Name
Title | ||
|---|---|---|
Bionic control of exoskeleton robot based on motion intention for rehabilitation training |
Abstract | ||
|---|---|---|
As the complexity of obtaining irregular daily motion trajectory during upper limb rehabilitation training, this paper proposes the bionic control method for the presented exoskeleton robot arm based on motion intention. Firstly, the collected motion signal is pre-processing by filtering. Then the motion intention and motion mode of the processed signal are classified by using the hierarchical multi-classification support vector machine. Meanwhile, the adaptive Hopf oscillator network based on dynamic learning is used for offline learning of joint motion trajectory, and the parameters of the reproduced signal in different motion modes are obtained. Finally, the corresponding parameters are transferred according to the user's intention, and the oscillator network is reconstructed to realize the periodic motion control of rehabilitation training. With experimental verification, the proposed method can follow the human body's motion intention and reproduce the daily motion trajectory of the upper limb. The results show it can be used to conduct rehabilitation training for the patient. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1080/01691864.2019.1621774 | ADVANCED ROBOTICS |
Keywords | Field | DocType |
Filtering,motion intention,model classification,adaptive Hopf oscillator | Offline learning,Periodic function,Rehabilitation,Dynamic learning,Computer vision,Control theory,Support vector machine,Filter (signal processing),Artificial intelligence,Engineering,Trajectory,Exoskeleton robot | Journal |
Volume | Issue | ISSN |
33.0 | 12 | 0169-1864 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wendong Wang | 1 | 821 | 72.69 |
| Lei Qin | 2 | 0 | 0.34 |
| Xiaoqing Yuan | 3 | 6 | 3.74 |
| Xing Ming | 4 | 0 | 0.34 |
| Tongsen Sun | 5 | 0 | 0.34 |
| Yifan Liu | 6 | 0 | 0.34 |