A large calibrated database of hand movements and grasps kinematics

Néstor J Jarque-Bou,Manfredo Atzori,Henning Müller

doi:10.1038/s41597-019-0349-2

Néstor J Jarque-Bou, Manfredo Atzori + Show 1 more

Open Access

https://doi.org/10.1038/s41597-019-0349-2

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Abstract

Modelling hand kinematics is a challenging problem, crucial for several domains including robotics, 3D modelling, rehabilitation medicine and neuroscience. Currently available datasets are few and limited in the number of subjects and movements. The objective of this work is to advance the modelling of hand kinematics by releasing and validating a large publicly available kinematic dataset of hand movements and grasp kinematics. The dataset is based on the harmonization and calibration of the kinematics data of three multimodal datasets previously released (Ninapro DB1, DB2 and DB5, that include electromyography, inertial and dynamic data). The novelty of the dataset is related to the high number of subjects (77) and movements (40 movements, each repeated several times) for which we release for the first time calibrated kinematic data, resulting in the largest available kinematic dataset. Differently from the previous datasets, the data are also calibrated to avoid sensor nonlinearities. The validation confirms that the data are not affected by experimental procedures and that they are similar to data acquired in real-life conditions.

Highlights

Background & SummaryThe hand is a complex functional limb including over 30 muscles and more than 20 joints that allow performing a wide range of activities with a high level of precision
The analysis of complex hand movements is useful for several applications, including robotics3,4, 3D modelling[5], rehabilitation and physiotherapy[6,7,8], bioengineering, medicine and neuroscience
Studies are improving the understanding of hand kinematics[11,12,13,14,15], scientific research in this field is still often affected by several limitations

Summary

Introduction

Background & SummaryThe hand is a complex functional limb including over 30 muscles and more than 20 joints that allow performing a wide range of activities with a high level of precision. The analysis of complex hand movements is useful for several applications, including robotics[3,4] (to improve grasping by manipulators), 3D modelling[5] (to develop more realistic models of the hand for movies or computer games), rehabilitation and physiotherapy[6,7,8] (to improve hand rehabilitation), bioengineering, medicine and neuroscience (to better understand human hand movements, in relationship to muscular and kinematic synergies[9,10]). Most studies are based on raw instrumented glove data, which do not provide the linear outputs required to obtain reliable joint angles and can invalidate kinematic models obtained without a specific and accurate calibration method[16,17]

Objectives

Methods