Journal article Open Access

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

Albini Alessandro; Giorgio Cannata

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  "DOI": "10.1177/0278364920907688", 
  "language": "eng", 
  "author": [
      "family": "Albini Alessandro"
      "family": "Giorgio Cannata"
  "issued": {
    "date-parts": [
  "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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