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.
Highlights
Stroke is a disease caused by brain nerve injury
To facilitate the subsequent establishment of a quantitative evaluation model based on machine learning algorithms and ensure that all values fall in the domain [0, 1], the Min-Max method [25] is used for normalization
Three different quantitative evaluation models based on Back Propagation Neural Network (BPNN), K-Nearest Neighbors (KNN), and Support Vector Regression (SVR) regression algorithms were established to evaluate upper limb motor function
Summary
Stroke is a disease caused by brain nerve injury. It is one of the common diseases among the elderly and the second leading cause of death [1]. It is of great significance to establish a quantitative evaluation system of upper limb motor function of stroke patients, and help doctors adjust the rehabilitation training methods in time according to the evaluation results. Lee et al [12] proposed a virtual reality upper limb movement training system for stroke rehabilitation, which used a machine learning method to evaluate upper limb motor function. Olesh et al [16] developed a low-cost motion capture system to automatically evaluate upper limb injury in stroke patients. Otten et al [20] used low-cost sensors to collect motion data and obtain the patient’s upper limb function score through a machine learning algorithm These scholars proposed a quantitative scoring method for the completion quality of a single action during rehabilitation training. The rehabilitation robot proposed in this study is promising for application in home and community
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