Knee Muscle Force Estimating Model Using Machine Learning Approach

Abstract

Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

Does enforcing glenohumeral joint stability matter? A new rapid muscle redundancy solver highlights the importance of non-superficial shoulder muscles

“…Several optimization-based approaches have been employed to solve the muscle redundancy problem in biomechanical simulations, which fall into three categories: methods integrating the dynamics of the model [ 7 – 11 ], methods considering the model statically [ 12 – 16 ], and data-driven approaches [ 17 , 18 ]. However, despite these efforts, there are several open issues regarding the estimation of muscle forces from a musculoskeletal model [ 19 ].…”

“…Neglecting passive forces leads to simplifications of muscle function [ 21 ], together with poor performances in estimating antagonist muscle activity at the GH joint [ 22 ]. Finally, recent data-driven machine learning methods achieved promising results [ 17 , 18 ], yet they currently disregard musculoskeletal properties altogether, retaining little direct connection with the way the human body actually functions.…”

Predicting Tissue Loads in Running from Inertial Measurement Units

“…Recent developments in data science have inspired data-based models linking inertial measurement unit (IMU) kinematics, complete kinematics [ 21 ] and musculoskeletal kinetics [ 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. Such models can be trained on musculoskeletal simulations based on optical motion capture data and have shown promising results for modeling of a variety of tasks, but on relatively small and uniform cohorts of test subjects.…”

Does enforcing glenohumeral joint stability matter? A new rapid muscle redundancy solver highlights the importance of non-superficial shoulder muscles

“…Several optimization-based approaches have been employed to solve the muscle redundancy problem in biomechanical simulations, which fall into three categories: methods integrating the dynamics of the model [7][8][9][10][11], methods considering the model statically [12][13][14][15][16], and data-driven approaches [17,18]. However, despite these efforts, there are several open issues regarding the estimation of muscle forces from a musculoskeletal model [19].…”