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Ankle Joint Torque Prediction Using an NMS Solver Informed-ANN Model and Transfer Learning.
- Source :
-
IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2022 Dec; Vol. 26 (12), pp. 5895-5906. Date of Electronic Publication: 2022 Dec 07. - Publication Year :
- 2022
-
Abstract
- In this work, we predicted ankle joint torque by combining a neuromusculoskeletal (NMS) solver-informed artificial neural network (hybrid-ANN) model with transfer learning based on joint angle and muscle electromyography signals. The hybrid-ANN is an ANN augmented with two kinds of features: 1) experimental measurements - muscle signals and joint angles, and 2) informative physical features extracted from the underlying NMS solver, such as individual muscle force and joint torque. The hybrid-ANN model accuracy in torque prediction was studied in both intra- and inter-subject tests, and compared to the baseline models (NMS and standard-ANN). For each prediction model, seven different cases were studied using data from gait at different speeds and from isokinetic ankle dorsi/plantarflexion motion. Additionally, we integrated a transfer learning method in inter-subject models to improve joint torque prediction accuracy by transferring the learned knowledge from previous participants to a new participant, which could be useful when training data is limited. Our results indicated that better accuracy could be obtained by integrating informative NMS features into a standard ANN model, especially in inter-subject cases; overall, the hybrid-ANN model predicted joint torque with higher accuracy than the baseline models, most notably in inter-subject prediction after adopting the transfer learning technique. We demonstrated the potential of combining physics-based NMS and standard-ANN models with a transfer learning technique in different prediction scenarios. This procedure holds great promise in applications such as assistance-as-needed exoskeleton control strategy design by incorporating the physiological joint torque of the users.
Details
- Language :
- English
- ISSN :
- 2168-2208
- Volume :
- 26
- Issue :
- 12
- Database :
- MEDLINE
- Journal :
- IEEE journal of biomedical and health informatics
- Publication Type :
- Academic Journal
- Accession number :
- 36112547
- Full Text :
- https://doi.org/10.1109/JBHI.2022.3207313