Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data

Abstract

Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system is lab-based, expensive, and requires a line of sight. Inertial Motion Capture (IMC) techniques are widely-used alternatives, which are portable, user-friendly, and relatively low-cost, although with lesser accuracy. Irrespective of the choice of motion capture technique, one typically uses an MSK model to obtain the kinematic and kinetic outputs, which is a computationally expensive tool increasingly well approximated by machine learning (ML) methods. Here, an ML approach is presented that maps experimentally recorded IMC input data to the human upper-extremity MSK model outputs computed from (‘gold standard’) OMC input data. Essentially, this proof-of-concept study aims to predict higher-quality MSK outputs from the much easier-to-obtain IMC data. We use OMC and IMC data simultaneously collected for the same subjects to train different ML architectures that predict OMC-driven MSK outputs from IMC measurements. In particular, we employed various neural network (NN) architectures, such as Feed-Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs) (vanilla, Long Short-Term Memory, and Gated Recurrent Unit) and a comprehensive search for the best-fit model in the hyperparameters space in both subject-exposed (SE) as well as subject-naive (SN) settings. We observed a comparable performance for both FFNN and RNN models, which have a high degree of agreement (ravg,SE,FFNN=0.90±0.19, ravg,SE,RNN=0.89±0.17, ravg,SN,FFNN=0.84±0.23, and ravg,SN,RNN=0.78±0.23) with the desired OMC-driven MSK estimates for held-out test data. The findings demonstrate that mapping IMC inputs to OMC-driven MSK outputs using ML models could be instrumental in transitioning MSK modelling from ‘lab to field’.

Gait Recognition and Assistance Parameter Prediction Determination Based on Kinematic Information Measured by Inertial Measurement Units

“…In terms of sensor system design, commonly used sensors include inertial sensors (IMUs) [ 3 , 4 , 5 ], plantar pressure sensors [ 6 ], surface electromyography electrodes [ 7 , 8 ], and electroencephalogram electrodes [ 9 ]. IMUs are widely used in gait motion recognition for their high portability, low cost, and environmental friendliness [ 10 , 11 , 12 , 13 , 14 ].…”

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.…”

Skeletal Modeling in Rhino Grasshopper—A Confirmed Kinematic Model

“…This study aims to provide further insights into the relationship between work-related activities and musculoskeletal disorders in the arms, while developing a new tool to further perform such analyses. The musculoskeletal systems of humans mechanically are very complex and computational models must be highly simplified in order to be efficient [14]. Gathering and processing biomechanical data requires compromises to define boundary conditions.…”