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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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2020/9160528 |
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| author | H. M. Ravindu T. Bandara K. S. Priyanayana A. G. Buddhika P. Jayasekara D. P. Chandima R. A. R. C. Gopura |
| author_facet | H. M. Ravindu T. Bandara K. S. Priyanayana A. G. Buddhika P. Jayasekara D. P. Chandima R. A. R. C. Gopura |
| author_sort | H. M. Ravindu T. Bandara |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-c1703f4f9ab842d6b442c83fb6f0da9a |
| institution | Kabale University |
| issn | 1176-2322 1754-2103 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-c1703f4f9ab842d6b442c83fb6f0da9a2025-02-03T01:04:19ZengWileyApplied Bionics and Biomechanics1176-23221754-21032020-01-01202010.1155/2020/91605289160528An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance CompensationH. M. Ravindu T. Bandara0K. S. Priyanayana1A. G. Buddhika P. Jayasekara2D. P. Chandima3R. A. R. C. Gopura4Intelligent Service Robotic Group, Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri LankaIntelligent Service Robotic Group, Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri LankaIntelligent Service Robotic Group, Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri LankaIntelligent Service Robotic Group, Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri LankaBionics Laboratory, Department of Mechanical Engineering, University of Moratuwa, Moratuwa 10400, Sri LankaElderly 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.http://dx.doi.org/10.1155/2020/9160528 |
| spellingShingle | H. M. Ravindu T. Bandara K. S. Priyanayana A. G. Buddhika P. Jayasekara D. P. Chandima R. A. R. C. Gopura An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation Applied Bionics and Biomechanics |
| 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 |
| url | http://dx.doi.org/10.1155/2020/9160528 |
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