Motivation: Modeling human grasping and hand movements is important for robotics, prosthetics and rehabilitation. Several qualitative taxonomies of hand grasps have been proposed in scientific literature. However it is not clear how well they correspond to subjects movements. Objective: In this work we quantitatively analyze the similarity between hand movements in 40 subjects using different features. Methods: Publicly available data from 40 healthy subjects were used for this study. The data include electromyography and kinematic data recorded while the subjects perform 20 hand grasps. The kinematic and myoelectric signal was windowed and several signal features were extracted. Then, for each subject, a set of hierarchical trees was computed for the hand grasps. The obtained results were compared in order to evaluate differences between features and different subjects. Results: The comparison of the signal feature taxonomies revealed a relation among the same subject. The comparison of the subject taxonomies highlighted also a similarity shared between subjects except for rare cases. Conclusions: The results suggest that quantitative hierarchical representations of hand movements can be performed with the proposed approach and the results from different subjects and features can be compared. The presented approach suggests a way to perform a systematic analysis of hand movements and to create a quantitative taxonomy of hand movements.

Quantitative hierarchical representation and comparison of hand grasps from electromyography and kinematic data

Francesca Stival;Stefano Michieletto;Enrico Pagello;Manfredo Atzori
2018

Abstract

Motivation: Modeling human grasping and hand movements is important for robotics, prosthetics and rehabilitation. Several qualitative taxonomies of hand grasps have been proposed in scientific literature. However it is not clear how well they correspond to subjects movements. Objective: In this work we quantitatively analyze the similarity between hand movements in 40 subjects using different features. Methods: Publicly available data from 40 healthy subjects were used for this study. The data include electromyography and kinematic data recorded while the subjects perform 20 hand grasps. The kinematic and myoelectric signal was windowed and several signal features were extracted. Then, for each subject, a set of hierarchical trees was computed for the hand grasps. The obtained results were compared in order to evaluate differences between features and different subjects. Results: The comparison of the signal feature taxonomies revealed a relation among the same subject. The comparison of the subject taxonomies highlighted also a similarity shared between subjects except for rare cases. Conclusions: The results suggest that quantitative hierarchical representations of hand movements can be performed with the proposed approach and the results from different subjects and features can be compared. The presented approach suggests a way to perform a systematic analysis of hand movements and to create a quantitative taxonomy of hand movements.
Workshop Proceedings of the 15th International Conference on Autonomous Systems IAS-15, Workshop on Learning Applications for Intelligent Autonomous Robots (LAIAR-2018)
978-3-00-059946-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3283482
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