Prediction of lower limb joint angles and moments during gait using artificial neural networks

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

doi:10.1007/s11517-019-02061-3

Cited by 81 publications

(105 citation statements)

References 45 publications

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“…However, the first layers of the different networks, which act as feature extractors, probably share some common features such that multi-task learning or transfer learning might improve results (Caruana, 1997 ). Future work should consider different network architectures which avoid pre-processing (segmentation into walking and running cycles and resampling) of sensor data like fully (circular) convolutional networks and allow a continuous estimation of movement biomechanics using recurrent architectures like long short-term memory networks (Mundt et al, 2020b ). In addition, the feature extraction using convolutional layers should be explored.…”

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%

See 1 more Smart Citation

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: Introductionmentioning

confidence: 99%

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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show abstract

“…They found an increased error for a reduced number of input channels. For the application of inertial sensors, the number and placement of sensors used was in general based on a priori decisions [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. Shahabpoor et al [ 35 ] used correlation techniques to find the optimum inertial sensor positions for the prediction of the ground reaction force.…”

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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“…The advantage of LSTM networks over other types of RNN is that the dependency of the current on the previous hidden state is designed in such a way that the LSTM obtains the ability to keep parts of its hidden state over a larger number of time steps than is possible with other RNN architectures, such as NARX. In [18], this type of network was used to estimate the lower body joint angles with simulated kinematic data obtained from the markers of a camera system. The main cell of a LSTM shown in Fig.…”

Section: Long Short-term Memory Networkmentioning

confidence: 99%

“…A similar approach was proposed in [16], where the data from five different sensors placed on the body were collected and a convolutional neural network (CNN) was trained for subject identification. Regarding estimation, different studies apply ML and deep learning to simulated gait data obtained from the markers of camera systems to assess lower limb kinematics [17,18]. In [19], a generalized regression neural network (GRNN) was trained to estimate foot, lower leg, and thigh kinematics in the sagittal plane from emulated 2D foot acceleration signals from a complex camera system and four IMUs during walking.…”

Section: Introductionmentioning

confidence: 99%

Efficiency of deep neural networks for joint angle modeling in digital gait assessment

Alcaraz

Moghaddamnia

Peissig

2021

EURASIP J. Adv. Signal Process.

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Reliability and user compliance of the applied sensor system are two key issues of digital healthcare and biomedical informatics. For gait assessment applications, accurate joint angle measurements are important. Inertial measurement units (IMUs) have been used in a variety of applications and can also provide significant information on gait kinematics. However, the nonlinear mechanism of human locomotion results in moderate estimation accuracy of the gait kinematics and thus joint angles. To develop “digital twins” as a digital counterpart of body lower limb joint angles, three-dimensional gait kinematic data were collected. This work investigates the estimation accuracy of different neural networks in modeling lower body joint angles in the sagittal plane using the kinematic records of a single IMU attached to the foot. The evaluation results based on the root mean square error (RMSE) show that long short-term memory (LSTM) networks deliver superior performance in nonlinear modeling of the lower limb joint angles compared to other machine learning (ML) approaches. Accordingly, deep learning based on the LSTM architecture is a promising approach in modeling of gait kinematics using a single IMU, and thus can reduce the required physical IMUs attached on the subject and improve the practical application of the sensor system.

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Prediction of lower limb joint angles and moments during gait using artificial neural networks

Cited by 81 publications

References 45 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

Efficiency of deep neural networks for joint angle modeling in digital gait assessment

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