setups. Feature engineering must also be undertaken to support ML-based analysis.…”

Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base

“…JungYeon Kim proposed a method to judge the degree of elbow spasticity and used machine learning algorithm to classify the level of elbow spasticity [22]. Mingliang Zhang proposed a rehabilitation evaluation system for desktop rehabilitation training robot, which can quantitatively assessment the UL motor function of patients and grasp the degree of rehabilitation of patients [23].…”