0000000000843479

AUTHOR

Floriant Labarrière

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Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review

2020

Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven controllers on locomotion assistive devices that recognize/predict the current locomotion mode or the upcoming one. This review synthesizes the machine learning algorithms designed to recognize or to predict a locomotion mode in order to automatically adapt the behavior of a locomotion assistive device. A systematic review was conducted on the Web of Science and MEDLINE databases (as well as in the retrieved papers) to identify articles published…

0209 industrial biotechnologyComputer science0206 medical engineeringWalkingReview02 engineering and technologyMachine learningcomputer.software_genrelcsh:Chemical technologyBiochemistryField (computer science)Analytical ChemistryActivity recognition020901 industrial engineering & automationMode (computer interface)Robustness (computer science)Humansassistive deviceslcsh:TP1-1185Electrical and Electronic EngineeringInstrumentationbusiness.industryembedded sensorsSelf-Help Devices020601 biomedical engineeringAtomic and Molecular Physics and Opticslocomotionmachine learningArtificial intelligencebusinesscomputerAlgorithmsSensors
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