Visual cues to improve myoelectric control of upper limb prostheses

Gigli, Andrea (Department of Computer, Control, and Management Engineering, University of Rome La Sapienza, Italy) ; Gijsberts, Arjan (Department of Computer, Control, and Management Engineering, University of Rome La Sapienza, Italy) ; Gregori, Valentina (Department of Computer, Control, and Management Engineering, University of Rome La Sapienza, Italy) ; Cognolato, Matteo (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Atzori, Manfredo (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Caputo, Barbara (Department of Computer, Control, and Management Engineering, University of Rome La Sapienza, Italy)

The instability of myoelectric signals over time complicates their use to control poly-articulated prosthetic hands. To address this problem, studies have tried to combine surface electromyography with modalities that are less affected by the amputation and the environment, such as accelerometry and gaze information. In the latter case, the hypothesis is that a subject looks at the object he or she intends to manipulate, and that the visual characteristics of that object allow to better predict the desired hand posture. The method we present in this paper automatically detects stable gaze fixations and uses the visual characteristics of the fixated objects to improve the performance of a multimodal grasp classifier. Particularly, the algorithm identifies online the onset of a prehension and the corresponding gaze fixations, obtains high-level feature representations of the fixated objects by means of a Convolutional Neural Network, and combines them with traditional surface electromyography in the classification stage. Tests have been performed on data acquired from five intact subjects who performed ten types of grasps on various objects during both static and functional tasks. The results show that the addition of gaze information increases the grasp classification accuracy, that this improvement is consistent for all grasps and concentrated during the movement onset and offset.


Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Economie/gestion
Publisher:
Enschede, The Netherlands, 26-29 August 2018
Date:
2018-08
Enschede, The Netherlands
26-29 August 2018
Pagination:
8 p.
Published in:
Proceedings of the 7th IEEE International Conference on BioRob2018
DOI:
Appears in Collection:



 Record created 2018-11-05, last modified 2021-01-22


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