Name
Abstract | ||
|---|---|---|
To adapt to different locomotion modes or terrains, real-time human intents recognition is an essential skill to the control of lower-limb exoskeletons timely and precisely. In this paper, we propose a real-time on-board training and recognition method to identify locomotion-related activities for an active pelvis orthosis using two IMUs integrated into it. The designed on-board intent recognition system with a BPNN based algorithm realizes distinguish among six locomotion modes including standing, level ground walking, ramp ascending, ramp descending, stair ascending and stair descending, and deliver the recognition results for future control strategies. Experiments are conducted on one healthy subject including on-board training and online recognition parts. The overall recognition accuracy is 97.79% with the cost time of one recognition decision is about 0.9ms, which is sufficient short compared with the sample interval of 10ms. The experimental results validate the great performance of the proposed real-time on-board training and recognition method for future control of the lower-limb exoskeletons assisting in various locomotion modes or terrains. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/HUMANOIDS.2018.8625044 | 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids) |
Keywords | Field | DocType |
on-board recognition,active pelvis orthosis,real-time human intents recognition,lower-limb exoskeletons,on-board training,locomotion-related activities,on-board intent recognition system,BPNN based algorithm,locomotion modes including standing | Recognition system,Simulation,Computer science,Exoskeleton | Conference |
ISSN | ISBN | Citations |
2164-0572 | 978-1-5386-7284-6 | 0 |
PageRank | References | Authors |
0.34 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Gong | 1 | 0 | 0.34 |
| Dongfang Xu | 2 | 1 | 3.79 |
| Zhihao Zhou | 3 | 27 | 7.98 |
| Nicola Vitiello | 4 | 365 | 42.86 |
| Qining Wang | 5 | 167 | 39.64 |