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.

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The surface electromyography (SEMG) based exoskeleton presents a new opportunity for human augmentation and rehabilitation. Developing an efficient exoskeleton in real‐time is challenging as each individual's muscles and joint forces are unique. The aim of this research article is to analyze and evaluate the design of the lower limb exoskeleton during the squatting movement in a simulated environment to address problems concerning the development of a functional exoskeleton for an individual. An exoskeleton was designed in SolidWorks CAD software and imported into AnyBody Modelling Software (AMS). Thereafter, the performance of 3D designed exoskeleton was evaluated by placing various loads (0:5:25 kg) on both the shoulders of the human musculoskeletal. The results show the force in the knee muscles with the assistance of the exoskeleton were reduced significantly by 65.18–97.20% in the biceps femoris, 50.01–33.16% in the rectus femoris, 41.87–28.31% in the vastus lateralis, 42.25–28.78% in the vastus medialis, 7.28–22.91% in gluteus medius, and 22.54–13.13% in semitendinosus. The force in the knee joint was reduced by 44.04–31.43% as the load increases. Individual muscle force estimated from the SEMG signal and AMS during squatting was also compared for validation. The developed model helps in understanding the load effects on different muscles and provides useful information for the construction of an individual's optimized exoskeleton.