Baoping Xiong1,2, Nianyin Zeng3, Yurong Li4, Min Du1,5, Meilan Huang1, Wuxiang Shi1, Guoju Mao2, Yuan Yang6. 1. College of Physics and Information Engineering, Fuzhou University, Fuzhou City 350116, Fujian Province, China. 2. Department of Mathematics and Physics, Fujian University of Technology, Fuzhou City 350118, Fujian Province, China. 3. Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China. 4. Fujian Key Laboratory of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou City 350116, Fujian Province, China. 5. Fujian provincial key laboratory of eco-industrial green technology, Wuyi University, Wuyishan City 354300, Fujian Province, China. 6. Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL 60208, USA.
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.
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.
Entities:
Keywords:
Hill muscle model; artificial neural network; extreme learning machine; joint moment prediction; online input variables
Authors: Benjamin J Fregly; Thor F Besier; David G Lloyd; Scott L Delp; Scott A Banks; Marcus G Pandy; Darryl D D'Lima Journal: J Orthop Res Date: 2011-12-12 Impact factor: 3.494
Authors: Wuxiang Shi; Yurong Li; Dujian Xu; Chen Lin; Junlin Lan; Yuanbo Zhou; Qian Zhang; Baoping Xiong; Min Du Journal: Front Public Health Date: 2021-04-16