Research on Identification Modeling for Rehabilitation Training Control System for Stroke Patients' Lower Limbs Based on Support Vector Machine

2014 ◽  
Author(s):  
Liye Ren ◽  
Lifeng Ren ◽  
Yajuan Song ◽  
Ping Feng
Keyword(s):  
Support Vector Machine ◽  
Control System ◽  
Lower Limbs ◽  
Support Vector ◽  
Stroke Patients ◽  
Rehabilitation Training ◽  
Training Control

Design and Implementation of a Rehabilitation Training Control System for Stroke Patients

2014 ◽  
Vol 670-671 ◽  
pp. 1406-1409
Author(s):  
Fu Cheng Cao ◽  
Xian Wei Wang
Keyword(s):  
Control System ◽  
Lower Cost ◽  
Control Strategies ◽  
Gait Rehabilitation ◽  
Stroke Patients ◽  
Rehabilitation Training ◽  
Lower Limb Exoskeleton ◽  
Control Scheme ◽  
Advanced Control ◽  
Training Control

This paper involved designing and implementing a control system of lower limb exoskeleton. In order to combine the unity of the hardware with the flexibility of the software, a full digital control scheme integrated based on DSP processor was presented. By embedding advanced control strategies, the system can effectively control the exoskeleton joint motor. The proposed system can achieve accurate active or passive gait rehabilitation training along the trajectory planed and retrain their muscles and feeling of walking in their legs for paralysis patients at a lower cost.

scholarly journals Support Vector Machine-Based Classifier for the Assessment of Finger Movement of Stroke Patients Undergoing Rehabilitation

2019 ◽  
Vol 40 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Toyohiro Hamaguchi ◽  
Takeshi Saito ◽  
Makoto Suzuki ◽  
Toshiyuki Ishioka ◽  
Yamato Tomisawa ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Peak Velocity ◽  
Hand Movement ◽  
Classification Model ◽  
Support Vector ◽  
Leap Motion ◽  
Stroke Patients ◽  
Related Data ◽  
Testing Dataset ◽  
Linear Svm

Abstract Purpose Traditionally, clinical evaluation of motor paralysis following stroke has been of value to physicians and therapists because it allows for immediate pathophysiological assessment without the need for specialized tools. However, current clinical methods do not provide objective quantification of movement; therefore, they are of limited use to physicians and therapists when assessing responses to rehabilitation. The present study aimed to create a support vector machine (SVM)-based classifier to analyze and validate finger kinematics using the leap motion controller. Results were compared with those of 24 stroke patients assessed by therapists. Methods A non-linear SVM was used to classify data according to the Brunnstrom recovery stages of finger movements by focusing on peak angle and peak velocity patterns during finger flexion and extension. One thousand bootstrap data values were generated by randomly drawing a series of sample data from the actual normalized kinematics-related data. Bootstrap data values were randomly classified into training (940) and testing (60) datasets. After establishing an SVM classification model by training with the normalized kinematics-related parameters of peak angle and peak velocity, the testing dataset was assigned to predict classification of paralytic movements. Results High separation accuracy was obtained (mean 0.863; 95% confidence interval 0.857–0.869; p = 0.006). Conclusion This study highlights the ability of artificial intelligence to assist physicians and therapists evaluating hand movement recovery of stroke patients.

Fault Diagnosis for Electrical Control System of Automobile Based on Support Vector Machine

2014 ◽  
Vol 666 ◽  
pp. 203-207
Author(s):  
Jian Hua Cao
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Electrical Control ◽  
Diagnosis Method ◽  
The Common

This paper is to present a fault diagnosis method for electrical control system of automobile based on support vector machine. We collect the common fault states of electrical control system of automobile to analyze the fault diagnosis ability of electrical control system of automobile based on support vector machine. It can be seen that the accuracy of fault diagnosis for electrical control system of automobile by support vector machine is 92.31%; and the accuracy of fault diagnosis for electrical control system of automobile by BP neural network is 80.77%. The experimental results show that the accuracy of fault diagnosis for electrical control system of automobile of support vector machine is higher than that of BP neural network.

scholarly journals sEMG based Classification of Hand Gestures using EMD Algorithm and its Application on Control System

2019 ◽  
Vol 8 (11S) ◽  
pp. 1103-1105
Keyword(s):  
Support Vector Machine ◽  
Control System ◽  
Empirical Mode Decomposition ◽  
Time Domain ◽  
Support Vector ◽  
Hand Gestures ◽  
Mode Decomposition ◽  
Single Sensor ◽  
Mean Variance

Single sensor is employed for classifying four hand gestures from flexor carpum ulnaris. The first three IMFs that are obtained as a result of Empirical Mode Decomposition are taken into consideration. Time domain features like mean, variance, skewness, etc are taken for each IMFs. Support Vector Machine was used for classification task and the extracted model is used for making predictions

scholarly journals Design and Analysis of a Lower Limb Rehabilitation Training Component for Bedridden Stroke Patients

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 224
Author(s):  
Xusheng Wang ◽  
Yongfei Feng ◽  
Jiazhong Zhang ◽  
Yungui Li ◽  
Jianye Niu ◽  
...  
Keyword(s):  
Lower Limb ◽  
Interventional Therapy ◽  
The Body ◽  
Lower Limbs ◽  
Linkage Mechanism ◽  
Training Module ◽  
Stroke Patients ◽  
Rehabilitation Training ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

Carrying out the immediate rehabilitation interventional therapy will better improve the curative effect of rehabilitation therapy, after the condition of bedridden stroke patients becomes stable. A new lower limb rehabilitation training module, as a component of a synchronous rehabilitation robot for bedridden stroke patients’ upper and lower limbs, is proposed. It can electrically adjust the body shape of patients with a different weight and height. Firstly, the innovative mechanism design of the lower limb rehabilitation training module is studied. Then, the mechanism of the lower limb rehabilitation module is simplified and the geometric relationship of the human–machine linkage mechanism is deduced. Next, the trajectory planning and dynamic modeling of the human–machine linkage mechanism are carried out. Based on the analysis of the static moment safety protection of the human–machine linkage model, the motor driving force required in the rehabilitation process is calculated to achieve the purpose of rationalizing the rehabilitation movement of the patient’s lower limb. To reconstruct the patient’s motor functions, an active training control strategy based on the sandy soil model is proposed. Finally, the experimental platform of the proposed robot is constructed, and the preliminary physical experiment proves the feasibility of the lower limb rehabilitation component.

scholarly journals Fault Diagnosis Method for Engine Control System Based on Probabilistic Neural Network and Support Vector Machine

2019 ◽  
Vol 9 (19) ◽  
pp. 4122 ◽  
Author(s):  
Bo Wang ◽  
Hongwei Ke ◽  
Xiaodong Ma ◽  
Bing Yu
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Probabilistic Neural Network ◽  
Support Vector ◽  
Engine Control ◽  
Piston Engine ◽  
Engine Control System ◽  
Diagnosis Method

Due to the poor working conditions of an engine, its control system is prone to failure. If these faults cannot be treated in time, it will cause great loss of life and property. In order to improve the safety and reliability of an aero-engine, fault diagnosis, and optimization method of engine control system based on probabilistic neural network (PNN) and support vector machine (SVM) is proposed. Firstly, using the German 3 W piston engine as a control object, the fault diagnosis scheme is designed and introduced briefly. Then, the fault injection is performed to produce faults, and the data sample for engine fault diagnosis is established. Finally, the important parameters of PNN and SVM are optimized by particle swarm optimization (PSO), and the results are analyzed and compared. It shows that the engine fault diagnosis method based on PNN and SVM can effectively diagnose the common faults. Under the optimization of PSO, the accuracy of PNN and SVM results are significantly improved, the classification accuracy of PNN is up to 96.4%, and the accuracy of SVM is up to 98.8%, which improves the application of them in fault diagnosis technology of aero-piston engine control system.

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