Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic Applications
(1) Background: The technologies of Industry 4.0 are increasingly promoting an operation of human motion prediction for improvement of the collaboration between workers and robots. The purposes of this study were to fuse the spatial and inertial data of human upper limbs for typical industrial pick...
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doaj-2d1802ec4c194bd7978cab590e4e968a2020-11-25T02:41:49ZengMDPI AGRobotics2218-65812020-05-019333310.3390/robotics9020033Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic ApplicationsElisa Digo0Mattia Antonelli1Valerio Cornagliotto2Stefano Pastorelli3Laura Gastaldi4Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, ItalyDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, ItalyDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, ItalyDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, ItalyDepartment of Mathematical Sciences “G.L. Lagrange”, Politecnico di Torino, 10129 Turin, Italy(1) Background: The technologies of Industry 4.0 are increasingly promoting an operation of human motion prediction for improvement of the collaboration between workers and robots. The purposes of this study were to fuse the spatial and inertial data of human upper limbs for typical industrial pick and place movements and to analyze the collected features from the future perspective of collaborative robotic applications and human motion prediction algorithms. (2) Methods: Inertial Measurement Units and a stereophotogrammetric system were adopted to track the upper body motion of 10 healthy young subjects performing pick and place operations at three different heights. From the obtained database, 10 features were selected and used to distinguish among pick and place gestures at different heights. Classification performances were evaluated by estimating confusion matrices and F1-scores. (3) Results: Values on matrices diagonals were definitely greater than those in other positions. Furthermore, F1-scores were very high in most cases. (4) Conclusions: Upper arm longitudinal acceleration and markers coordinates of wrists and elbows could be considered representative features of pick and place gestures at different heights, and they are consequently suitable for the definition of a human motion prediction algorithm to be adopted in effective collaborative robotics industrial applications.https://www.mdpi.com/2218-6581/9/2/33IMUstereophotogrammetryupper limbmotion predictionIndustry 4.0sensor fusion |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Elisa Digo Mattia Antonelli Valerio Cornagliotto Stefano Pastorelli Laura Gastaldi |
spellingShingle |
Elisa Digo Mattia Antonelli Valerio Cornagliotto Stefano Pastorelli Laura Gastaldi Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic Applications Robotics IMU stereophotogrammetry upper limb motion prediction Industry 4.0 sensor fusion |
author_facet |
Elisa Digo Mattia Antonelli Valerio Cornagliotto Stefano Pastorelli Laura Gastaldi |
author_sort |
Elisa Digo |
title |
Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic Applications |
title_short |
Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic Applications |
title_full |
Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic Applications |
title_fullStr |
Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic Applications |
title_full_unstemmed |
Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic Applications |
title_sort |
collection and analysis of human upper limbs motion features for collaborative robotic applications |
publisher |
MDPI AG |
series |
Robotics |
issn |
2218-6581 |
publishDate |
2020-05-01 |
description |
(1) Background: The technologies of Industry 4.0 are increasingly promoting an operation of human motion prediction for improvement of the collaboration between workers and robots. The purposes of this study were to fuse the spatial and inertial data of human upper limbs for typical industrial pick and place movements and to analyze the collected features from the future perspective of collaborative robotic applications and human motion prediction algorithms. (2) Methods: Inertial Measurement Units and a stereophotogrammetric system were adopted to track the upper body motion of 10 healthy young subjects performing pick and place operations at three different heights. From the obtained database, 10 features were selected and used to distinguish among pick and place gestures at different heights. Classification performances were evaluated by estimating confusion matrices and F1-scores. (3) Results: Values on matrices diagonals were definitely greater than those in other positions. Furthermore, F1-scores were very high in most cases. (4) Conclusions: Upper arm longitudinal acceleration and markers coordinates of wrists and elbows could be considered representative features of pick and place gestures at different heights, and they are consequently suitable for the definition of a human motion prediction algorithm to be adopted in effective collaborative robotics industrial applications. |
topic |
IMU stereophotogrammetry upper limb motion prediction Industry 4.0 sensor fusion |
url |
https://www.mdpi.com/2218-6581/9/2/33 |
work_keys_str_mv |
AT elisadigo collectionandanalysisofhumanupperlimbsmotionfeaturesforcollaborativeroboticapplications AT mattiaantonelli collectionandanalysisofhumanupperlimbsmotionfeaturesforcollaborativeroboticapplications AT valeriocornagliotto collectionandanalysisofhumanupperlimbsmotionfeaturesforcollaborativeroboticapplications AT stefanopastorelli collectionandanalysisofhumanupperlimbsmotionfeaturesforcollaborativeroboticapplications AT lauragastaldi collectionandanalysisofhumanupperlimbsmotionfeaturesforcollaborativeroboticapplications |
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