Population based approaches to computational musculoskeletal modelling

Fernandez, Justin; Dickinson, Alexander; Hunter, Peter

doi:10.1007/s10237-020-01364-x

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…In particular, using supervised ML models to circumvent biomechanical models that are computationally expensive has become commonplace. Numerous studies have reported ML applications for generating in vivo insights (e.g., joint and muscle loading) from either OMC or IMC inputs [28][29][30][31][32][33][34][35][36][37][38] or markerless methods [27]. Among ML approaches, deep learning models, including Convolutional Neural Networks (CNN) and, increasingly, Recurrent Neural Networks (RNN), have become popular in lowerextremity biomechanical analyses [26].…”

Section: Introductionmentioning

confidence: 99%

“…Among ML approaches, deep learning models, including Convolutional Neural Networks (CNN) and, increasingly, Recurrent Neural Networks (RNN), have become popular in lowerextremity biomechanical analyses [26]. Such ML techniques have multiple benefits over their MSK model-based counterparts-(i) although the ML model's initial training is considerably time-intensive, making predictions corresponding to new data is computationally efficient, facilitating real-time applications; and (ii) they do not require the large amounts of data required for MSK models and help provide population-level insights [32].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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’.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…Due to advances in techniques for classifying such videos, research incorporating deep learning into musculoskeletal fields [19] might be of interest. Given the recent interest in musculoskeletal video data collection [20,21], it is predicted that machine learning or deep learning research using these data would subsequently expand to orthopedic or occupational therapy. Especially due to the coronavirus pandemic, many practical classes have been canceled or reduced, and classical physical training methods are usually the only alternative [22], raising complaints from educators and students; learners are still demanding smart educators who can guide musculoskeletal work in non-face-to-face conditions [23].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches to Automated Video Classification of Upper Limb Tension Test

Choi

Heo

2021

Healthcare

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The purpose of this study was to classify ULTT videos through transfer learning with pre-trained deep learning models and compare the performance of the models. We conducted transfer learning by combining a pre-trained convolution neural network (CNN) model into a Python-produced deep learning process. Videos were processed on YouTube and 103,116 frames converted from video clips were analyzed. In the modeling implementation, the process of importing the required modules, performing the necessary data preprocessing for training, defining the model, compiling, model creation, and model fit were applied in sequence. Comparative models were Xception, InceptionV3, DenseNet201, NASNetMobile, DenseNet121, VGG16, VGG19, and ResNet101, and fine tuning was performed. They were trained in a high-performance computing environment, and validation and loss were measured as comparative indicators of performance. Relatively low validation loss and high validation accuracy were obtained from Xception, InceptionV3, and DenseNet201 models, which is evaluated as an excellent model compared with other models. On the other hand, from VGG16, VGG19, and ResNet101, relatively high validation loss and low validation accuracy were obtained compared with other models. There was a narrow range of difference between the validation accuracy and the validation loss of the Xception, InceptionV3, and DensNet201 models. This study suggests that training applied with transfer learning can classify ULTT videos, and that there is a difference in performance between models.

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OpenHands: An Open-Source Statistical Shape Model of the Finger Bones

Munyebvu,

Metcalf,

Burson-Thomas

et al. 2024

Ann Biomed Eng

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This paper presents statistical shape models of the four fingers of the hand, with an emphasis on anatomic analysis of the proximal and distal interphalangeal joints. A multi-body statistical shape modelling pipeline was implemented on an exemplar training dataset of computed tomography (CT) scans of 10 right hands (5F:5M, 27–37 years, free from disease or injury) imaged at 0.3 mm resolution, segmented, meshed and aligned. Model generated included pose neutralisation to remove joint angle variation during imaging. Repositioning was successful; no joint flexion variation was observed in the resulting model. The first principal component (PC) of morphological variation represented phalanx size in all fingers. Subsequent PCs showed variation in position along the palmar-dorsal axis, and bone breadth: length ratio. Finally, the models were interrogated to provide gross measures of bone lengths and joint spaces. These models have been published for open use to support wider community efforts in hand biomechanical analysis, providing bony anatomy descriptions whilst preserving the security of the underlying imaging data and privacy of the participants. The model describes a small, homogeneous population, and assumptions cannot be made about how it represents individuals outside the training dataset. However, it supplements anthropometric datasets with additional shape information, and may be useful for investigating factors such as joint morphology and design of hand-interfacing devices and products. The model has been shared as an open-source repository (https://github.com/abel-research/OpenHands), and we encourage the community to use and contribute to it.

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Population based approaches to computational musculoskeletal modelling

Cited by 7 publications

References 15 publications

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

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

Deep Learning Approaches to Automated Video Classification of Upper Limb Tension Test

OpenHands: An Open-Source Statistical Shape Model of the Finger Bones

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