Journal article
Ankle Joint Torque Prediction Using an NMS Solver Informed-ANN Model and Transfer Learning
IEEE journal of biomedical and health informatics, Vol.26(12), pp.5895-5906
09/16/2022
DOI: 10.1109/JBHI.2022.3207313
PMID: 36112547
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
- Title: Subtitle
- Ankle Joint Torque Prediction Using an NMS Solver Informed-ANN Model and Transfer Learning
- Creators
- Longbin ZhangXueyu ZhuElena M Gutierrez FarewikRuoli Wang
- Resource Type
- Journal article
- Publication Details
- IEEE journal of biomedical and health informatics, Vol.26(12), pp.5895-5906
- DOI
- 10.1109/JBHI.2022.3207313
- PMID
- 36112547
- NLM abbreviation
- IEEE J Biomed Health Inform
- ISSN
- 2168-2194
- eISSN
- 2168-2208
- Grant note
- DOI: 10.13039/100009389, name: Stiftelsen Promobilia, award: 21302, 19302, A22078, 18202, 18014, 18200; DOI: 10.13039/501100004359, name: Vetenskapsrådet, award: 2018-04902, 2018-00750; DOI: 10.13039/100000893, name: Simons Foundation, award: 504054
- Language
- English
- Date published
- 09/16/2022
- Academic Unit
- Mathematics
- Record Identifier
- 9984297558202771
Metrics
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