A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculoskeletal Systems

Taneja, Karan; He, Xiaolong; He, Qi; Zhao, Xinlun; Lin, Yung-Yang; Loh, Kenneth J.; Chen, Jiun‐Shyan

doi:10.1115/1.4055238

Cited by 12 publications

(19 citation statements)

References 56 publications

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“…For example, physics-based models simplify the human body using parameterized formulas, where parameters were traditionally determined empirically or using population averages. Alternatively, these parameters can be learned from collected data using machine learning 115 , 116 . Extraction of 3-D joint angles from camera data.…”

Section: Discussionmentioning

confidence: 99%

A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation

et al. 2023

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Anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) surgery are common. Laboratory-based biomechanical assessment can evaluate ACL injury risk and rehabilitation progress after ACLR; however, lab-based measurements are expensive and inaccessible to most people. Portable sensors such as wearables and cameras can be deployed during sporting activities, in clinics, and in patient homes. Although many portable sensing approaches have demonstrated promising results during various assessments related to ACL injury, they have not yet been widely adopted as tools for out-of-lab assessment. The purpose of this review is to summarize research on out-of-lab portable sensing applied to ACL and ACLR and offer our perspectives on new opportunities for future research and development. We identified 49 original research articles on out-of-lab ACL-related assessment; the most common sensing modalities were inertial measurement units, depth cameras, and RGB cameras. The studies combined portable sensors with direct feature extraction, physics-based modeling, or machine learning to estimate a range of biomechanical parameters (e.g., knee kinematics and kinetics) during jump-landing tasks, cutting, squats, and gait. Many of the reviewed studies depict proof-of-concept methods for potential future clinical applications including ACL injury risk screening, injury prevention training, and rehabilitation assessment. By synthesizing these results, we describe important opportunities that exist for clinical validation of existing approaches, using sophisticated modeling techniques, standardization of data collection, and creation of large benchmark datasets. If successful, these advances will enable widespread use of portable-sensing approaches to identify ACL injury risk factors, mitigate high-risk movements prior to injury, and optimize rehabilitation paradigms.

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Section: Discussionmentioning

confidence: 99%

A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation

et al. 2023

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Section: Discussionmentioning

confidence: 99%

Towards Out-of-Lab Anterior Cruciate Ligament Injury Prevention and Rehabilitation Assessment: A Review of Portable Sensing Approaches

Tan

Gatti

Fan

et al. 2022

Preprint

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Anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) surgery are common. Many ACL-injured subjects develop osteoarthritis within a decade of injury, a major cause of disability without cure. Laboratory-based biomechanical assessment can evaluate ACL injury risk and rehabilitation progress after ACLR; however, lab-based measurements are expensive and inaccessible to a majority of people. Portable sensors such as wearables and cameras can be deployed during sporting activities, in clinics, and in patient homes for biomechanical assessment. Although many portable sensing approaches have demonstrated promising results during various assessments related to ACL injury, they have not yet been widely adopted as tools for ACL injury prevention training, evaluation of ACL reconstructions, and return-to-sport decision making. The purpose of this review is to summarize research on out-of-lab portable sensing applied to ACL and ACLR and offer our perspectives on new opportunities for future research and development. We identified 49 original research articles on out-of-lab ACL-related assessment; the most common sensing modalities were inertial measurement units (IMUs), depth cameras, and RGB cameras. The studies combined portable sensors with direct feature extraction, physics-based modeling, or machine learning to estimate a range of biomechanical parameters (e.g., knee kinematics and kinetics) during jump-landing tasks, cutting, squats, and gait. Many of the reviewed studies depict proof-of-concept methods for potential future clinical applications including ACL injury risk screening, injury prevention training, and rehabilitation assessment. By synthesizing these results, we describe important opportunities that exist for using sophisticated modeling techniques to enable more accurate assessment along with standardization of data collection and creation of large benchmark datasets. If successful, these advances will enable widespread use of portable-sensing approaches to identify ACL injury risk factors, mitigate high-risk movements prior to injury, and optimize rehabilitation paradigms.

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“…The prediction of the evolution of state variables in dynamical systems has been a vital component to several scientific applications such as biology, geophysics, earthquake engineering, solid mechanics, robotics, computer vision [ 1 – 7 ] etc. Black-box techniques based on data-driven mapping and development of parameterized multi-physics models describing the progression of the data have been previously utilized for making predictions on the states.…”

Section: Introductionmentioning

confidence: 99%

“…By minimizing the residuals of the governing partial differential equations (PDEs) and the associated initial and boundary conditions, PINNs have been successfully applied to solve forward problems [ 11 , 40 , 41 ], and inverse problems [ 11 , 38 , 42 – 44 ], where the unknown system characteristics are considered trainable parameters or functions [ 38 , 45 ]. For biomechanics and biomedical applications [ 1 , 46 – 50 ], this method has been applied extensively along with other ML techniques [ 51 , 52 ]. These attempt to bridge the gap between ML-based data-driven surrogate models and the satisfaction of physical laws.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we focus on the application to musculoskeletal systems, aiming at utilizing non-invasive muscle activity measurements such as surface electromyography (sEMG) signals to predict joint kinetics or kinematics [ 1 , 18 , 19 ], which is of great significance to health assessment and rehabilitation purposes [ 15 , 16 ]. These sEMG signals can be used as control inputs to drive the physiological subsystems that are governed by parameterized non-linear differential equations, and thus form the forward dynamics problem.…”

Section: Introductionmentioning

confidence: 99%

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A multi-resolution physics-informed recurrent neural network: formulation and application to musculoskeletal systems

Taneja,

He,

et al. 2023

Comput Mech

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This work presents a multi-resolution physics-informed recurrent neural network (MR PI-RNN), for simultaneous prediction of musculoskeletal (MSK) motion and parameter identification of the MSK systems. The MSK application was selected as the model problem due to its challenging nature in mapping the high-frequency surface electromyography (sEMG) signals to the low-frequency body joint motion controlled by the MSK and muscle contraction dynamics. The proposed method utilizes the fast wavelet transform to decompose the mixed frequency input sEMG and output joint motion signals into nested multi-resolution signals. The prediction model is subsequently trained on coarser-scale input–output signals using a gated recurrent unit (GRU), and then the trained parameters are transferred to the next level of training with finer-scale signals. These training processes are repeated recursively under a transfer-learning fashion until the full-scale training (i.e., with unfiltered signals) is achieved, while satisfying the underlying dynamic equilibrium. Numerical examples on recorded subject data demonstrate the effectiveness of the proposed framework in generating a physics-informed forward-dynamics surrogate, which yields higher accuracy in motion predictions of elbow flexion–extension of an MSK system compared to the case with single-scale training. The framework is also capable of identifying muscle parameters that are physiologically consistent with the subject’s kinematics data.

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A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculoskeletal Systems

Cited by 12 publications

References 56 publications

A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation

A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation

Towards Out-of-Lab Anterior Cruciate Ligament Injury Prevention and Rehabilitation Assessment: A Review of Portable Sensing Approaches

A multi-resolution physics-informed recurrent neural network: formulation and application to musculoskeletal systems

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