Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb

2008 ◽  
Vol 55 (8) ◽  
pp. 1956-1965 ◽  
Author(s):  
M.A. Oskoei ◽  
Huosheng Hu
Keyword(s):  
Support Vector Machine ◽  
Upper Limb ◽  
Classification Scheme ◽  
Myoelectric Control ◽  
Support Vector

scholarly journals Distinction of Students and Expert Therapists Based on Therapeutic Motions on a Robotic Device Using Support Vector Machine

2020 ◽  
Vol 40 (6) ◽  
pp. 790-797
Author(s):  
Koike Yuji ◽  
Suzuki Makoto ◽  
Okino Akihisa ◽  
Takeda Kazuhisa ◽  
Takanami Yasuhiro ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Exercise Therapy ◽  
Upper Limb ◽  
Peak Velocity ◽  
Movement Time ◽  
Elbow Flexion ◽  
Flexion Angle ◽  
Support Vector ◽  
Robot Arm ◽  
Clinical Practices

Abstract Purpose To clarify the feature values of exercise therapy that can differentiate students and expert therapists and use this information as a reference for exercise therapy education. Methods The participants were therapists with 5 or more years of clinical experience and 4th year students at occupational therapist training schools who had completed their clinical practices. The exercise therapy task included Samothrace (code name, SAMO) exercises implemented on the elbow joint based on the elbow flexion angle, angular velocity, and exercise interval recordings. For analyses and student/therapist comparisons, the peak flexion angle, peak velocity, and movement time were calculated using data on elbow angle changes acquired via SAMO. Subsequently, bootstrap data were generated to differentiate between the exercise therapy techniques adopted by therapists and students, and a support vector machine was used to generate four types of data combinations with the peak flexion angle, peak velocity, and movement time values. These data were used to estimate and compare the respective accuracies with the Friedman test. Results The peak flexion angles were significantly smaller in the case of students. Furthermore, the peak velocities were larger, the peak flexion angles were smaller, and the movement times were shorter compared with those of therapists. The combination of peak velocity and peak flexion angle yielded the highest diagnostic accuracies. Conclusion When students and therapists performed upper limb exercise therapy techniques based on the kinematics movement of a robot arm, the movement speeds and joint angles differed. The combination of peak velocity and peak flexion angle was the most effective classifier used for the differentiation of the abilities of students and therapists. The peak velocity and peak flexion angle of the therapist group can be used as a reference for students when they learn upper limb therapeutic exercise techniques.

scholarly journals Feature subset evaluation method for upper limb rehabilitation training based on joint feature discernibility

2019 ◽  
Vol 15 (3) ◽  
pp. 155014771983846
Author(s):  
Guoyu Zuo ◽  
Zhaokun Xu ◽  
Jiahao Lu ◽  
Daoxiong Gong
Keyword(s):  
Support Vector Machine ◽  
Upper Limb ◽  
Evaluation Method ◽  
Support Vector ◽  
Feature Subset ◽  
Fisher Score ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Limb Rehabilitation ◽  
Candidate Feature

A feature subset discernibility hybrid evaluation method using Fisher score based on joint feature and support vector machine is proposed for the feature selection problem of the upper limb rehabilitation training motion of Brunnstrom 4–5 stage patients. In this method, the joint feature is introduced to evaluate the discernibility between classes due to the joint effect of both candidate and selected features. A feature subset search strategy is used to search a set of candidate feature subsets. The Fisher score based on joint feature method is used to evaluate the candidate feature subsets and the best subset is selected as a new selected feature subset. From these selected subsets such as obtained by the above process, the subset with the best performance of support vector machine classification is finally selected as the optimal feature subset. Experiments were carried out on the upper limb routine rehabilitation training samples of the Brunnstrom 4–5 stage. Compared with both the F-score and the discernibility of feature subset methods, the experimental results show the effectiveness and feasibility of the proposed method which can obtain the feature subsets with higher accuracy and smaller feature dimension.

scholarly journals Rough Support Vector Machine for Classification with Interval and Incomplete Data

2020 ◽  
Vol 10 (1) ◽  
pp. 47-56 ◽  
Author(s):  
Robert K. Nowicki ◽  
Konrad Grzanek ◽  
Yoichi Hayashi
Keyword(s):  
Support Vector Machine ◽  
Rough Sets ◽  
Incomplete Data ◽  
Input Data ◽  
Missing Values ◽  
Classification Scheme ◽  
Equivalence Classes ◽  
Support Vector ◽  
Positive Boundary

AbstractThe paper presents the idea of connecting the concepts of the Vapnik’s support vector machine with Pawlak’s rough sets in one classification scheme. The hybrid system will be applied to classifying data in the form of intervals and with missing values [1]. Both situations will be treated as a cause of dividing input space into equivalence classes. Then, the SVM procedure will lead to a classification of input data into rough sets of the desired classes, i.e. to their positive, boundary or negative regions. Such a form of answer is also called a three–way decision. The proposed solution will be tested using several popular benchmarks.

scholarly journals Evaluation of an Information Flow Gain Algorithm for Microsensor Information Flow in Limber Motor Rehabilitation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Naiqiao Ning ◽  
Yong Tang
Keyword(s):  
Support Vector Machine ◽  
Information Flow ◽  
Upper Limb ◽  
Surface Emg ◽  
Support Vector ◽  
Denoising Method ◽  
Myoelectric Signals ◽  
Rehabilitation Training ◽  
Reduction Effect ◽  
The Time Domain

This paper conducts an evaluative study on the rehabilitation of limb motor function by using a microsensor information flow gain algorithm and investigates the surface electromyography (EMG) signals of the upper limb during rehabilitation training. The surface EMG signals contain a large amount of limb movement information. By analysing and processing the surface EMG signals, we can grasp the human muscle movement state and identify the human upper limb movement intention. The EMG signals were processed by the trap and filter combination denoising method and wavelet denoising method, respectively, the signal-to-noise ratio was used to evaluate the noise reduction effect, and finally, the wavelet denoising method with a better noise reduction effect was selected to process all the EMG signals. After the noise is removed, the signal is extracted in the time domain and frequency domain, and the root mean square (RMS), absolute mean, median frequency in the time domain, and average power frequency in the frequency domain are selected and input to the classifier for pattern recognition. The support vector machine is used to classify the myoelectric signals and optimize the parameters in the support vector machine using the grid search method and particle swarm optimization algorithm and classify the test samples using the trained support vector machine. Compared with the classification results of the grid search optimized support vector machine, the optimized vector machine has a 7% higher recognition rate, reaching 85%. The action recognition classification method of myoelectric signals is combined with an upper limb rehabilitation training platform to verify the feasibility of using myoelectric signals for rehabilitation training. After the classifier recognizes the upper limb movements, the upper computer sends movement commands to the controller to make the rehabilitation platform move according to the recognition results, and finally, the movement execution accuracy of the rehabilitation platform reaches 80% on average.

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