Medical & Biological Engineering & Computing

, Volume 55, Issue 12, pp 2197–2208 | Cite as

A stochastic algorithm for automatic hand pose and motion estimation

  • Francesca Cordella
  • Francesco Di Corato
  • Bruno Siciliano
  • Loredana Zollo
Original Article


In this paper, a novel, robust, and simple method for automatically estimating the hand pose is proposed and validated. The method uses a multi-camera optoelectronic system and a model-based stochastic algorithm. The approach is marker-based and relies on an Unscented Kalman Filter. A hand kinematic model is introduced for constraining relative marker’s positions and improving the algorithm robustness with respect to outliers and possible occlusions. The algorithm outputs are 3D coordinate measures of markers and hand joint angle values. To validate the proposed algorithm, a comparison with ground truths for angular and 3D coordinate measures is carried out. The comparative analysis shows the advantages of using the model-based stochastic algorithm with respect to standard processing software of optoelectronic cameras in terms of implementation simplicity, time consumption, and user effort. The accuracy is remarkable, with a difference of maximum 0.035r a d and 4m m with respect to angular and 3D Cartesian coordinates ground truths, respectively.


Hand pose estimation Unscented Kalman filter Optoelectronic cameras Hand motion analysis 



This work was supported partly by the Italian Institute for Labour Accidents (INAIL) with PPR 2 project (CUP: E58C13000990001) and partly by the European Project H2020/AIDE: Multimodal and Natural computer interaction Adaptive Multimodal Interfaces to Assist Disabled People in Daily Activities (CUP J42I15000030006).


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Copyright information

© International Federation for Medical and Biological Engineering 2017

Authors and Affiliations

  • Francesca Cordella
    • 1
  • Francesco Di Corato
    • 3
  • Bruno Siciliano
    • 2
  • Loredana Zollo
    • 1
  1. 1.Unit of Biomedical Robotics and BiomicrosystemsUniversità Campus Bio-Medico di RomaRomeItaly
  2. 2.PRISMA Lab, Department of Electrical Engineering and Information TechnologyUniversità di Napoli Federico IINaplesItaly
  3. 3.Research Center “E. Piaggio”PisaItaly

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