Article
Estimation of Knee Joint Extension Force Using
Mechanomyography Based on IGWO-SVR Algorithm
Zebin Li
1,2,3,
* , Lifu Gao
1
, Wei Lu
1,2
, Daqing Wang
1
, Chenlei Xie
1
and Huibin Cao
1,
*
Citation: Li, Z.; Gao, L.; Lu, W.;
Wang, D.; Xie, C.; Cao, H. Estimation
of Knee Joint Extension Force Using
Mechanomyography Based on
IGWO-SVR Algorithm. Electronics
2021, 10, 2972. https://doi.org/
10.3390/electronics10232972
Academic Editors: YangQuan Chen,
Subhas Mukhopadhyay, Nunzio
Cennamo, Mohamed Jamal Deen,
Junseop Lee, Simone Morais
and Jichai Jeong
Received: 1 October 2021
Accepted: 25 November 2021
Published: 29 November 2021
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4.0/).
1
Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences,
Hefei 230031, China; lifugao@iim.ac.cn (L.G.); lw9296@mail.ustc.edu.cn (W.L.);
dqwang@mail.ustc.edu.cn (D.W.); xiecl@mail.ustc.edu.cn (C.X.)
2
Department of Science Island, University of Science and Technology of China, Hefei 230026, China
3
School of Electrical and Photoelectric Engineering, West Anhui University, Lu’an 237012, China
* Correspondence: robotzebinli@foxmail.com (Z.L.); hbcao@iim.ac.cn (H.C.)
Abstract:
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.
Keywords:
mechanomyography; knee joint extension force; improved gray wolf algorithm; support
vector machine
1. Introduction
With the rapid increase in global aging, the elderly are more likely to lose their ability
to exercise due to stroke, arthritis, central nervous system diseases, and other diseases.
According to the Indian Aging Report 2017, the cases of arthritis and stroke among the
elderly may increase up to 55.9 million and 1.9 million, respectively, by 2030 [
1
]. In
addition, millions of people around the world have lost limbs for various reasons, and
according to the World Health Organization, there are approximately 40 million amputees
in the world [
2
,
3
]. High-performance prosthetics and rehabilitation training equipment
can significantly improve the quality of life of the elderly and patients with lost limbs.
However, at present, the product gap of assistive devices is huge, and the intelligence
degree is not high. Simple manual supervision or simple assistive devices have been unable
to meet the needs of the disabled and the elderly. Therefore, safe and reliable auxiliary
training instruments and intelligent and convenient prosthetic devices will greatly improve
Electronics 2021, 10, 2972. https://doi.org/10.3390/electronics10232972 https://www.mdpi.com/journal/electronics
