Journal article Open Access

# Pressure Distribution Classification and Segmentation of Human Hands in Contact with the Robot Body

Albini Alessandro; Giorgio Cannata

### Citation Style Language JSON Export

{
"DOI": "10.1177/0278364920907688",
"language": "eng",
"author": [
{
"family": "Albini Alessandro"
},
{
"family": "Giorgio Cannata"
}
],
"issued": {
"date-parts": [
[
2020,
3,
10
]
]
},
"abstract": "<p>This paper deals with the problem of the recognition of human hand touch by a robot equipped with large area tactile<br>\nsensors covering its body. This problem is relevant in the domain of physical human-robot interaction for discriminating<br>\nbetween human and non-human contacts and to trigger and to drive cooperative tasks or robot motions, or to ensure a<br>\nsafe interaction. The underlying assumption, used in this paper, is that voluntary physical interaction tasks involve hand<br>\ntouch over the robot body, and therefore the capability of recognizing hand contacts is a key element to discriminate a<br>\npurposive human touch from other types of interaction.<br>\nThe proposed approach is based on a geometric transformation of the tactile data, formed by pressure measurements<br>\nassociated to a non uniform cloud of 3D points (taxels) spread over a non linear manifold corresponding to the robot<br>\nbody, into tactile images representing the contact pressure distribution in 2D. Tactile images can be processed using<br>\ndeep learning algorithms to recognize human hands and to compute the pressure distribution applied by the various<br>\nhand segments: palm and single fingers.<br>\nExperimental results, performed on a real robot covered with robot skin, show the effectiveness of the proposed<br>\nmethodology. Moreover, to evaluate its robustness, various types of failures have been simulated. A further analysis<br>\nconcerning the transferability of the system has been performed, considering contacts occurring on a different<br>\nsensorized robot part.</p>",
"title": "Pressure Distribution Classification and Segmentation of Human Hands in Contact with the Robot Body",
"type": "article-journal",
"id": "3706665"
}
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