Design of Upper Limb Muscle Strength Assessment System Based on Surface Electromyography Signals and Joint Motion

Overview

Journal Front Neurol

Specialty Neurology

Date 2024 Dec 30

PMID 39734626

Authors

Siqi Wang

Wei Lai

Yipeng Zhang

Junyu Yao

Xingyue Gou

Hui Ye

Jun Yi

Dong Cao

Affiliations

Soon will be listed here.

Abstract

Purpose: This study aims to develop a assessment system for evaluating shoulder joint muscle strength in patients with varying degrees of upper limb injuries post-stroke, using surface electromyographic (sEMG) signals and joint motion data.

Methods: The assessment system includes modules for acquiring muscle electromyography (EMG) signals and joint motion data. The EMG signals from the anterior, middle, and posterior deltoid muscles were collected, filtered, and denoised to extract time-domain features. Concurrently, shoulder joint motion data were captured using the MPU6050 sensor and processed for feature extraction. The extracted features from the sEMG and joint motion data were analyzed using three algorithms: Random Forest (RF), Backpropagation Neural Network (BPNN), and Support Vector Machines (SVM), to predict muscle strength through regression models. Model performance was evaluated using Root Mean Squared Error (), R-Square ( ), Mean Absolute Error (), and Mean Bias Error (), to identify the most accurate regression prediction algorithm.

Results: The system effectively collected and analyzed the sEMG from the deltoid muscles and shoulder joint motion data. Among the models tested, the Support Vector Regression (SVR) model achieved the highest accuracy with an of 0.8059, of 0.2873, of 0.2155, and of 0.0071. The Random Forest model achieved an of 0.7997, of 0.3039, of 0.2405, and of 0.0090. The BPNN model achieved an of 0.7542, of 0.3173, of 0.2306, and of 0.0783.

Conclusion: The SVR model demonstrated superior accuracy in predicting muscle strength. The RF model, with its feature importance capabilities, provides valuable insights that can assist therapists in the muscle strength assessment process.

References

Fernandez-Solana J, Alvarez-Pardo S, Moreno-Villanueva A, Santamaria-Pelaez M, Gonzalez-Bernal J, Velez-Santamaria R . Efficacy of a Rehabilitation Program Using Mirror Therapy and Cognitive Therapeutic Exercise on Upper Limb Functionality in Patients with Acute Stroke. Healthcare (Basel). 2024; 12(5). PMC: 10931296. DOI: 10.3390/healthcare12050569. View

Deb S, Islam M, Rahman S, Rahman S . Graph Convolutional Networks for Assessment of Physical Rehabilitation Exercises. IEEE Trans Neural Syst Rehabil Eng. 2022; 30:410-419. DOI: 10.1109/TNSRE.2022.3150392. View

Ng S, Chen P, Chang H, Chun W, Kong T, Lam Y . Psychometric properties of Upper-body Dressing Scale in people with stroke. J Rehabil Med. 2023; 55:jrm00391. PMC: 10161440. DOI: 10.2340/jrm.v55.5766. View

Borbely B, Szolgay P . Real-time inverse kinematics for the upper limb: a model-based algorithm using segment orientations. Biomed Eng Online. 2017; 16(1):21. PMC: 5240469. DOI: 10.1186/s12938-016-0291-x. View

Kim J, Park G, Lee S, Nam Y . Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors. Sensors (Basel). 2020; 20(6). PMC: 7146614. DOI: 10.3390/s20061622. View

Li R, Zheng S, Zhang Y, Zhang H, Du L, Cheng L . Quantitative assessment of thenar to evaluate hand function after stroke by Bayes discriminant. BMC Musculoskelet Disord. 2023; 24(1):682. PMC: 10463400. DOI: 10.1186/s12891-023-06789-w. View

Gomez-Vilda P, Gomez-Rodellar A, Ferrandez Vicente J, Mekyska J, Palacios-Alonso D, Rodellar-Biarge V . Neuromechanical Modelling of Articulatory Movements from Surface Electromyography and Speech Formants. Int J Neural Syst. 2018; 29(2):1850039. DOI: 10.1142/S0129065718500399. View

Burns A, Adeli H, Buford J . Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network. J Med Syst. 2020; 44(10):176. DOI: 10.1007/s10916-020-01639-x. View

Tjhai C, OKeefe K . Using Step Size and Lower Limb Segment Orientation from Multiple Low-Cost Wearable Inertial/Magnetic Sensors for Pedestrian Navigation. Sensors (Basel). 2019; 19(14). PMC: 6679558. DOI: 10.3390/s19143140. View

10.

Bai J, Li G, Lu X, Wen X . Automatic rehabilitation assessment method of upper limb motor function based on posture and distribution force. Front Neurosci. 2024; 18:1362495. PMC: 10909926. DOI: 10.3389/fnins.2024.1362495. View

11.

Chung C, Su M, Lee S, Wu E, Tang L, Yeh S . An Intelligent Motor Assessment Method Utilizing a Bi-Lateral Virtual-Reality Task for Stroke Rehabilitation on Upper Extremity. IEEE J Transl Eng Health Med. 2022; 10:2100811. PMC: 9704741. DOI: 10.1109/JTEHM.2022.3213348. View

12.

Yan Y, Chen R, Yang Z, Ma Y, Huang J, Luo L . Application of back propagation neural network model optimized by particle swarm algorithm in predicting the risk of hypertension. J Clin Hypertens (Greenwich). 2022; 24(12):1606-1617. PMC: 9731601. DOI: 10.1111/jch.14597. View

13.

Wang A, Tian X, Jiang D, Yang C, Xu Q, Zhang Y . Rehabilitation with brain-computer interface and upper limb motor function in ischemic stroke: A randomized controlled trial. Med. 2024; 5(6):559-569.e4. DOI: 10.1016/j.medj.2024.02.014. View

14.

Passon A, Schauer T, Seel T . Inertial-Robotic Motion Tracking in End-Effector-Based Rehabilitation Robots. Front Robot AI. 2021; 7:554639. PMC: 7806092. DOI: 10.3389/frobt.2020.554639. View

15.

Rech K, Salazar A, Marchese R, Schifino G, Cimolin V, Pagnussat A . Fugl-Meyer Assessment Scores Are Related With Kinematic Measures in People with Chronic Hemiparesis after Stroke. J Stroke Cerebrovasc Dis. 2019; 29(1):104463. DOI: 10.1016/j.jstrokecerebrovasdis.2019.104463. View

16.

Dai C, Hu X . Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings. Int J Neural Syst. 2018; 29(1):1850025. DOI: 10.1142/S0129065718500259. View

17.

Wu Y, Xin B, Wan Q, Ren Y, Jiang W . Risk factors and prediction models for cardiovascular complications of hypertension in older adults with machine learning: A cross-sectional study. Heliyon. 2024; 10(6):e27941. PMC: 10950703. DOI: 10.1016/j.heliyon.2024.e27941. View

18.

Ueyama Y, Takebayashi T, Takeuchi K, Yamazaki M, Hanada K, Okita Y . Attempt to Make the Upper-Limb Item of Objective Fugl-Meyer Assessment Using 9-Axis Motion Sensors. Sensors (Basel). 2023; 23(11). PMC: 10255665. DOI: 10.3390/s23115213. View

19.

Wu C, Zha D, Gao H . Prediction of Bronchopneumonia Inpatients' Total Hospitalization Expenses Based on BP Neural Network and Support Vector Machine Models. Comput Math Methods Med. 2022; 2022:9275801. PMC: 9132643. DOI: 10.1155/2022/9275801. View

20.

Gaur P, McCreadie K, Pachori R, Wang H, Prasad G . Tangent Space Features-Based Transfer Learning Classification Model for Two-Class Motor Imagery Brain-Computer Interface. Int J Neural Syst. 2019; 29(10):1950025. DOI: 10.1142/S0129065719500254. View