# Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm

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## Abstract

**:**

## 1. Introduction

## 2. Principles and Methods

#### 2.1. sEMG Feature Extraction

#### 2.2. Principal Component Analysis

#### 2.3. Regularized Extreme Learning Machine

## 3. Experimental Data Acquisition and Processing

#### 3.1. Experiment Process

#### 3.2. sEMG Signal and Joint Angle Signal Acquisition

#### 3.2.1. sEMG Signal Acquisition

#### 3.2.2. Joint Angle Signal Acquisition

#### 3.3. EMG Signal Feature Dimension Reduction

#### 3.4. Regularization Overrun Learning Machine Model Training

## 4. Results and Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 6.**The proportion of mean absolute value (MAV), after principal component analysis (PCA) reducing dimension.

**Figure 8.**Key performance indicators (KPIs). (

**a**) Root mean square error (RMSE); (

**b**) correlation coefficient.

No. | Position | No. | Position |
---|---|---|---|

1 | medial femoral muscle | 4 | biceps femoris |

2 | rectus femoris muscle | 5 | semitendinosus |

3 | lateral femoral muscle | 6 | gastrocnemius muscle |

Group | Root Mean Square Error (RMSE) | ρ | Train Time (s) | ||||||
---|---|---|---|---|---|---|---|---|---|

Back Propagation (BP) | Support Vector Machine (SVM) | RELM | BP | SVM | RELM | BP | SVM | RELM | |

G1 | 8.285 | 7.451 | 7.224 | 0.964 | 0.971 | 0.974 | 2.478 | 0.071 | 0.021 |

G2 | 8.901 | 8.053 | 8.160 | 0.949 | 0.967 | 0.961 | 2.884 | 0.064 | 0.031 |

G3 | 8.263 | 7.315 | 7.188 | 0.965 | 0.978 | 0.976 | 2.232 | 0.081 | 0.012 |

G4 | 7.046 | 6.561 | 6.785 | 0.967 | 0.981 | 0.974 | 2.431 | 0.062 | 0.017 |

G5 | 9.847 | 9.769 | 9.497 | 0.921 | 0.929 | 0.937 | 2.784 | 0.061 | 0.018 |

G6 | 12.480 | 11.908 | 11.841 | 0.886 | 0.907 | 0.910 | 2.421 | 0.072 | 0.021 |

G7 | 11.93 | 9.741 | 9.990 | 0.900 | 0.941 | 0.933 | 2.714 | 0.061 | 0.034 |

G8 | 8.174 | 7.283 | 7.141 | 0.951 | 0.968 | 0.966 | 2.413 | 0.064 | 0.012 |

mean | 9.366 | 8.510 | 8.478 | 0.938 | 0.955 | 0.954 | 2.545 | 0.067 | 0.021 |

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**MDPI and ACS Style**

Deng, Y.; Gao, F.; Chen, H.
Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm. *Symmetry* **2020**, *12*, 130.
https://doi.org/10.3390/sym12010130

**AMA Style**

Deng Y, Gao F, Chen H.
Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm. *Symmetry*. 2020; 12(1):130.
https://doi.org/10.3390/sym12010130

**Chicago/Turabian Style**

Deng, Yanxia, Farong Gao, and Huihui Chen.
2020. "Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm" *Symmetry* 12, no. 1: 130.
https://doi.org/10.3390/sym12010130