Abstract
Human joint moment plays an important role in quantitative rehabilitation assessment and exoskeleton robot control. However, the existing moment prediction methods require kinematic and kinetic data of human body as input, and the measurement of them needs special equipment, which makes them unable to be used in an unconstrained environment. According to the situation, this paper develops a novel method where a small number of input variables selected by Elastic Net are used as the input of artificial neural network (ANN) to predict joint moments, which makes the prediction in daily life possible. The method is tested on the experimental data collected from eight healthy subjects that are running on a treadmill at a speed of 2, 3, 4, and 5 m/s, respectively. Taking the right lower limb's 10 electromyography (EMG) and 5 joints angle data as candidate variable sets, Elastic Net is used to obtain the variable coefficients of the right lower limb's four joint moments. The inputs of the ANN determined by the variable coefficients are used to train and predict the joint moments. Prediction accuracy is evaluated by using the normalized root-mean-square error (NRMSE %) and cross correlation coefficient ( >amp;rho>amp; ) between the predicted joint moment and multi-body dynamics moment. Results of our study suggest that the method can accurately predict joint moment (NRMSE < 7.89%, >amp;rho >0.9633$>amp; ) with only 5-6 EMG signals. In conclusion, this method can effectively reduce the input variables while keeping a certain precision, which makes the joint moment prediction simple and out of equipment limitation. This method may facilitate the researches on real-Time gait analysis and exoskeleton robot control in motor rehabilitation.
| Original language | English (US) |
|---|---|
| Article number | 8648489 |
| Pages (from-to) | 29973-29980 |
| Number of pages | 8 |
| Journal | IEEE Access |
| Volume | 7 |
| DOIs | |
| State | Published - 2019 |
Funding
This work was supported in part by the National Nature Science Foundation of China under Grant 61773124, in part by the National Key Research and Development Program of China under Grant 2016YFE0122700, in part by the U.K.-China Industry Academia Partnership Programmer under Grant UK-CIAPP-276, in part by the Science and Technology Project in Fujian Province Education Department under Grant JT180344 and Grant JT180320, in part by the Open Fund of Provincial Key Laboratory of Eco-Industrial Green Technology, Wuyi University, in part by the International Science and Technology Cooperation Project of Fujian Province of China under Grant 2019I0003, and in part by the Scientific Fund Projects in Fujian University of Technology under Grant GY-Z18081, Grant GY-Z17151, and Grant GY-Z17144.
Keywords
- Joint moment prediction
- artificial neural network
- elastic net
ASJC Scopus subject areas
- General Computer Science
- General Materials Science
- General Engineering
