Motion analysis for identification of overused body segments: the packaging task in industry 4.0

Brenda Elizabeth Olivas Padilla (1, 2) , Alina Glushkova (1, 2) , Sotiris Manitsaris (1, 2)

1 Mines Paris - PSL (École nationale supérieure des mines de Paris)
2 CAOR - Centre de Robotique

This work presents a statistical analysis of professional gestures from household appliances manufacturing. The goal is to investigate the hypothesis that some body segments are more involved than others in professional gestures and present thus higher ergonomic risk. The gestures were recorded with a full body Inertial Measurement Unit (IMU) suit and represented with rotations of each segment. Data dimensions have been reduced with principal component analysis (PCA), permitting us to reveal hidden correlations between the body segments and to extract the ones with the highest variance. This work aims at detecting among numerous upper body segments, which are the ones that are overused and consequently, which is the minimum number of segments that is sufficient to represent our dataset for ergonomic analysis. To validate the results, Hidden Markov Models (HMMs) based recognition method has been used and trained only with the segments from the PCA. The recognition accuracy of 95.71% was achieved confirming this hypothesis.

Mots clés: Motion analysis; gesture recognition; PCA; ergonomics

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]; Interface homme-machine [cs.HC]

Brenda Elizabeth Olivas Padilla, Alina Glushkova, Sotiris Manitsaris. Motion analysis for identification of overused body segments: the packaging task in industry 4.0. 17th IFIP TC.13 International Conference on Human-Computer Interaction, Sep 2019, Paphos, Cyprus. pp.349-354, ⟨10.18573/book3.as⟩. ⟨hal-03530164⟩