Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG

Abstract

Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moments) which cannot be readily measured in vivo. Physics-based computational neuromusculoskeletal models can interpret the dynamic interaction between neural drive to muscles, muscle dynamics, body and joint kinematics and kinetics. Still, such set of solutions suffers from slowness, especially for the complex models, hindering the utility in real-time applications. In recent years, datadriven methods have emerged as a promising alternative due to the benefits in speedy and simple implementation, but they cannot reflect the underlying neuromechanical processes. This paper proposes a physics-informed deep learning framework for musculoskeletal modelling, where physics-based domain knowledge is brought into the data-driven model as soft constraints to penalise/regularise the data-driven model. We use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework. Simultaneously, the physics law between muscle forces and joint kinematics is used the soft constraint. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The experimental results demonstrate the effectiveness and robustness of the proposed framework.

Neural Networks Estimate Muscle Force in Dynamic Conditions Better than Hill-type Muscle Models

“…A dataset that better represents the complete functional range could improve network performance, while a physics-informed neural network (e.g. [29,30]) could also improve network performance at extremes where data recording is difficult. A physics-informed neural network calculates the loss not only from training data, but also based on some physics relationships that the network should follow.…”

Multi-Action Knee Contact Force Prediction by Domain Adaptation

“…Recently, researchers have turned to data-driven (machine learning) approaches for estimating human biomechanics, since they are more automated, require less parameterization and manual effort, and offer real-time solutions as well. Most works develop surrogate models for force estimation or prediction that focus on estimating medial and lateral knee contact (KC) forces [5], [7]- [9] or muscle forces in lower extremities [5], [9], [11] using a plethora of DL techniques, such as ANNs [7], [8], RNNs, fully-connected neural networks [9], and CNNs [5], [9], or ML algorithms, such as principal component regression (i.e. a regression analysis based on PCA) [9].…”

“…In respect to application field, ML-based solutions focus mainly in estimating tibiofemoral load data during gait [5], [7], [9], [11], sit-to-stand [9] or more rarely sport movements [8]. Models were trained using raw data (marker motion data, ground reaction forces (GRFs), muscle electromyography (EMG), IMU signals) as well as derivative data from musculoskeletal analyses (e.g.…”

Distributional Prototypical Methods for Reliable Explanation Space Construction

“…This notion, that the difference in the explana-tions given by the model need to mirror the differences that humans perceive, motivated us to use similarity information to improve explainability as in knowledge-based approaches. Papers that have been improved by using this knowledge include [35] and [36] which improve musculoskeletal modeling based on information from physics.…”