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A survey of sensor fusion methods in wearable robotics


Novak, Domen; Riener, Robert (2015). A survey of sensor fusion methods in wearable robotics. Robotics and Autonomous Systems, 73:155-170.

Abstract

Modern wearable robots are not yet intelligent enough to fully satisfy the demands of end-users, as they lack the sensor fusion algorithms needed to provide optimal assistance and react quickly to perturbations or changes in user intentions. Sensor fusion applications such as intention detection have been emphasized as a major challenge for both robotic orthoses and prostheses. In order to better examine the strengths and shortcomings of the field, this paper presents a review of existing sensor fusion methods for wearable robots, both stationary ones such as rehabilitation exoskeletons and portable ones such as active prostheses and full-body exoskeletons. Fusion methods are first presented as applied to individual sensing modalities (primarily electromyography, electroencephalography and mechanical sensors), and then four approaches to combining multiple modalities are presented. The strengths and weaknesses of the different methods are compared, and recommendations are made for future sensor fusion research.

Abstract

Modern wearable robots are not yet intelligent enough to fully satisfy the demands of end-users, as they lack the sensor fusion algorithms needed to provide optimal assistance and react quickly to perturbations or changes in user intentions. Sensor fusion applications such as intention detection have been emphasized as a major challenge for both robotic orthoses and prostheses. In order to better examine the strengths and shortcomings of the field, this paper presents a review of existing sensor fusion methods for wearable robots, both stationary ones such as rehabilitation exoskeletons and portable ones such as active prostheses and full-body exoskeletons. Fusion methods are first presented as applied to individual sensing modalities (primarily electromyography, electroencephalography and mechanical sensors), and then four approaches to combining multiple modalities are presented. The strengths and weaknesses of the different methods are compared, and recommendations are made for future sensor fusion research.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Balgrist University Hospital, Swiss Spinal Cord Injury Center
Dewey Decimal Classification:610 Medicine & health
Uncontrolled Keywords:Sensor fusion; Assistive robotics; Rehabilitation robotics; Classification; Electromyography; Electroencephalography
Language:English
Date:2015
Deposited On:05 Dec 2014 14:26
Last Modified:05 Apr 2016 18:34
Publisher:Elsevier
ISSN:0921-8890
Publisher DOI:https://doi.org/10.1016/j.robot.2014.08.012

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