Estimation of Knee Joint Extension Force Using Mechanomyography Based on IGWO-SVR Algorithm

被引:6
|
作者
Li, Zebin [1 ,2 ,3 ]
Gao, Lifu [1 ]
Lu, Wei [1 ,2 ]
Wang, Daqing [1 ]
Xie, Chenlei [1 ]
Cao, Huibin [1 ]
机构
[1] Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China
[2] Univ Sci & Technol China, Dept Sci Isl, Hefei 230026, Peoples R China
[3] West Anhui Univ, Sch Elect & Photoelect Engn, Luan 237012, Peoples R China
基金
中国国家自然科学基金;
关键词
mechanomyography; knee joint extension force; improved gray wolf algorithm; support vector machine; WOLF OPTIMIZATION;
D O I
10.3390/electronics10232972
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Muscle force is an important physiological parameter of the human body. Accurate estimation of the muscle force can improve the stability and flexibility of lower limb joint auxiliary equipment. Nevertheless, the existing force estimation methods can neither satisfy the accuracy requirement nor ensure the validity of estimation results. It is a very challenging task that needs to be solved. Among many optimization algorithms, gray wolf optimization (GWO) is widely used to find the optimal parameters of the regression model because of its superior optimization ability. Due to the traditional GWO being prone to fall into local optimum, a new nonlinear convergence factor and a new position update strategy are employed to balance local and global search capability. In this paper, an improved gray wolf optimization (IGWO) algorithm to optimize the support vector regression (SVR) is developed to estimate knee joint extension force accurately and timely. Firstly, mechanomyography (MMG) of the lower limb is measured by acceleration sensors during leg isometric muscle contractions extension training. Secondly, root mean square (RMS), mean absolute value (MAV), zero crossing (ZC), mean power frequency (MPF), and sample entropy (SE) of the MMG are extracted to construct feature sets as candidate data sets for regression analysis. Lastly, the features are fed into IGWO-SVR for further training. Experiments demonstrate that the IGWO-SVR provides the best performance indexes in the estimation of knee joint extension force in terms of RMSE, MAPE, and R compared with the other state-of-art models. These results are expected to become the most effective as guidance for rehabilitation training, muscle disease diagnosis, and health evaluation.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR
    Li, Zebin
    Gao, Lifu
    Lu, Wei
    Wang, Daqing
    Cao, Huibin
    Zhang, Gang
    [J]. SENSORS, 2022, 22 (12)
  • [2] Estimation of Knee Extension Force Using Mechanomyography Signals Detected Through Clothing
    Wang, Daqing
    Xie, Chenlei
    Wu, Haifeng
    Hu, Dun
    Zhang, Qianqian
    Gao, Lifu
    [J]. INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2019, PT II, 2019, 11741 : 3 - 14
  • [3] Settlement Prediction of High-filled Embankment Based on Collaborative Denoising and IGWO-SVR
    Su, Qian
    Zhang, Qi
    Zhang, Zongyu
    Niu, Yunbin
    Chen, De
    [J]. Tiedao Xuebao/Journal of the China Railway Society, 2024, 46 (03): : 87 - 98
  • [4] MMG-Based Knee Dynamic Extension Force Estimation Using Cross-Talk and IGWO-LSTM
    Li, Zebin
    Gao, Lifu
    Zhang, Gang
    Lu, Wei
    Wang, Daqing
    Zhang, Jinzhong
    Cao, Huibin
    [J]. BIOENGINEERING-BASEL, 2024, 11 (05):
  • [5] A Radar TR Component Electromagnetic Interference Signal Strength Prediction Model Based on IGWO-SVR
    Wang, Jingyang
    Li, Jin
    Ma, Liyun
    Wang, Yuming
    [J]. IEEE ACCESS, 2024, 12 : 83900 - 83910
  • [6] Fouling prediction of heat exchanger surface under alternating magnetic field based on IGWO-SVR
    Wang, Jianguo
    Xu, Xuefei
    Xu, Yuan
    Liu, Yanchen
    Liang, Yandong
    [J]. INTERNATIONAL JOURNAL OF THERMAL SCIENCES, 2023, 184
  • [7] Index for estimation of muscle force from mechanomyography based on the Lempel-Ziv algorithm
    Sarlabous, Leonardo
    Torres, Abel
    Fiz, Jose A.
    Morera, Josep
    Jane, Raimon
    [J]. JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY, 2013, 23 (03) : 548 - 557
  • [8] Software Cost Estimation using SVR based on Immune Algorithm
    Lee, Joon-kil
    Kwon, Ki-Tae
    [J]. SNPD 2009: 10TH ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCES, NETWORKING AND PARALLEL DISTRIBUTED COMPUTING, PROCEEDINGS, 2009, : 462 - 466
  • [9] Study on an Assembly Prediction Method of RV Reducer Based on IGWO Algorithm and SVR Model
    Jin, Shousong
    Cao, Mengyi
    Qian, Qiancheng
    Zhang, Guo
    Wang, Yaliang
    [J]. SENSORS, 2023, 23 (01)
  • [10] Force Myography based Continuous Estimation of Knee Joint Angle using Artificial Neural Network
    Kumar, Amit
    Godiyal, Anoop Kant
    Joshi, Deepak
    [J]. 2019 IEEE 5TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT), 2019,