Feature Selection of Input Variables for Intelligence Joint Moment Prediction Based on Binary Particle Swarm Optimization
Joint moment is an important parameter for a quantitative assessment of human motor function. However, most existing joint moment prediction methods lacking feature selection of optimal inputs subset, which reduced the prediction accuracy and output comprehensibility, increased the complexity of the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8930916/ |
| _version_ | 1819276590615363584 |
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| author | Baoping Xiong Yurong Li Meilan Huang Wuxiang Shi Min Du Yuan Yang |
| author_facet | Baoping Xiong Yurong Li Meilan Huang Wuxiang Shi Min Du Yuan Yang |
| author_sort | Baoping Xiong |
| collection | DOAJ |
| description | Joint moment is an important parameter for a quantitative assessment of human motor function. However, most existing joint moment prediction methods lacking feature selection of optimal inputs subset, which reduced the prediction accuracy and output comprehensibility, increased the complexity of the input sensor structure, making the portable prediction equipment impossible to achieve. To address this problem, this paper develops a novel method based on the binary particle swarm optimization (BPSO) with the variance accounted for (VAF) as fitness function to reduce the number of input variables while improves the accuracy in joint moment prediction. The proposed method is tested on the experimental data collected from ten healthy subjects who are running on a treadmill with four different speeds of 2, 3, 4 and 5m/s. The BPSO is used to select optimal inputs subset from ten electromyography (EMG) data and six joints angles, and then the selected optimal inputs subset be used to train and predict the joint moments via artificial neural network (ANN). Prediction accuracy is evaluated by the variance accounted for (VAF) test between the predicted joint moment and multi-body dynamics moment. Results show that the proposed method can reduce the number of input variables of five joint moment from 16 to less than 11. Furthermore, the proposed method can better predict joint moment (mean VAF: 94.40±0.84%) in comparison with the state-of-the-art methods, i.e. Elastic Net (mean VAF: 93.38±0.96%) and mutual information (mean VAF: 86.27±1.41%). In conclusion, the proposed method reduces the number of input variables and improves the prediction accuracy that may allow the future development of a portable, non-invasive system for joint moment prediction. As such, it may facilitate real-time assessment of human motor function. |
| first_indexed | 2024-12-23T23:42:38Z |
| format | Article |
| id | doaj.art-1477dd8af3fb416283913c1639c8baa5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:42:38Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1477dd8af3fb416283913c1639c8baa52022-12-21T17:25:36ZengIEEEIEEE Access2169-35362019-01-01718228918229510.1109/ACCESS.2019.29590648930916Feature Selection of Input Variables for Intelligence Joint Moment Prediction Based on Binary Particle Swarm OptimizationBaoping Xiong0https://orcid.org/0000-0003-1004-7884Yurong Li1https://orcid.org/0000-0001-5819-7895Meilan Huang2https://orcid.org/0000-0001-8446-9807Wuxiang Shi3https://orcid.org/0000-0002-6388-7644Min Du4https://orcid.org/0000-0002-1954-3473Yuan Yang5https://orcid.org/0000-0003-2442-3713College of Physics and Information Engineering, Fuzhou University, Fuzhou, ChinaFujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology, Fuzhou University, Fuzhou, ChinaCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, ChinaCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, ChinaCollege of Physics and Information Engineering, Fuzhou University, Fuzhou, ChinaDepartment of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, USAJoint moment is an important parameter for a quantitative assessment of human motor function. However, most existing joint moment prediction methods lacking feature selection of optimal inputs subset, which reduced the prediction accuracy and output comprehensibility, increased the complexity of the input sensor structure, making the portable prediction equipment impossible to achieve. To address this problem, this paper develops a novel method based on the binary particle swarm optimization (BPSO) with the variance accounted for (VAF) as fitness function to reduce the number of input variables while improves the accuracy in joint moment prediction. The proposed method is tested on the experimental data collected from ten healthy subjects who are running on a treadmill with four different speeds of 2, 3, 4 and 5m/s. The BPSO is used to select optimal inputs subset from ten electromyography (EMG) data and six joints angles, and then the selected optimal inputs subset be used to train and predict the joint moments via artificial neural network (ANN). Prediction accuracy is evaluated by the variance accounted for (VAF) test between the predicted joint moment and multi-body dynamics moment. Results show that the proposed method can reduce the number of input variables of five joint moment from 16 to less than 11. Furthermore, the proposed method can better predict joint moment (mean VAF: 94.40±0.84%) in comparison with the state-of-the-art methods, i.e. Elastic Net (mean VAF: 93.38±0.96%) and mutual information (mean VAF: 86.27±1.41%). In conclusion, the proposed method reduces the number of input variables and improves the prediction accuracy that may allow the future development of a portable, non-invasive system for joint moment prediction. As such, it may facilitate real-time assessment of human motor function.https://ieeexplore.ieee.org/document/8930916/Joint moment predictionartificial neural networkbinary particle swarm optimizationfeature selection |
| spellingShingle | Baoping Xiong Yurong Li Meilan Huang Wuxiang Shi Min Du Yuan Yang Feature Selection of Input Variables for Intelligence Joint Moment Prediction Based on Binary Particle Swarm Optimization IEEE Access Joint moment prediction artificial neural network binary particle swarm optimization feature selection |
| title | Feature Selection of Input Variables for Intelligence Joint Moment Prediction Based on Binary Particle Swarm Optimization |
| title_full | Feature Selection of Input Variables for Intelligence Joint Moment Prediction Based on Binary Particle Swarm Optimization |
| title_fullStr | Feature Selection of Input Variables for Intelligence Joint Moment Prediction Based on Binary Particle Swarm Optimization |
| title_full_unstemmed | Feature Selection of Input Variables for Intelligence Joint Moment Prediction Based on Binary Particle Swarm Optimization |
| title_short | Feature Selection of Input Variables for Intelligence Joint Moment Prediction Based on Binary Particle Swarm Optimization |
| title_sort | feature selection of input variables for intelligence joint moment prediction based on binary particle swarm optimization |
| topic | Joint moment prediction artificial neural network binary particle swarm optimization feature selection |
| url | https://ieeexplore.ieee.org/document/8930916/ |
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