Estimation of Elbow Flexion Torque Using Equilibrium Optimizer on Feature Selection of NMES MMG Signals and Hyperparameter Tuning of Random Forest Regression

Overview

Journal Front Rehabil Sci

Specialty Rehabilitation Medicine

Date 2025 Mar 6

PMID 40046456

Authors

Raphael Uwamahoro

Kenneth Sundaraj

Farah Shahnaz Feroz

Affiliations

Soon will be listed here.

Abstract

Background: The assessment of limb joint torque is essential for understanding musculoskeletal system dynamics. Yet, the lack of direct muscle strength measurement techniques has prompted previous research to deploy joint torque estimation using machine learning models. These models often suffer from reduced estimation accuracies due to the presence of redundant and irrelevant information within the rapidly expanding complex biomedical datasets as well as suboptimal hyperparameters configurations.

Methods: This study utilized a random forest regression (RFR) model to estimate elbow flexion torque using mechanomyography (MMG) signals recorded during electrical stimulation of the biceps brachii (BB) muscle in 36 right-handed healthy subjects. Given the significance of both feature engineering and hyperparameter tuning in optimizing RFR performance, this study proposes a hybrid method leveraging the General Learning Equilibrium Optimizer (GLEO) to identify most informative MMG features and tune RFR hyperparameters. The performance of the GLEO-coupled with the RFR model was compared with the standard Equilibrium Optimizer (EO) and other state-of-the-art algorithms in physical and physiological function estimation using biological signals.

Results: Experimental results showed that selected features and tuned hyperparameters demonstrated a significant improvement in root mean square error (RMSE), coefficient of determination (R) and slope with values improving from 0.1330 to 0.1174, 0.7228 to 0.7853 and 0.6946 to 0.7414, respectively for the test dataset. Convergence analysis further revealed that the GLEO algorithm exhibited a superior learning capability compared to EO.

Conclusion: This study underscores the potential of the hybrid GLEO approach in selecting highly informative features and optimizing hyperparameters for machine learning models. These advancements are essential for evaluating muscle function and represent a significant advancement in musculoskeletal biomechanics research.

References

Hondo N, Tsuji T . Torque Estimation of Knee Flexion and Extension Movements From a Mechanomyogram of the Femoral Muscle. IEEE Trans Neural Syst Rehabil Eng. 2022; 30:1120-1126. DOI: 10.1109/TNSRE.2022.3169225. View

Sirago G, Pellegrino M, Bottinelli R, Franchi M, Narici M . Loss of neuromuscular junction integrity and muscle atrophy in skeletal muscle disuse. Ageing Res Rev. 2022; 83:101810. DOI: 10.1016/j.arr.2022.101810. View

Liu J, Zhou Z, Kong S, Ma Z . Application of random forest based on semi-automatic parameter adjustment for optimization of anti-breast cancer drugs. Front Oncol. 2022; 12:956705. PMC: 9353770. DOI: 10.3389/fonc.2022.956705. View

Islam M, Sundaraj K, Ahmad R, Ahamed N . Mechanomyogram for muscle function assessment: a review. PLoS One. 2013; 8(3):e58902. PMC: 3594217. DOI: 10.1371/journal.pone.0058902. View

Velez-Guerrero M, Callejas-Cuervo M, Mazzoleni S . Design, Development, and Testing of an Intelligent Wearable Robotic Exoskeleton Prototype for Upper Limb Rehabilitation. Sensors (Basel). 2021; 21(16). PMC: 8401039. DOI: 10.3390/s21165411. View

Khushaba R, Takruri M, Valls Miro J, Kodagoda S . Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features. Neural Netw. 2014; 55:42-58. DOI: 10.1016/j.neunet.2014.03.010. View

Srivastava A, Karpievitch Y, Eichten S, Borevitz J, Lister R . HOME: a histogram based machine learning approach for effective identification of differentially methylated regions. BMC Bioinformatics. 2019; 20(1):253. PMC: 6521357. DOI: 10.1186/s12859-019-2845-y. View

Chimera N, Holmes M, Gabriel D . Anthropometrics and electromyography as predictors for maximal voluntary isometric wrist torque: Considerations for ergonomists. Appl Ergon. 2021; 97:103496. DOI: 10.1016/j.apergo.2021.103496. View

Santos E, Fernandes Vara M, Ranciaro M, Strasse W, Neto G, Nohama P . Influence of sensor mass and adipose tissue on the mechanomyography signal of elbow flexor muscles. J Biomech. 2021; 122:110456. DOI: 10.1016/j.jbiomech.2021.110456. View

10.

Zhang Y, Xia C . A preliminary study of classification of upper limb motions and forces based on mechanomyography. Med Eng Phys. 2020; 81:97-104. DOI: 10.1016/j.medengphy.2020.05.009. View

11.

Li Z, Gao L, Lu W, Wang D, Cao H, Zhang G . Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR. Sensors (Basel). 2022; 22(12). PMC: 9231143. DOI: 10.3390/s22124651. View

12.

van Dam K, Gielissen M, Bles R, van der Poel A, Boon B . The impact of assistive living technology on perceived independence of people with a physical disability in executing daily activities: a systematic literature review. Disabil Rehabil Assist Technol. 2023; 19(4):1262-1271. DOI: 10.1080/17483107.2022.2162614. View

13.

Zhao Y, Zhang Z, Li Z, Yang Z, Dehghani-Sanij A, Xie S . An EMG-Driven Musculoskeletal Model for Estimating Continuous Wrist Motion. IEEE Trans Neural Syst Rehabil Eng. 2020; 28(12):3113-3120. DOI: 10.1109/TNSRE.2020.3038051. View

14.

Forman D, Abdel-Malek D, Bunce C, Holmes M . Muscle length and joint angle influence spinal but not corticospinal excitability to the biceps brachii across forearm postures. J Neurophysiol. 2019; 122(1):413-423. PMC: 6689784. DOI: 10.1152/jn.00620.2018. View

15.

Alon G, Smith G . Tolerance and conditioning to neuro-muscular electrical stimulation within and between sessions and gender. J Sports Sci Med. 2014; 4(4):395-405. PMC: 3899655. View

16.

Sakr M, Jiang X, Menon C . Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography. Front Robot AI. 2021; 6:120. PMC: 7805656. DOI: 10.3389/frobt.2019.00120. View

17.

Beck T, Housh T, Johnson G, Weir J, Cramer J, Coburn J . Mechanomyographic and electromyographic time and frequency domain responses during submaximal to maximal isokinetic muscle actions of the biceps brachii. Eur J Appl Physiol. 2004; 92(3):352-9. DOI: 10.1007/s00421-004-1110-9. View

18.

Nagineni S, Taran S, Bajaj V . Features based on variational mode decomposition for identification of neuromuscular disorder using EMG signals. Health Inf Sci Syst. 2018; 6(1):13. PMC: 6146874. DOI: 10.1007/s13755-018-0050-4. View

19.

Gravholt A, Herskind J, Kloster C, Hvid L, Overgaard K . Torque and Discomfort During Electrically Evoked Muscle Contractions in Healthy Young Adults: Influence of Stimulation Current and Pulse Frequency. Arch Phys Med Rehabil. 2022; 104(3):444-450. DOI: 10.1016/j.apmr.2022.09.004. View

20.

Willingham T, Melbourn J, Moldavskiy M, McCully K, Backus D . Effect of Antigravity Treadmill Training on Muscle Oxidative Capacity, Muscle Endurance, and Walking Function in a Person with Multiple Sclerosis. Int J MS Care. 2018; 20(4):186-190. PMC: 6107344. DOI: 10.7224/1537-2073.2017-035. View