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dc.contributor.authorStroh, Ashley
dc.contributor.authorDesai, Jaydip M.
dc.date.accessioned2021-10-22T18:46:45Z
dc.date.available2021-10-22T18:46:45Z
dc.date.issued2021-10-06
dc.identifier.citationStroh, A., & Desai, J. (2021). Hand gesture-based artificial neural network trained hybrid Human–machine interface system to navigate a powered wheelchair. Journal of Bionic Engineering, doi:10.1007/s42235-021-00074-zen_US
dc.identifier.issn1672-6529
dc.identifier.issn2543-2141
dc.identifier.urihttps://link.springer.com/article/10.1007/s42235-021-00074-z
dc.identifier.urihttps://soar.wichita.edu/handle/10057/22245
dc.descriptionClick on the URL link to access the article (may not be free).en_US
dc.description.abstractIndividuals with cerebral palsy and muscular dystrophy often lack fine motor control of their fingers which makes it difficult to control traditional powered wheelchairs using a joystick. Studies have shown the use of surface electromyography to steer powered wheelchairs or automobiles either through simulations or gaming controllers. However, these studies significantly lack issues with real world scenarios such as user’s safety, real-time control, and efficiency of the controller mechanism. The purpose of this study was to design, evaluate, and implement a hybrid human–machine interface system for a powered wheelchair that can detect human intent based on artificial neural network trained hand gesture recognition and navigate a powered wheelchair without colliding with objects around the path. Scaled Conjugate Gradient (SCG), Bayesian Regularization (BR), and Levenberg Marquart (LM) supervised artificial neural networks were trained in offline testing on eight participants without disability followed by online testing using the classifier with highest accuracy. Bayesian Regularization architecture showed highest accuracy at 98.4% across all participants and hidden layers. All participants successfully completed the path in an average of 5 min and 50 s, touching an average of 22.1% of the obstacles. The proposed hybrid system can be implemented to assist people with neuromuscular disabilities in near future.en_US
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofseriesJournal of Bionic Engineering;
dc.subjectElectromyographyen_US
dc.subjectArtificial neural networken_US
dc.subjectHybrid controlen_US
dc.subjectPowered wheelchairen_US
dc.subjectAssistive technologyen_US
dc.subjectHand gesture recognitionen_US
dc.titleHand gesture-based artificial neural network trained hybrid human–machine interface system to navigate a powered wheelchairen_US
dc.typeArticleen_US
dc.rights.holderCopyright © 2021, Jilin University 2021en_US


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