Reaching away and toward the body is one of the most im- portant upper-limb task in daily-living activities. Several robotic tech- nologies have been developed to assist neurologically impaired people with motor disabilities, such as exoskeletons and teleoperated manip- ulators. However, a high level of disability and muscle weakness could prevent an eective identication of user intention. In this paper, we present a novel approach for the classication of four reaching directions in the early phase of movement. A dimensionality-reduction algorithm based on the extraction of muscle synergies is coupled to a Gaussian Mixture Model in an evidence-accumulation framework. On average, the system identies the desired direction with 82% of accuracy at movement onset, up to 98% at 20% of reaching distance. We believe the proposed method to improve the robustness of myoelectric controlled devices, both for rehabilitation and functional assistance.

Synergy-based Gaussian Mixture Model to anticipate reaching direction identification for robotic applications

Stefano Tortora
Writing – Original Draft Preparation
;
Stefano Michieletto
Methodology
;
Emanuele Menegatti
Supervision
2018

Abstract

Reaching away and toward the body is one of the most im- portant upper-limb task in daily-living activities. Several robotic tech- nologies have been developed to assist neurologically impaired people with motor disabilities, such as exoskeletons and teleoperated manip- ulators. However, a high level of disability and muscle weakness could prevent an eective identication of user intention. In this paper, we present a novel approach for the classication of four reaching directions in the early phase of movement. A dimensionality-reduction algorithm based on the extraction of muscle synergies is coupled to a Gaussian Mixture Model in an evidence-accumulation framework. On average, the system identies the desired direction with 82% of accuracy at movement onset, up to 98% at 20% of reaching distance. We believe the proposed method to improve the robustness of myoelectric controlled devices, both for rehabilitation and functional assistance.
Workshop Proceedings of the 15th International Conference on Intelligent Autonomous Systems
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/11577/3297581
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