Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network

Mundt, Marion; Koeppe, Arnd; David, Sina; Witter, Tom; Bamer, Franz; Potthast, Wolfgang; Markert, Bernd

doi:10.3389/fbioe.2020.00041

Cited by 122 publications

(142 citation statements)

References 56 publications

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“…In contrast to physics-based approaches, machine learning models require representative training data. Combining simulated and measured data seems a promising approach (Mundt et al, 2020a ). In this work, we focused on the comparison between learning on measured and learning on simulated data to evaluate whether simulations can decrease the generalization error by incorporating variations of movement.…”

Section: Discussionmentioning

confidence: 99%

“…In this work, we focused on the comparison between learning on measured and learning on simulated data to evaluate whether simulations can decrease the generalization error by incorporating variations of movement. Future work should expand this method to 3D analysis and evaluate against state of the art methods (Stetter et al, 2019 ; Mundt et al, 2020a ). 3D biomechanical optimal control simulations are more expensive to compute, but are advancing recently (Falisse et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…Huang et al ( 2018 ) also synthesized inertial sensor data from motion capture datasets using a 3D model of the human body shape and pose (SMPL) together with a virtual sensor model. Mundt et al ( 2020a , b ) used OMC data from several studies of their lab together with a biomechanical model to create a large simulated dataset, which was used for training feedforward neural networks to estimate joint kinematics and kinetics. One drawback of these approaches is that additional datasets containing OMC data or SMPL poses of the movement of interest were required.…”

Section: Introductionmentioning

confidence: 99%

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CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data

Dorschky

Nitschke

Martindale

et al. 2020

Front. Bioeng. Biotechnol.

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Machine learning is a promising approach to evaluate human movement based on wearable sensor data. A representative dataset for training data-driven models is crucial to ensure that the model generalizes well to unseen data. However, the acquisition of sufficient data is time-consuming and often infeasible. We present a method to create realistic inertial sensor data with corresponding biomechanical variables by 2D walking and running simulations. We augmented a measured inertial sensor dataset with simulated data for the training of convolutional neural networks to estimate sagittal plane joint angles, joint moments, and ground reaction forces (GRFs) of walking and running. When adding simulated data, the root mean square error (RMSE) of the test set of hip, knee, and ankle joint angles decreased up to 17 %, 27 % and 23 %, the RMSE of knee and ankle joint moments up to 6 % and the RMSE of anterior-posterior and vertical GRF up to 2 and 6 %. Simulation-aided estimation of joint moments and GRFs was limited by inaccuracies of the biomechanical model. Improving the physics-based model and domain adaptation learning may further increase the benefit of simulated data. Future work can exploit biomechanical simulations to connect different data sources in order to create representative datasets of human movement. In conclusion, machine learning can benefit from available domain knowledge on biomechanical simulations to supplement cumbersome data collections.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data

Dorschky

Nitschke

Martindale

et al. 2020

Front. Bioeng. Biotechnol.

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“…For the functional calibration, the user needs to be able to execute the required position of movement. Expert knowledge is necessary for both methods to ensure a meaningful and repeatable calibration [ 8 , 12 ]. Lebleu et al [ 8 ] achieved mean RMSE values for functional calibrations of 2.0 to 4.1°.…”

Section: Introductionmentioning

confidence: 99%

“…The validity of simulated data was presented and evaluated previously. The accuracy was

[ 12 ]. Long short-term memory (LSTM) neural networks were trained on the different input data.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks in Motion Analysis—Applications of Unsupervised and Heuristic Feature Selection Techniques

Mundt

Koeppe

Bamer

et al. 2020

Sensors

Self Cite

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The use of machine learning to estimate joint angles from inertial sensors is a promising approach to in-field motion analysis. In this context, the simplification of the measurements by using a small number of sensors is of great interest. Neural networks have the opportunity to estimate joint angles from a sparse dataset, which enables the reduction of sensors necessary for the determination of all three-dimensional lower limb joint angles. Additionally, the dimensions of the problem can be simplified using principal component analysis. Training a long short-term memory neural network on the prediction of 3D lower limb joint angles based on inertial data showed that three sensors placed on the pelvis and both shanks are sufficient. The application of principal component analysis to the data of five sensors did not reveal improved results. The use of longer motion sequences compared to time-normalised gait cycles seems to be advantageous for the prediction accuracy, which bridges the gap to real-time applications of long short-term memory neural networks in the future.

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GMM‐LIME explainable machine learning model for interpreting sensor‐based human gait

Mulwa,

Mwangi,

Mindila

2024

Engineering Reports

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Machine learning (ML) has been used in human gait data for appropriate assistive device prediction. However, their uptake in the medical setup still remains low due to their black box nature which restricts clinicians from understanding how they operate. This has led to the exploration of explainable ML. Studies have recommended local interpretable model‐agnostic explanation (LIME) because it builds sparse linear models around an individual prediction in its local vicinity hence fast and also because it could be used on any ML model. LIME is however, is not always stable. The research aimed to enhance LIME to attain stability by avoid the sampling step through combining Gaussian mixture model (GMM) sampling. To test performance of the GMM‐LIME, supervised ML were recommended because study revealed that their accuracy was above 90% when used on human gait. Neural networks were adopted for GaitRec dataset and Random Forest (RF) was adopted and applied on HugaDB datasets. Maximum accuracies attained were multilayer perceptron (95%) and RF (99%). Graphical results on stability and Jaccard similarity scores were presented for both original LIME and GMM‐LIME. Unlike original LIME, GMM‐LIME produced not only more accurate and reliable but also consistently stable explanations.

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Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network

Cited by 122 publications

References 56 publications

CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data

CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data

Artificial Neural Networks in Motion Analysis—Applications of Unsupervised and Heuristic Feature Selection Techniques

GMM‐LIME explainable machine learning model for interpreting sensor‐based human gait

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