4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"

Research Article

Exploiting hand kinematic synergies and wearable under-sensing for hand functional grasp recognition

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  • @INPROCEEDINGS{10.4108/icst.mobihealth.2014.257228,
        author={Matteo Bianchi and Nicola Carbonaro and Edoardo Battaglia and Federico Lorussi and Antonio Bicchi and Danilo De Rossi and Alessandro Tognetti},
        title={Exploiting hand kinematic synergies and wearable under-sensing for hand functional grasp recognition},
        proceedings={4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"},
        publisher={IEEE},
        proceedings_a={MOBIHEALTH},
        year={2014},
        month={12},
        keywords={wearable sensing hand kinematic synergies grasp recognition},
        doi={10.4108/icst.mobihealth.2014.257228}
    }
    
  • Matteo Bianchi
    Nicola Carbonaro
    Edoardo Battaglia
    Federico Lorussi
    Antonio Bicchi
    Danilo De Rossi
    Alessandro Tognetti
    Year: 2014
    Exploiting hand kinematic synergies and wearable under-sensing for hand functional grasp recognition
    MOBIHEALTH
    IEEE
    DOI: 10.4108/icst.mobihealth.2014.257228
Matteo Bianchi1, Nicola Carbonaro1, Edoardo Battaglia1, Federico Lorussi1, Antonio Bicchi1, Danilo De Rossi1, Alessandro Tognetti1,*
  • 1: Research Center E.Piaggio - University of Pisa
*Contact email: a.tognetti@centropiaggio.unipi.it

Abstract

Wearable sensing represents an effective manner to correctly recognize hand functional grasps. The need of wearability is strictly related to the minimization of the number of sensors, in order to avoid cumbersome and hence obtrusive systems. In this paper we present a wearable glove, which is able to provide accurate measurements from three joint angles. These measurements are then completed to reconstruct the whole hand posture, by exploiting a priori synergistic information on how human commonly shape their hands in grasping tasks. Results, although preliminary, show the effectiveness of the here described devices and methods and encourage to further investigate this kind of approach.