Descending stairs and floors classification as control reference in autonomous smart wheelchair
Disability is a disruption or limitation of a person’s body functions in carrying out daily activities. A person with physical disabilities needs an assistive device such as a wheelchair. The latest wheelchair development is the smart wheelchair. Smart wheelchairs require a control system to detect...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157821002093 |
| _version_ | 1828121562108133376 |
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| author | Fitri Utaminingrum A.W. Satria Bahari Johan I. Komang Somawirata Risnandar Anindita Septiarini |
| author_facet | Fitri Utaminingrum A.W. Satria Bahari Johan I. Komang Somawirata Risnandar Anindita Septiarini |
| author_sort | Fitri Utaminingrum |
| collection | DOAJ |
| description | Disability is a disruption or limitation of a person’s body functions in carrying out daily activities. A person with physical disabilities needs an assistive device such as a wheelchair. The latest wheelchair development is the smart wheelchair. Smart wheelchairs require a control system to detect obstacles quickly. This aims to provide safety, especially for users. One of the obstacles that are quite dangerous is descending stairs. Therefore the researchers propose a descending stairs detection system for smart wheelchairs. The proposed method in this study is the gray level co-occurrence matrix (GLCM) as the feature extraction algorithm, learning vector quantization (LVQ) as the classification algorithm, and sequential forward selection (SFS) for feature selection. Based on the simulation result, the SFS feature selection gets two selected GLCM features. The best accuracy is 94.5% with the selected features, namely contrast and dissimilarity. This result has an increase in accuracy when compared to using the GLCM 6 feature method with the LVQ classification that does not use feature selection, where the method gets 92.5% accuracy in off-time testing. Accuracy decreased to 78.21% when detecting floors and 89.06% when detecting descending stairs in real-time system testing. |
| first_indexed | 2024-04-11T14:21:23Z |
| format | Article |
| id | doaj.art-8c57277cc2a34e27a575bd4bcaa1876b |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-11T14:21:23Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-8c57277cc2a34e27a575bd4bcaa1876b2022-12-22T04:19:03ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-09-0134860406047Descending stairs and floors classification as control reference in autonomous smart wheelchairFitri Utaminingrum0A.W. Satria Bahari Johan1I. Komang Somawirata2 Risnandar3Anindita Septiarini4Computer Vision Research Group, Faculty of Computer Science, Brawijaya University, Malang 65145, Indonesia; Corresponding author.Computer Vision Research Group, Faculty of Computer Science, Brawijaya University, Malang 65145, IndonesiaElectrical Engineering, Faculty of Industrial Technology, National Institute of Technology, Malang 65145, IndonesiaThe Research Center for Informatics, Indonesian Institute of Sciences (LIPI), IndonesiaDepartment of Informatics, Faculty of Engineering, Mulawarman University, Samarinda, 75119, IndonesiaDisability is a disruption or limitation of a person’s body functions in carrying out daily activities. A person with physical disabilities needs an assistive device such as a wheelchair. The latest wheelchair development is the smart wheelchair. Smart wheelchairs require a control system to detect obstacles quickly. This aims to provide safety, especially for users. One of the obstacles that are quite dangerous is descending stairs. Therefore the researchers propose a descending stairs detection system for smart wheelchairs. The proposed method in this study is the gray level co-occurrence matrix (GLCM) as the feature extraction algorithm, learning vector quantization (LVQ) as the classification algorithm, and sequential forward selection (SFS) for feature selection. Based on the simulation result, the SFS feature selection gets two selected GLCM features. The best accuracy is 94.5% with the selected features, namely contrast and dissimilarity. This result has an increase in accuracy when compared to using the GLCM 6 feature method with the LVQ classification that does not use feature selection, where the method gets 92.5% accuracy in off-time testing. Accuracy decreased to 78.21% when detecting floors and 89.06% when detecting descending stairs in real-time system testing.http://www.sciencedirect.com/science/article/pii/S1319157821002093DisabilitySmart wheelchairGray level co-occurrence matrixSequential feature selectionLearning vector quantization |
| spellingShingle | Fitri Utaminingrum A.W. Satria Bahari Johan I. Komang Somawirata Risnandar Anindita Septiarini Descending stairs and floors classification as control reference in autonomous smart wheelchair Journal of King Saud University: Computer and Information Sciences Disability Smart wheelchair Gray level co-occurrence matrix Sequential feature selection Learning vector quantization |
| title | Descending stairs and floors classification as control reference in autonomous smart wheelchair |
| title_full | Descending stairs and floors classification as control reference in autonomous smart wheelchair |
| title_fullStr | Descending stairs and floors classification as control reference in autonomous smart wheelchair |
| title_full_unstemmed | Descending stairs and floors classification as control reference in autonomous smart wheelchair |
| title_short | Descending stairs and floors classification as control reference in autonomous smart wheelchair |
| title_sort | descending stairs and floors classification as control reference in autonomous smart wheelchair |
| topic | Disability Smart wheelchair Gray level co-occurrence matrix Sequential feature selection Learning vector quantization |
| url | http://www.sciencedirect.com/science/article/pii/S1319157821002093 |
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