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| Content Provider | Springer Nature Link |
|---|---|
| Author | Silvera Tawil, David Rye, David Velonaki, Mari |
| Copyright Year | 2014 |
| Abstract | During social interaction humans extract important information from tactile stimuli that improves their understanding of the interaction. The development of a similar capacity in a robot will contribute to the future success of intuitive human–robot interactions. This paper presents experiments on the classification of social touch on a full-sized mannequin arm covered with touch-sensitive artificial skin. The flexible and stretchable sensitive skin was implemented using electrical impedance tomography. A classifier based on the LogitBoost algorithm was used to classify six emotions and six social messages transmitted by humans when touching the artificial arm. Experimental results show that classification of social touch can be achieved with accuracies comparable to those achieved by humans. |
| Starting Page | 489 |
| Ending Page | 505 |
| Page Count | 17 |
| File Format | |
| ISSN | 18754791 |
| Journal | International Journal of Social Robotics |
| Volume Number | 6 |
| Issue Number | 4 |
| e-ISSN | 18754805 |
| Language | English |
| Publisher | Springer Netherlands |
| Publisher Date | 2014-01-14 |
| Publisher Place | Dordrecht |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Social touch Human–robot interaction (HRI) Social robotics Supervised machine learning LogitBoost Artificial sensitive skin Electrical impedance tomography Control, Robotics, Mechatronics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Philosophy Control and Systems Engineering Social Psychology Electrical and Electronic Engineering Human-Computer Interaction |
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