Abstract
Introduction: Human joint moment is a critical parameter to rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network (ANN) model. However, challenge remains as lack of an effective approach to determining the input variables for the ANN model in joint moment prediction, which determines the number of input sensors and the complexity of prediction. Methods: To address this research gap, this study develops a mathematical model based on the Hill muscle model to determining the online input variables of the ANN for the prediction of joint moments. In this method, the muscle activation, muscle-tendon moment velocity and length in the Hill muscle model and muscle-tendon moment arm are translated to the online measurable variables, i.e. muscle electromyography (EMG), joint angles and angular velocities of the muscle span. To test the predictive ability of these input variables, an ANN model is designed and trained to predict joint moments. The ANN model with the online measurable input variables is tested on the experimental data collected from ten healthy subjects running with the speeds of 2, 3, 4 and 5 m/s on a treadmill. The variance accounted for (VAF) between the predicted and inverse dynamics moment is used to evaluate the prediction accuracy. Results: The results suggested that the method can predict joint moments with a higher accuracy (mean VAF = 89.67±5.56 %) than those obtained by using other joint angles and angular velocities as inputs (mean VAF = 86.27±6.6%) evaluated by jack-knife cross-validation. Conclusions: The proposed method provides us with a powerful tool to predict joint moment based on online measurable variables, which establishes the theoretical basis for optimizing the input sensors and detection complexity of the prediction system. It may facilitate the research on exoskeleton robot control and real-time gait analysis in motor rehabilitation.
Original language | English (US) |
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Article number | 1185 |
Journal | Sensors (Switzerland) |
Volume | 20 |
Issue number | 4 |
DOIs | |
State | Published - Feb 2 2020 |
Funding
Acknowledgments: This work was supported in part by in part by National Nature Science Foundation of China (61773124, 61773415), in part by National Key Research and Development Program of China (2016YFE0122700), in part by UK‐China Industry Academia Partnership Programmer\276, the Science and Tecohnology Project in Fujian Province Education Department (JT180344/ JT180320/JAT170398), and in part by the Scientific Fund Projects in Fujian University of Technology (GY‐Z17151/GY‐Z17144). Y.Y. is supported by the Dixon Translational Research Grants Initiative from the Northwestern Memorial Foundation. This work was supported in part by in part by National Nature Science Foundation of China (61773124, 61773415), in part by National Key Research and Development Program of China (2016YFE0122700), in part by UK-China Industry Academia Partnership Programmer\276, the Science and Tecohnology Project in Fujian Province Education Department (JT180344/ JT180320/JAT170398), and in part by the Scientific Fund Projects in Fujian University of Technology (GY-Z17151/GY-Z17144). Y.Y. is supported by the Dixon Translational Research Grants Initiative from the Northwestern Memorial Foundation.
Keywords
- Artificial neural network
- Extreme learning machine
- Hill muscle model
- Joint moment prediction
- Online input variables
ASJC Scopus subject areas
- Analytical Chemistry
- Information Systems
- Atomic and Molecular Physics, and Optics
- Biochemistry
- Instrumentation
- Electrical and Electronic Engineering