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Motion Trajectories Prediction of Lower Limb Exoskeleton Based on Long Short-Term Memory (LSTM) Networks
| Content Provider | MDPI |
|---|---|
| Author | Ren, Bin Zhang, Zhiqiang Zhang, Chi Chen, Silu |
| Copyright Year | 2022 |
| Description | A typical man–machine coupling system could provide the wearer a coordinated and assisted movement by the lower limb exoskeleton. The process of cooperative movement relies on the accurate perception of the wearer’s human movement information and the accurate planning and control of the joint movement of the lower limb exoskeleton. In this paper, a neural network and a Long-Short Term Memory (LSTM) machine learning model method is proposed to predict the actual movement trajectory of the human body’s lower limbs. Then a wearable joint angle measurement device was designed for gait trajectory prediction, which can be used for predictive control through machine learning methods. The experimental results show that the LSTM model can accurately predict the gait trajectory with an average mean square error. This method has practical significance for prediction the trajectory of the lower limb exoskeleton. |
| Starting Page | 73 |
| e-ISSN | 20760825 |
| DOI | 10.3390/act11030073 |
| Journal | Actuators |
| Issue Number | 3 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-26 |
| Access Restriction | Open |
| Subject Keyword | Actuators Industrial Engineering Lower Limb Exoskeleton Gait Trajectory Prediction Long Short-term Memory (lstm) Wearable Measurement Device |
| Content Type | Text |
| Resource Type | Article |
