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

• Published in: Bioengineering (Basel) Vol. 10; no. 5; p. 510
• Main Authors: Dasgupta, Abhishek, Sharma, Rahul, Mishra, Challenger, Nagaraja, Vikranth Harthikote
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.04.2023; MDPI
• Subjects: Bioengineering; Biomechanics; Decision making; feed-forward neural network; gated recurrent unit; In vivo methods and tests; inertial motion capture; Kinematics; Learning algorithms; linear model; Long short-term memory; Machine learning; Modelling; Motion capture; Neural networks; Recurrent neural networks; Sensors; Software; United Kingdom; Netherlands; recurrent neural network; feed-forward neural network; optical motion capture; gated recurrent unit; linear model; inertial motion capture; machine learning; musculoskeletal modelling; upper extremity; long short-term memory
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering10050510

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’.