Name
Abstract | ||
|---|---|---|
In a human robot interaction scenario, predicting the human motion intention is essential for avoiding inconvenient delays and for a smooth reactivity of the robotic system. In particular, when dealing with hand prosthetic devices, an early estimation of the final hand gesture is crucial for a smooth control of the robotic hand. In this work we develop an electromyographic (EMG) based learning approach that decodes the grasping intention at an early stage of the reaching to grasping motion, i.e before the final grasp/hand preshape takes place. EMG electrodes are used for recording the arm muscles activities and a cyberglove is used to measure the finger joints during the textit{reach and} textit{grasp} motion. Results show that we can correctly classify with $90%$ accuracy for three typical grasps textit{before the onset of the hand pre-shape}. Such an early detection of the grasp intention allows to control a robotic hand simultaneously to the motion of subject's arm, hence generating no delay between the natural arm motion and the artificial hand motion. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2701973.2701997 | HRI (Extended Abstracts) |
Keywords | Field | DocType |
applications,user/machine systems,hand preshape,human grasp,electromyography,learning,echo state networks | Robotic systems,Computer vision,Upper limb,GRASP,Robotic hand,Computer science,Simulation,Gesture,Electromyography,Human motion,Artificial intelligence,Human–robot interaction | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iason Batzianoulis | 1 | 4 | 1.44 |
| S. El-Khoury | 2 | 143 | 7.92 |
| Silvestro Micera | 3 | 100 | 25.68 |
| Aude Billard | 4 | 3316 | 254.98 |