ArticleMedical & biological engineering & computing2019
From deep learning to transfer learning for the prediction of skeletal muscle forces.
Article in Medical & biological engineering & computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Transfer Learning With Simulated and Recorded Data Improves Predictions of Lateral Pinch Thumb-Tip Forces.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
- Improved Surface Electromyogram-Based Hand-Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning.Sensors (Basel, Switzerland) · 2024Article
- Efficient Generation of Pretraining Samples for Developing a Deep Learning Brain Injury Model via Transfer Learning.Annals of biomedical engineering · 2024Article
- MMG-Based Knee Dynamic Extension Force Estimation Using Cross-Talk and IGWO-LSTM.Bioengineering (Basel, Switzerland) · 2024Article
- Prediction of medial knee contact force using multisource fusion recurrent neural network and transfer learning.Medical & biological engineering & computing · 2024Article
- Estimation of Muscle Forces of Lower Limbs Based on CNN-LSTM Neural Network and Wearable Sensor System.Sensors (Basel, Switzerland) · 2024Article
- From Simulation to Reality: Predicting Torque With Fatigue Onset via Transfer Learning.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024Article
- Deep reinforcement learning coupled with musculoskeletal modelling for a better understanding of elderly falls.Medical & biological engineering & computing · 2022Article
- Recurrent neural network to predict hyperelastic constitutive behaviors of the skeletal muscle.Medical & biological engineering & computing · 2022Article
- Deep Learning-Based Myoelectric Potential Estimation Method for Wheelchair Operation.Sensors (Basel, Switzerland) · 2022Article
- InverseMuscleNET: Alternative Machine Learning Solution to Static Optimization and Inverse Muscle Modeling.Frontiers in computational neuroscience · 2021Article
- Medical expert system for low back pain management: design issues and conflict resolution with Bayesian network.Medical & biological engineering & computing · 2020Article
- Multi-Segmentation Parallel CNN Model for Estimating Assembly Torque Using Surface Electromyography Signals.Sensors (Basel, Switzerland) · 2020Article
- Multi-view deep learning for rigid gas permeable lens base curve fitting based on Pentacam images.Medical & biological engineering & computing · 2020Article
- Transfer learning for informative-frame selection in laryngoscopic videos through learned features.Medical & biological engineering & computing · 2020Article
- sEMG-Based Neural Network Prediction Model Selection of Gesture Fatigue and Dataset Optimization.Computational intelligence and neuroscience · 2020Article
- Single Inertial Sensor-Based Neural Networks to Estimate COM-COP Inclination Angle During Walking.Sensors (Basel, Switzerland) · 2019Article
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Abstract
Skeletal muscle forces may be estimated using rigid musculoskeletal models and neural networks. Neural network (NN) approach has the advantages of real-time estimation ability and promising prediction accuracy. However, most of the developed NN models are based on conventional feedforward NNs, which do not take dynamic temporal relationships of the muscle force profiles into consideration. The objectives of this present paper are twofold: (1) to develop a recurrent deep neural network (RDNN) incorporating dynamic temporal relationships to estimate skeletal muscle forces from kinematics data during a gait cycle; (2) then to establish a transfer learning strategy to improve the accuracy of muscle force estimation. A long short-term memory (LSTM) model as a RDNN was developed and evaluated. A weight transfer strategy was established. Three databases were established for training and evaluation purposes. The predictions of rectus femoris, soleus, and tibialis anterior forces with developed LSTM network show root mean square error range of 2.4-84.6 N. Relative root mean square error (RMSE) deviations for internal and external validations are less than 5% and 10% for all analyzed muscles respectively. Pearson correlation coefficients (R) range of 0.95-0.999 showed perfect waveform similarity between data and predicted muscle forces for all analyzed muscles. The use of weight transfer leads to an improvement of 1.3% for the relative deviation between simulation outcome and LSMT prediction. This present study suggests that the recurrent deep neural network is a powerful and accurate computational tool for the prediction of skeletal muscle forces. Moreover, the coupling between this deep learning approach and a transfer learning strategy leads to improve the prediction accuracy. In future work, this coupling approach will be incorporated into a developed decision support tool for functional rehabilitation with real-time estimation and tracking of skeletal muscle forces. Graphical abstract ᅟ.
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