: HANDdata is a dataset designed to provide hand kinematics and proximity vision data during reach to grasp actions of non-virtual objects, specifically tailored for autonomous grasping of a robotic hand, and with particular attention to the reaching phase. Thus, we sought to capture target object characteristics from radar and time-of-flight proximity sensors, as well as details of the reach-to-grasp action by looking at wrist and fingers kinematics, and at hand-object interaction main events. We structured the data collection as a sequence of static and grasping tasks, organized by increasing levels of complexity. HANDdata is a first-person, reach-to-grasp dataset that includes almost 6000 human-object interactions from 29 healthy adults, with 10 standardized objects of 5 different shapes and 2 kinds of materials. We believe that such data collection can be of value for researchers interested in autonomous grasping robots for healthcare and industrial applications, as well as for those interested in radar-based computer vision and in basic aspects of sensorimotor control and manipulation.

HANDdata - first-person dataset including proximity and kinematics measurements from reach-to-grasp actions

Mastinu, Enzo
;
Cipriani, Christian
2023-01-01

Abstract

: HANDdata is a dataset designed to provide hand kinematics and proximity vision data during reach to grasp actions of non-virtual objects, specifically tailored for autonomous grasping of a robotic hand, and with particular attention to the reaching phase. Thus, we sought to capture target object characteristics from radar and time-of-flight proximity sensors, as well as details of the reach-to-grasp action by looking at wrist and fingers kinematics, and at hand-object interaction main events. We structured the data collection as a sequence of static and grasping tasks, organized by increasing levels of complexity. HANDdata is a first-person, reach-to-grasp dataset that includes almost 6000 human-object interactions from 29 healthy adults, with 10 standardized objects of 5 different shapes and 2 kinds of materials. We believe that such data collection can be of value for researchers interested in autonomous grasping robots for healthcare and industrial applications, as well as for those interested in radar-based computer vision and in basic aspects of sensorimotor control and manipulation.
2023
File in questo prodotto:
File Dimensione Formato  
Mastinu2023 - HANDdata dataset.pdf

accesso aperto

Tipologia: Documento in Post-print/Accepted manuscript
Licenza: Copyright dell'editore
Dimensione 1.88 MB
Formato Adobe PDF
1.88 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11382/557154
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
social impact