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An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation

Elderly and disabled population is rapidly increasing. It is important to uplift their living standards by improving the confidence towards daily activities. Navigation is an important task, most elderly and disabled people need assistance with. Replacing human assistance with an intelligent system...

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Detalles Bibliográficos
Autores principales: Bandara, H. M. Ravindu T., Priyanayana, K. S., Jayasekara, A. G. Buddhika P., Chandima, D. P., Gopura, R. A. R. C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204291/
https://www.ncbi.nlm.nih.gov/pubmed/32399060
http://dx.doi.org/10.1155/2020/9160528
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author Bandara, H. M. Ravindu T.
Priyanayana, K. S.
Jayasekara, A. G. Buddhika P.
Chandima, D. P.
Gopura, R. A. R. C.
author_facet Bandara, H. M. Ravindu T.
Priyanayana, K. S.
Jayasekara, A. G. Buddhika P.
Chandima, D. P.
Gopura, R. A. R. C.
author_sort Bandara, H. M. Ravindu T.
collection PubMed
description Elderly and disabled population is rapidly increasing. It is important to uplift their living standards by improving the confidence towards daily activities. Navigation is an important task, most elderly and disabled people need assistance with. Replacing human assistance with an intelligent system which is capable of assisting human navigation via wheelchair systems is an effective solution. Hand gestures are often used in navigation systems. However, those systems do not possess the capability to accurately identify gesture variances. Therefore, this paper proposes a method to create an intelligent gesture classification system with a gesture model which was built based on human studies for every essential motion in domestic navigation with hand gesture variance compensation capability. Experiments have been carried out to evaluate user remembering and recalling capability and adaptability towards the gesture model. Dynamic Gesture Identification Module (DGIM), Static Gesture Identification Module (SGIM), and Gesture Clarifier (GC) have been introduced in order to identify gesture commands. The proposed system was analyzed for system accuracy and precision using results of the experiments conducted with human users. Accuracy of the intelligent system was determined with the use of confusion matrix. Further, those results were analyzed using Cohen's kappa analysis in which overall accuracy, misclassification rate, precision, and Cohen's kappa values were calculated.
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spelling pubmed-72042912020-05-12 An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation Bandara, H. M. Ravindu T. Priyanayana, K. S. Jayasekara, A. G. Buddhika P. Chandima, D. P. Gopura, R. A. R. C. Appl Bionics Biomech Research Article Elderly and disabled population is rapidly increasing. It is important to uplift their living standards by improving the confidence towards daily activities. Navigation is an important task, most elderly and disabled people need assistance with. Replacing human assistance with an intelligent system which is capable of assisting human navigation via wheelchair systems is an effective solution. Hand gestures are often used in navigation systems. However, those systems do not possess the capability to accurately identify gesture variances. Therefore, this paper proposes a method to create an intelligent gesture classification system with a gesture model which was built based on human studies for every essential motion in domestic navigation with hand gesture variance compensation capability. Experiments have been carried out to evaluate user remembering and recalling capability and adaptability towards the gesture model. Dynamic Gesture Identification Module (DGIM), Static Gesture Identification Module (SGIM), and Gesture Clarifier (GC) have been introduced in order to identify gesture commands. The proposed system was analyzed for system accuracy and precision using results of the experiments conducted with human users. Accuracy of the intelligent system was determined with the use of confusion matrix. Further, those results were analyzed using Cohen's kappa analysis in which overall accuracy, misclassification rate, precision, and Cohen's kappa values were calculated. Hindawi 2020-01-30 /pmc/articles/PMC7204291/ /pubmed/32399060 http://dx.doi.org/10.1155/2020/9160528 Text en Copyright © 2020 H. M. Ravindu T. Bandara et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Bandara, H. M. Ravindu T.
Priyanayana, K. S.
Jayasekara, A. G. Buddhika P.
Chandima, D. P.
Gopura, R. A. R. C.
An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation
title An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation
title_full An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation
title_fullStr An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation
title_full_unstemmed An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation
title_short An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation
title_sort intelligent gesture classification model for domestic wheelchair navigation with gesture variance compensation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204291/
https://www.ncbi.nlm.nih.gov/pubmed/32399060
http://dx.doi.org/10.1155/2020/9160528
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